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<div class="quarto-title-block"><div><h1 class="title">Systematic literature review</h1><button type="button" class="btn code-tools-button dropdown-toggle" id="quarto-code-tools-menu" data-bs-toggle="dropdown" aria-expanded="false"><i class="bi"></i> Code</button><ul class="dropdown-menu dropdown-menu-end" aria-labelelledby="quarto-code-tools-menu"><li><a id="quarto-show-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Show All Code</a></li><li><a id="quarto-hide-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Hide All Code</a></li><li><hr class="dropdown-divider"></li><li><a id="quarto-view-source" class="dropdown-item" href="javascript:void(0)" role="button">View Source</a></li></ul></div></div>
<p class="subtitle lead">NLP techniques used in marketing</p>
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<div class="quarto-title-meta-heading">Authors</div>
<div class="quarto-title-meta-heading">Affiliation</div>
<div class="quarto-title-meta-contents">
<p class="author">Olivier Caron </p>
</div>
<div class="quarto-title-meta-contents">
<p class="affiliation">
Paris Dauphine - PSL
</p>
</div>
<div class="quarto-title-meta-contents">
<p class="author">Christophe Benavent </p>
</div>
<div class="quarto-title-meta-contents">
<p class="affiliation">
Paris Dauphine - PSL
</p>
</div>
</div>
<div class="quarto-title-meta">
<div>
<div class="quarto-title-meta-heading">Published</div>
<div class="quarto-title-meta-contents">
<p class="date">October 4, 2023</p>
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<nav id="TOC" role="doc-toc" class="toc-active">
<h2 id="toc-title">Table of contents</h2>
<ul>
<li><a href="#libraries-r-and-loading-data" id="toc-libraries-r-and-loading-data" class="nav-link active" data-scroll-target="#libraries-r-and-loading-data"><span class="header-section-number">1</span> Libraries R and loading data</a></li>
<li><a href="#loading-of-all-annotations-stanza-udpipe-trankit" id="toc-loading-of-all-annotations-stanza-udpipe-trankit" class="nav-link" data-scroll-target="#loading-of-all-annotations-stanza-udpipe-trankit"><span class="header-section-number">2</span> Loading of all annotations (Stanza, UDPipe, Trankit)</a>
<ul class="collapse">
<li><a href="#table-of-most-frequent-propn-words-in-the-stanza-annotations" id="toc-table-of-most-frequent-propn-words-in-the-stanza-annotations" class="nav-link" data-scroll-target="#table-of-most-frequent-propn-words-in-the-stanza-annotations"><span class="header-section-number">2.1</span> Table of most frequent PROPN words in the Stanza annotations</a></li>
<li><a href="#table-of-most-frequent-propn-words-in-the-udpipe-annotations" id="toc-table-of-most-frequent-propn-words-in-the-udpipe-annotations" class="nav-link" data-scroll-target="#table-of-most-frequent-propn-words-in-the-udpipe-annotations"><span class="header-section-number">2.2</span> Table of most frequent PROPN words in the UDPipe annotations</a></li>
<li><a href="#table-of-most-frequent-propn-words-in-the-trankit-annotations" id="toc-table-of-most-frequent-propn-words-in-the-trankit-annotations" class="nav-link" data-scroll-target="#table-of-most-frequent-propn-words-in-the-trankit-annotations"><span class="header-section-number">2.3</span> Table of most frequent PROPN words in the Trankit annotations</a></li>
</ul></li>
<li><a href="#nlp-techniques-in-marketing" id="toc-nlp-techniques-in-marketing" class="nav-link" data-scroll-target="#nlp-techniques-in-marketing"><span class="header-section-number">3</span> NLP techniques in Marketing</a>
<ul class="collapse">
<li><a href="#generic-techniques" id="toc-generic-techniques" class="nav-link" data-scroll-target="#generic-techniques"><span class="header-section-number">3.1</span> Generic techniques</a></li>
</ul></li>
</ul>
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<div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
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<main class="content quarto-banner-title-block" id="quarto-document-content">
<section id="libraries-r-and-loading-data" class="level2" data-number="1">
<h2 data-number="1" class="anchored" data-anchor-id="libraries-r-and-loading-data"><span class="header-section-number">1</span> Libraries R and loading data</h2>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(reactable)</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(plotly)</span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(wesanderson)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co">#load data</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>data_embeddings <span class="ot"><-</span> <span class="fu">read.csv</span>(<span class="st">"data_for_embeddings.csv"</span>)</span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>annotations_stanza <span class="ot"><-</span> <span class="fu">read.csv</span>(<span class="st">"annotated_stanza.csv"</span>)</span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>annotations_udpipe <span class="ot"><-</span> <span class="fu">read.csv</span>(<span class="st">"annotated_udpipe.csv"</span>)</span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>annotations_trankit <span class="ot"><-</span> <span class="fu">read.csv</span>(<span class="st">"annotated_trankit.csv"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="loading-of-all-annotations-stanza-udpipe-trankit" class="level2" data-number="2">
<h2 data-number="2" class="anchored" data-anchor-id="loading-of-all-annotations-stanza-udpipe-trankit"><span class="header-section-number">2</span> Loading of all annotations (Stanza, UDPipe, Trankit)</h2>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co">#annotation filter on PROPN</span></span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>annotations_stanza_propn <span class="ot"><-</span> annotations_stanza <span class="sc">%>%</span> <span class="fu">filter</span>(upos <span class="sc">==</span> <span class="st">"PROPN"</span>)</span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>annotations_udpipe_propn <span class="ot"><-</span> annotations_udpipe <span class="sc">%>%</span> <span class="fu">filter</span>(upos <span class="sc">==</span> <span class="st">"PROPN"</span>)</span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>annotations_trankit_propn <span class="ot"><-</span> annotations_trankit <span class="sc">%>%</span> <span class="fu">filter</span>(upos <span class="sc">==</span> <span class="st">"PROPN"</span>)</span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a><span class="co">#list of most frequent words in annotatinos</span></span>
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a>toppropn_stanza <span class="ot"><-</span> annotations_stanza_propn <span class="sc">%>%</span> <span class="fu">count</span>(lemma, <span class="at">sort =</span> <span class="cn">TRUE</span>) <span class="sc">%>%</span> <span class="fu">drop_na</span>() <span class="sc">%>%</span> <span class="fu">filter</span>(n <span class="sc">></span> <span class="dv">1</span>)</span>
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a>toppropn_udpipe <span class="ot"><-</span> annotations_udpipe_propn <span class="sc">%>%</span> <span class="fu">count</span>(lemma, <span class="at">sort =</span> <span class="cn">TRUE</span>) <span class="sc">%>%</span> <span class="fu">drop_na</span>() <span class="sc">%>%</span> <span class="fu">filter</span>(n <span class="sc">></span> <span class="dv">1</span>)</span>
<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a>toppropn_trankit <span class="ot"><-</span> annotations_trankit_propn <span class="sc">%>%</span> <span class="fu">count</span>(lemma, <span class="at">sort =</span> <span class="cn">TRUE</span>) <span class="sc">%>%</span> <span class="fu">drop_na</span>() <span class="sc">%>%</span> <span class="fu">filter</span>(n <span class="sc">></span> <span class="dv">1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
<section id="table-of-most-frequent-propn-words-in-the-stanza-annotations" class="level3" data-number="2.1">
<h3 data-number="2.1" class="anchored" data-anchor-id="table-of-most-frequent-propn-words-in-the-stanza-annotations"><span class="header-section-number">2.1</span> Table of most frequent PROPN words in the Stanza annotations</h3>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co">#reactable of most frequent words in annotations_propn</span></span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>toppropn_stanza <span class="sc">%>%</span> </span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">reactable</span>(</span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a> <span class="at">searchable =</span> <span class="cn">TRUE</span>,</span>
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a> <span class="at">defaultColDef =</span> <span class="fu">colDef</span>(</span>
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a> <span class="at">minWidth =</span> <span class="dv">100</span>,</span>
<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a> <span class="at">sortable =</span> <span class="cn">TRUE</span></span>
<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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</div>
</div>
</section>
<section id="table-of-most-frequent-propn-words-in-the-udpipe-annotations" class="level3" data-number="2.2">
<h3 data-number="2.2" class="anchored" data-anchor-id="table-of-most-frequent-propn-words-in-the-udpipe-annotations"><span class="header-section-number">2.2</span> Table of most frequent PROPN words in the UDPipe annotations</h3>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co">#reactable of most frequent words in annotations_propn</span></span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>toppropn_udpipe <span class="sc">%>%</span> </span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">reactable</span>(</span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a> <span class="at">searchable =</span> <span class="cn">TRUE</span>,</span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a> <span class="at">defaultColDef =</span> <span class="fu">colDef</span>(</span>
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a> <span class="at">minWidth =</span> <span class="dv">100</span>,</span>
<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a> <span class="at">sortable =</span> <span class="cn">TRUE</span></span>
<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
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<div class="reactable html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-82c7a44cc1f67379604a" style="width:auto;height:auto;"></div>
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</div>
</div>
</section>
<section id="table-of-most-frequent-propn-words-in-the-trankit-annotations" class="level3" data-number="2.3">
<h3 data-number="2.3" class="anchored" data-anchor-id="table-of-most-frequent-propn-words-in-the-trankit-annotations"><span class="header-section-number">2.3</span> Table of most frequent PROPN words in the Trankit annotations</h3>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co">#reactable of most frequent words in annotations_propn</span></span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>toppropn_trankit <span class="sc">%>%</span> </span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">reactable</span>(</span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a> <span class="at">searchable =</span> <span class="cn">TRUE</span>,</span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a> <span class="at">defaultColDef =</span> <span class="fu">colDef</span>(</span>
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a> <span class="at">minWidth =</span> <span class="dv">100</span>,</span>
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a> <span class="at">sortable =</span> <span class="cn">TRUE</span></span>
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a> )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
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<div class="reactable html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-024be12c85a0af5dfc18" style="width:auto;height:auto;"></div>
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</div>
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</section>
<section id="nlp-techniques-in-marketing" class="level2" data-number="3">
<h2 data-number="3" class="anchored" data-anchor-id="nlp-techniques-in-marketing"><span class="header-section-number">3</span> NLP techniques in Marketing</h2>
<section id="generic-techniques" class="level3" data-number="3.1">
<h3 data-number="3.1" class="anchored" data-anchor-id="generic-techniques"><span class="header-section-number">3.1</span> Generic techniques</h3>
<p>We want to provide a list of recurrent techniques in marketing. The tables above clearly show that some of them are often used. Let’s focus first on them in no particular order.</p>
<ul>
<li><p>LIWC</p></li>
<li><p>Leximancer</p></li>
<li><p>BERT</p></li>
<li><p>ChatGPT</p></li>
<li><p>PassivePy</p></li>
</ul>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co">#i want to get just one number if multiple words of the vector are found in the string</span></span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a><span class="co">#detect and count the words above in the "combined_text" column of data_embeddings</span></span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>data_embeddings<span class="sc">$</span>combined_text <span class="ot"><-</span> <span class="fu">tolower</span>(data_embeddings<span class="sc">$</span>combined_text)</span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a>data_embeddings<span class="sc">$</span>liwc <span class="ot"><-</span> <span class="fu">str_count</span>(data_embeddings<span class="sc">$</span>combined_text, <span class="st">"liwc"</span>)</span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a>data_embeddings<span class="sc">$</span>leximancer <span class="ot"><-</span> <span class="fu">str_count</span>(data_embeddings<span class="sc">$</span>combined_text, <span class="st">"leximancer"</span>)</span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a>data_embeddings<span class="sc">$</span>bert <span class="ot"><-</span> <span class="fu">str_count</span>(data_embeddings<span class="sc">$</span>combined_text, <span class="st">"bert"</span>)</span>
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a>data_embeddings<span class="sc">$</span>chatgpt <span class="ot"><-</span> <span class="fu">str_count</span>(data_embeddings<span class="sc">$</span>combined_text, <span class="st">"chatgpt"</span>)</span>
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a>data_embeddings<span class="sc">$</span>passivepy <span class="ot"><-</span> <span class="fu">str_count</span>(data_embeddings<span class="sc">$</span>combined_text, <span class="st">"passivepy"</span>)</span>
<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a><span class="co"># Group by year and calculate the cumulative sum for each technique</span></span>
<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a>sum_data <span class="ot"><-</span> data_embeddings <span class="sc">%>%</span></span>
<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(year) <span class="sc">%>%</span></span>
<span id="cb7-15"><a href="#cb7-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarize</span>(</span>
<span id="cb7-16"><a href="#cb7-16" aria-hidden="true" tabindex="-1"></a> <span class="at">sum_liwc =</span> <span class="fu">sum</span>(liwc),</span>
<span id="cb7-17"><a href="#cb7-17" aria-hidden="true" tabindex="-1"></a> <span class="at">sum_leximancer =</span> <span class="fu">sum</span>(leximancer),</span>
<span id="cb7-18"><a href="#cb7-18" aria-hidden="true" tabindex="-1"></a> <span class="at">sum_bert =</span> <span class="fu">sum</span>(bert),</span>
<span id="cb7-19"><a href="#cb7-19" aria-hidden="true" tabindex="-1"></a> <span class="at">sum_chatgpt =</span> <span class="fu">sum</span>(chatgpt),</span>
<span id="cb7-20"><a href="#cb7-20" aria-hidden="true" tabindex="-1"></a> <span class="at">sum_passivepy =</span> <span class="fu">sum</span>(passivepy)</span>
<span id="cb7-21"><a href="#cb7-21" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span>
<span id="cb7-22"><a href="#cb7-22" aria-hidden="true" tabindex="-1"></a> <span class="fu">ungroup</span>()</span>
<span id="cb7-23"><a href="#cb7-23" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-24"><a href="#cb7-24" aria-hidden="true" tabindex="-1"></a>cumulative_data <span class="ot"><-</span> sum_data <span class="sc">%>%</span></span>
<span id="cb7-25"><a href="#cb7-25" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span>
<span id="cb7-26"><a href="#cb7-26" aria-hidden="true" tabindex="-1"></a> <span class="at">cum_liwc =</span> <span class="fu">cumsum</span>(sum_liwc),</span>
<span id="cb7-27"><a href="#cb7-27" aria-hidden="true" tabindex="-1"></a> <span class="at">cum_leximancer =</span> <span class="fu">cumsum</span>(sum_leximancer),</span>
<span id="cb7-28"><a href="#cb7-28" aria-hidden="true" tabindex="-1"></a> <span class="at">cum_bert =</span> <span class="fu">cumsum</span>(sum_bert),</span>
<span id="cb7-29"><a href="#cb7-29" aria-hidden="true" tabindex="-1"></a> <span class="at">cum_chatgpt =</span> <span class="fu">cumsum</span>(sum_chatgpt),</span>
<span id="cb7-30"><a href="#cb7-30" aria-hidden="true" tabindex="-1"></a> <span class="at">cum_passivepy =</span> <span class="fu">cumsum</span>(sum_passivepy)</span>
<span id="cb7-31"><a href="#cb7-31" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span>
<span id="cb7-32"><a href="#cb7-32" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(year <span class="sc">></span> <span class="dv">2012</span>)</span>
<span id="cb7-33"><a href="#cb7-33" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-34"><a href="#cb7-34" aria-hidden="true" tabindex="-1"></a>patterns <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"linguistic inquiry and word count"</span>, <span class="st">"linguistic inquiry"</span>, <span class="st">"linguistic inquiry word count"</span>, <span class="st">"liwc"</span>)</span>
<span id="cb7-35"><a href="#cb7-35" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-36"><a href="#cb7-36" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a new column "liwc_alt" with initial empty strings</span></span>
<span id="cb7-37"><a href="#cb7-37" aria-hidden="true" tabindex="-1"></a>data_embeddings<span class="sc">$</span>liwc_alt <span class="ot"><-</span> <span class="st">""</span></span>
<span id="cb7-38"><a href="#cb7-38" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-39"><a href="#cb7-39" aria-hidden="true" tabindex="-1"></a><span class="co"># Find the column index for "liwc_alt"</span></span>
<span id="cb7-40"><a href="#cb7-40" aria-hidden="true" tabindex="-1"></a>ncol <span class="ot"><-</span> <span class="fu">which</span>(<span class="fu">colnames</span>(data_embeddings) <span class="sc">==</span> <span class="st">"liwc_alt"</span>)</span>
<span id="cb7-41"><a href="#cb7-41" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-42"><a href="#cb7-42" aria-hidden="true" tabindex="-1"></a><span class="co"># Loop through each row and pattern to count occurrences and populate "liwc_alt"</span></span>
<span id="cb7-43"><a href="#cb7-43" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="fu">nrow</span>(data_embeddings)) {</span>
<span id="cb7-44"><a href="#cb7-44" aria-hidden="true" tabindex="-1"></a> counts <span class="ot"><-</span> <span class="fu">sapply</span>(patterns, <span class="cf">function</span>(pattern) {</span>
<span id="cb7-45"><a href="#cb7-45" aria-hidden="true" tabindex="-1"></a> <span class="fu">str_count</span>(data_embeddings<span class="sc">$</span>combined_text[i], pattern)</span>
<span id="cb7-46"><a href="#cb7-46" aria-hidden="true" tabindex="-1"></a> })</span>
<span id="cb7-47"><a href="#cb7-47" aria-hidden="true" tabindex="-1"></a> data_embeddings<span class="sc">$</span>liwc_alt[i] <span class="ot"><-</span> <span class="fu">sum</span>(counts)</span>
<span id="cb7-48"><a href="#cb7-48" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb7-49"><a href="#cb7-49" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-50"><a href="#cb7-50" aria-hidden="true" tabindex="-1"></a>liwc_df <span class="ot"><-</span> data_embeddings <span class="sc">%>%</span> <span class="fu">select</span>(combined_text, liwc, liwc_alt)</span>
<span id="cb7-51"><a href="#cb7-51" aria-hidden="true" tabindex="-1"></a><span class="fu">reactable</span>(liwc_df, <span class="at">searchable =</span> <span class="cn">TRUE</span>, </span>
<span id="cb7-52"><a href="#cb7-52" aria-hidden="true" tabindex="-1"></a> <span class="at">defaultPageSize =</span> <span class="dv">3</span>, <span class="co"># Set the maximum number of rows to display</span></span>
<span id="cb7-53"><a href="#cb7-53" aria-hidden="true" tabindex="-1"></a> <span class="at">columns =</span> <span class="fu">list</span>(</span>
<span id="cb7-54"><a href="#cb7-54" aria-hidden="true" tabindex="-1"></a> <span class="at">combined_text =</span> <span class="fu">colDef</span>(<span class="at">width =</span> <span class="dv">850</span>),</span>
<span id="cb7-55"><a href="#cb7-55" aria-hidden="true" tabindex="-1"></a> <span class="at">liwc =</span> <span class="fu">colDef</span>(<span class="at">width =</span> <span class="dv">50</span>),</span>
<span id="cb7-56"><a href="#cb7-56" aria-hidden="true" tabindex="-1"></a> <span class="at">liwc_alt =</span> <span class="fu">colDef</span>(<span class="at">width =</span> <span class="dv">50</span>)</span>
<span id="cb7-57"><a href="#cb7-57" aria-hidden="true" tabindex="-1"></a> ))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div class="reactable html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-004309dd2a171017940e" style="width:auto;height:auto;"></div>
<script type="application/json" data-for="htmlwidget-004309dd2a171017940e">{"x":{"tag":{"name":"Reactable","attribs":{"data":{"combined_text":["what reviews foretell about opening weekend box office revenue: the harbinger of failure effect in the movie industry. we empirically investigate the harbinger of failure phenomenon in the motion picture industry by analyzing the pre-release reviews written on movies by film critics. we find that harbingers of failure do exist. their positive (negative) pre-release movie reviews provide a strong predictive signal that the movie will turn out to be a flop (success). this signal persists even for the top critic category, which usually consists of professional critics, indicating that having expertise in a professional domain does not necessarily lead to correct predictions. our findings challenge the current belief that positive reviews always help enhance box office revenue and shed new light on the influencer-predictor hypothesis. we further analyze the writing style of harbingers and provide new insights into their personality traits and cognitive biases.. harbingers of failure movies preference heterogeneity reviews text analytics","tighter nets for smaller fishes? mapping the development of statistical practices in consumer research between 2008 and 2020. during the last decade, confidence in many social sciences, including consumer research, has been undermined by doubts about the replicability of empirical research findings. these doubts have led to increased calls to improve research practices and adopt new measures to increase the replicability of published work from various stakeholders such as funding agencies, journals, and scholars themselves. despite these demands, it is unclear to which the research published in the leading consumer research journals has adhered to these calls for change. this article provides the first systematic empirical analysis of this question by surveying three crucial statistics of published consumer research over time: sample sizes, effect sizes, and the distribution of published p values. the authors compile a hand-coded sample of n = 258 articles published between 2008 and 2020 in the journal of consumer psychology, the journal of consumer research, and the journal of marketing research. an automated text analysis across all publications in these three journals corroborates the representativeness of the hand-coded sample. results reveal a substantial increase in sample sizes above and beyond the use of online samples along with a decrease in reported effect sizes. effect and samples sizes are highly correlated which at least partially explains the reduction in reported effect sizes.. experimental research methods false-positive results review","been there, done that: how episodic and semantic memory affects the language of authentic and fictitious reviews. this article suggests a theory-driven approach to address the managerial problem of distinguishing between real and fake reviews. building on memory research and linguistics, we predict that when recollecting an authentic experience in a product review, people rely to a greater extent on episodic memory. by contrast, when writing a fictitious review, people do not have episodic memory available to them. therefore, they must rely to a greater extent on semantic memory. we suggest that reliance on these different memory types is reflected in the language used in authentic and fictitious reviews. we develop predictions about five linguistic features characterizing authentic versus fictitious reviews. we test our predictions via a multi-method approach, combining computational linguistics, experimental design, and machine learning. we employ a large-scale experiment to derive a dataset of reviews, as well as two datasets containing reviews from online platforms. we also test whether an algorithm relying on our theory-driven linguistic features is context independent, relative to other benchmark algorithms, and shows better cross-domain performance when tested across datasets. by developing a theory that extends memory and psycholinguistics research to the realm of word of mouth, this work contributes to our understanding of how authentic and fictitious reviews are created.. fake reviews lie detection linguistic features machine learning memory text analysis","more voices persuade: the attentional benefits of voice numerosity. the authors posit that in an initial exposure to a broadcast video, hearing different voices narrate (in succession) a persuasive message encourages consumers’ attention and processing of the message, thereby facilitating persuasion; this is referred to as the voice numerosity effect. across four studies (plus validation and replication studies)—including two large-scale, real-world data sets (with more than 11,000 crowdfunding videos and over 3.6 million customer transactions, and more than 1,600 video ads) and two controlled experiments (with over 1,800 participants)—the results provide support for the hypothesized effect. the effect (1) has consequential, economic implications in a real-world marketplace, (2) is more pronounced when the message is easier to comprehend, (3) is more pronounced when consumers have the capacity to process the ad message, and (4) is mediated by the favorability of consumers’ cognitive responses. the authors demonstrate the use of machine learning, text mining, and natural language processing to process and analyze unstructured (multimedia) data. theoretical and marketing implications are discussed.. advertising crowdfunding marketing communications persuasion sensory marketing videos voice voice numerosity","online complaint handling: a text analytics-based classification framework. purpose: this study aims to both identify content-based and interaction-based online consumer complaint types and predict complaint types according to the complaint magnitude rooted in complainants' personality traits, emotion, twitter usage activity, as well as complaint's sentiment polarity, and interaction rate. design/methodology/approach: in total, 297,000 complaint tweets were collected from twitter, featuring over 220,000 consumer profiles and over 24 million user tweets. the obtained data were analyzed via two-step machine learning approach. findings: this study proposes a set of content and profile features that can be employed for determining complaint types and reveals the relationship between content features, profile features and online complaint type. originality/value: this study proposes a novel model for identifying types of online complaints, offering a set of content and profile features that can be used for predicting complaint type, and therefore introduces a flexible approach for enhancing online complaint management.. complaint handling machine learning social crm text analytics twitter","text mining approach to explore determinants of grocery mobile app satisfaction using online customer reviews. in recent years, there has been proliferation of grocery mobile apps as grocery shopping on mobile has found increasing acceptance among customers accelerated by multiple factors. maintaining high level of customer satisfaction is important for grocery mobile apps in the highly competitive app market. online reviews have been a rich source of information to analyze customer satisfaction with a product or service. this paper explores the determinants of customer satisfaction for grocery mobile apps using online reviews. latent dirichlet analysis (lda), which is a text mining technique, is used to analyze online customer reviews of 27,337 customers to identify determinants of customer satisfaction. the determinants identified were further analyzed using a series of analysis to understand the importance of each determinant. dominance analysis examined the relative importance of the determinants of customer satisfaction based on the overall rating. correspondence analysis identified determinants which cause satisfaction separately from the determinants which cause dissatisfaction. the results from this study will provide insights to business managers of grocery mobile apps for decision-making on customer satisfaction management.. customer satisfaction grocery mobile apps online customer reviews text mining topic modelling","analysis of customers' satisfaction with baby products: the moderating role of brand image. the previous experience of customers is assessed by textual reviews and ratings to reflect their levels of satisfaction with the service or product. online reviews and ratings have played a vital role to assess the performance of the services or the quality of goods. this research explores customers' levels of satisfaction with baby products on amazon.com through text mining and survey-based methodologies. the moderating role of a brand image is also investigated on the relationship between customers' satisfaction and recommendations to others. two sources of data were utilized, online reviews from the amazon portal and responses of the participants on a large-scale survey. the research model is developed based on the factors that were extracted from the text mining approach. following that, a questionnaire was distributed and the collected data were inspected using partial least squares structural equations modeling (pls-sem). the findings revealed that several dimensions have an influential effect on customers' experiences with baby products, among which are comfortableness, material quality, easy use, easy return, design, price, packaging, and size information. in addition, the moderating influence of the brand image on the hypothesis between customers' satisfaction and recommendations to others was significant. by deploying the two-phase methodology, baby product vendors can understand the influential variables in customers' experiences and their impacts on customers’ satisfaction more accurately.. analytic approach baby product brand image machine learning satisfaction","how online reviews with different influencing factors affect the diffusion of new products. online reviews are becoming increasingly important for the diffusion of new products. based on the content of online reviews on jd.com, the study utilizes the latent allocation model to identify the critical factors of interest to consumers and categorized them into two categories, namely, product quality and supporting service factors. thereafter, we use the system generalized method of moments to analyse the dynamic model of the panel data and further study the relationship between online reviews and new product diffusion under the two types of factors. we further investigate how the interaction between the critical metrics of online reviews affects the diffusion of new products. the results indicate that as various factors are considered, online reviews have a significantly different impact on the diffusion of new products. moreover, compared with supporting service factors, online reviews of product quality factors have a more significant impact. in addition, the interactions between online review metrics demonstrate complex characteristics, especially the volume and positive emotional tendency, which does not promote new product diffusion, as expected.. interaction effect latent dirichlet allocation model new product diffusion online reviews text mining","it is not just a name: effects of proper name for high-quality versus low-quality brands. the usage of proper names to advertise a product is ubiquitous in the marketplace. in many cases, there is very little information about these names. for example, treatment lotion by “lady aiko.” this research examines whether such a strategy effectively increases evaluations for a product. if so, is this strategy more effective when used by a high-quality brand or a low-quality brand? across one field study, one text analytics study, and two experimental studies, we find that the proper name strategy can lead to higher product evaluations and that such names are more effective when advertised for a low-quality brand. we first propose, using contagion effects, that products advertised with a proper name are more likely to contain the essence of human creation, resulting in greater product effectiveness than identical products without such proper names. furthermore, we employ the expectation-disconfirmation account to propose that when the brand has a higher (vs. lower) quality (expectation stage), the product with a proper name strategy (postexpectation stage) leads to small (vs. large) positive disconfirmation, which thereby results in assimilation (vs. contrast) and forming product evaluations similar (vs. higher) to the original assessments of the brand. we offer implications for new product marketers on how to be most effective in influencing product evaluations, as well as policymakers looking to improve consumer welfare by encouraging manufacturers of generic products to consider using a proper name strategy.. branding contagion expectation-disconfirmation product judgments proper names quality","voice bots on the frontline: voice-based interfaces enhance flow-like consumer experiences & boost service outcomes. voice-based interfaces provide new opportunities for firms to interact with consumers along the customer journey. the current work demonstrates across four studies that voice-based (as opposed to text-based) interfaces promote more flow-like user experiences, resulting in more positively-valenced service experiences, and ultimately more favorable behavioral firm outcomes (i.e., contract renewal, conversion rates, and consumer sentiment). moreover, we also provide evidence for two important boundary conditions that reduce such flow-like user experiences in voice-based interfaces (i.e., semantic disfluency and the amount of conversational turns). the findings of this research highlight how fundamental theories of human communication can be harnessed to create more experiential service experiences with positive downstream consequences for consumers and firms. these findings have important practical implications for firms that aim at leveraging the potential of voice-based interfaces to improve consumers’ service experiences and the theory-driven “conversational design” of voice-based interfaces.. artificial intelligence flow media richness natural language processing service research speech interaction voice-based interface","exploring the perceptions and motivations of gen z and millennials toward sustainable clothing. this study explores consumers' perceptions of sustainable clothing and motivations for purchasing it. an analysis of 682 generation z and millennial consumers shows both similarities and differences in their perceptions as well as their perceived importance, value, and need for sustainable clothing consumption. descriptive statistics, t-tests, and text mining were used for analysis. respondents were aware that sustainable clothing plays an important role in the overall environmental movement and has ecological benefits. however, researchers, educators, and businesses need stronger communication on other aspects of sustainable clothing. understanding consumers' priorities and knowledge gaps can help the industry encourage sustainable clothing consumption.. generation z millennials motivation perception sustainable clothing sustainable clothing consumption","profiling diverse reviewer segments using online reviews of service industries. consumers often rely on online review sites (e.g., yelp, tripadvisor) to read reviews on service products they may consider choosing. despite the usefulness of such reviews, consumers may have difficulty finding reviews suitable for their own preferences because those reviews possess significant preference heterogeneity among reviewers. differences in the four dimensions of individual reviewers (experience, productivity, details, and criticalness) lead them to have differential impacts on various consumers. thus, this research is aimed at understanding the profiles of multiple influential reviewer segments in the service industries under two fundamental evaluation principles (quality and likeness). based on the results, we theorize that reviewer influence arises from writing either reviews of high quality (quality) or reviews reflecting a significant segment of consumers’ common preferences (likeness). using two service product categories (restaurants and hotels), we empirically identify and profile four specific influential review segments: (1) top-tier quality reviewers, (2) second-tier quality reviewers, (3) likeness (common) reviewers, and (4) less critical likeness reviewers. by using our approach, product review website managers can present product reviews in such a way that users can easily choose reviews and reviewers that match their preferences out of a vast collection of reviews containing all kinds of preferences.. finite mixture modeling online reviewer reviewer segmentation text mining","satisfaction dimensions influencing consumers’ behavioral intentions through structural topic modeling analysis of restaurant reviews. purpose: this study aims to identify the impact of affective components on behavioral intentions applying electronic word of mouth (ewom) and is based on the “cognitive–affective–conative” model. ewom allows researchers to get new insights about consumers’ behavior and explores new patterns of consumers’ decision-making processes. design/methodology/approach: this study is based on the theory of planned behavior (ajzen, 1991), doubled with “cognitive–affective–conative” model (oliver, 2014). this study applies structural topic modeling to examine the impact of satisfaction dimensions from all the tripadvisor reviews on consumer behavioral intentions. the research sample covers all restaurants located in st. petersburg (n = 10,424) and all consumers’ reviews (n = 286,642). findings: in this study, the dimensions of the affective component were identified. the results demonstrate that dimensions of the affective component (food quality, service quality, atmosphere and cost fairness) affect behavioral intention (willingness to share positive emotions). in total, 20 topics, forming these dimensions, have been indicated. consumers tend to pay more attention toward food quality and restaurant staffs’ work when they are willing to share positive emotions and tend to point out auxiliary service when they have less willingness to share positive emotions. random restaurant visits tend to increase the willingness to share positive emotions. originality/value: research originality lies in a new methodological approach which is based on text mining techniques. to the best of the authors’ knowledge, this study is the first attempt to examine consumer behavior through the lens of the “cognitive–affective–conative” model based on ewom and covers all businesses in the specific economic sphere. this has allowed the researchers to reveal new dimensions of consumer behavior and brought more insights into the consumers’ decision-making process.. consumer behavior electronic word of mouth restaurants satisfaction dimensions structural topic modeling tripadvisor","it is about inclusion! mining online reviews to understand the needs of adaptive clothing customers. the adaptive clothing market, which focuses on the inclusive design of clothing and footwear for people with varying degrees of disability, has grown substantially in recent years. however, few scholars have sought to understand the perspectives of online adaptive clothing consumers. this study employed topic modelling, sentiment analysis and collocation analysis to discover common themes and insights emerging from online customer reviews, scraped from a third-party review platform and three retailing web sites. we utilized customer value and functional, expressive and aesthetic theories to group the results from topic modelling into key themes. clothing function is the most frequently discussed theme in online customer reviews, followed by customer service and clothing aesthetics. collocation analysis revealed the cause underlying each theme vis-à-vis customer satisfaction (e.g., fit and material quality) and dissatisfaction (e.g., sewing defects and lost shipment). the findings contribute to understanding the clothing needs and wants of people living with disabilities. it also provides practical guidelines on product offerings and online service optimization for adaptive clothing retailers.. adaptive clothing online reviews sentiment analysis text analytics topic modelling","analysing customers' reviews and ratings for online food deliveries: a text mining approach. the purpose of this study was to explore the relationship between online reviews and ratings through text mining and empirical techniques. an indian food delivery portal (zomato.com) was used, where 50 restaurants on presence across nation (pan) basis were selected through stratified random sampling. a total of 2530 reviews were collected, scrutinized, and analysed. using the nvivo software for qualitative analysis, seven themes were identified from collected reviews, out of which, the ‘delivery’ theme was explored further for identifying sub-themes. linear regression modelling was used to identify the variables affecting delivery ratings and sentiment analysis was also performed on the identified sub-themes. regression results revealed that hygiene and pricing (delivery subthemes) demonstrated lower delivery ratings. these variables can be established as indicators for restaurants and related online food delivery services to build their business model around them. similarly, negative sentiments were observed in pricing and hygiene sub-themes. restaurants and online food services can enhance hygiene levels of their food delivery process in order to receive higher delivery ratings. similarly, pricing of food items can be modified such that customers are not deterred from ordering the items—food and ordering service do not become cost-prohibitive. this study devised a standardized methodology for analysing vast amounts of online user-generated content (ugc). findings from this study can be extrapolated to other sectors and service industries such as, tourism, cleaning, transportation, hospitals and engineering especially during the pandemic.. customer satisfaction food delivery nvivo qualitative data analysis ratings reviews","examining post-purchase consumer responses to product automation. automation is increasingly being introduced into a variety of consumer products, ranging from vacuum cleaners to autonomous vehicles. while automation provides convenience and efficiency benefits consumers value, related evidence suggests it can also undermine post-purchase consumer product responses of importance to managers (e.g., brand loyalty). using insights derived from amazon customer reviews, a survey of product owners, a virtual reality lab, and two vignette experiments, we formally explore this possibility and find that automation is indeed a double-edged sword. that is, we uncover that automation has undesirable effects on post-purchase outcomes because it interferes with psychological ownership formation. we also find that, depending on consumer identity motives (e.g., task-related vs. technology-related) and product design affordances (e.g., a remote access feature), this effect can be strengthened, weakened, or even reversed. our findings offer managers needed guidance on how to counter automation’s dark side through identity-based targeting and product design.. automation design affordances identity propensity score matching psychological ownership text analysis","exploring mobile banking service quality dimensions in pakistan: a text mining approach. purpose: customer reviews of mobile banking (m-banking) apps contain the most direct and first-hand accounts of customer experiences with m-banking. however, surprisingly little effort has been made to understand m-banking service quality using these reviews. therefore, this study aims to discover m-banking service quality dimensions from customers' reviews of the m-banking apps through a text mining approach. design/methodology/approach: reviews of m-banking apps of 24 banks operating in pakistan were scraped from google play store. latent dirichlet allocation (lda) method was applied to discover the dimensions of m-banking service quality from 24,529 positive and 29,569 negative useable reviews. findings: different dimensions of m-banking service quality are discussed in positive and negative reviews. positive reviews focus on security, convenience, ease of use, continuous improvement, usefulness and app attributes, whereas negative reviews discuss system availability, responsiveness, faulty updates, login problems and reliability. research limitations/implications: the results are based only on customer reviews in one country and generalization may not be possible. moreover, due to the unavailability of demographic information about reviewers, the effect of demographic characteristics on users' perceptions of m-banking quality could not be determined. practical implications: the study provides managers with useful insights to improve the service experience of m-banking customers. the study also demonstrates how managers can employ text analytical techniques to assess and improve the quality of m-banking services. originality/value: in addition to enriching the understanding of m-banking quality based on direct and first-hand user experiences, the current study also provides initial evidence for the two-factor structure of m-banking service quality.. mobile applications mobile banking (m-banking) service quality text mining topic modeling","the importance of language: a comparison of consumer and academic definitions of mindfulness. this article created a consumer and an academic mindfulness dictionary to better understand consumers', academics', and marketers' perceptions of mindfulness. a consumer survey and text mining methods were used to create the consumer dictionary, and an extensive review of mindfulness definitions was used for the academic dictionary. the consumer dictionary described mindfulness more in terms of affect and resulting outcomes, while the academic dictionary was more cognitive and process-oriented. the dictionaries were then applied to marketers' communications from a database of 883 company descriptions that were classified as mindful. the results showed that marketers used more terms from the consumer dictionary than the academic dictionary. the article discusses the potential for consumers to be vulnerable to marketers' communication strategies, characterized as mcmindfulness and mindful washing, that can impact consumers' well-being and brand relationships.. marketing communication mcmindfulness mindful washing mindfulness text mining well-being","paralanguage classifier (para): an algorithm for automatic coding of paralinguistic nonverbal parts of speech in text. brands and consumers alike have become creators and distributors of digital words, thus generating increasing interest in insights to be gained from text-based content. this work develops an algorithm to identify textual paralanguage, defined as nonverbal parts of speech expressed in online communication. the authors develop and validate a paralanguage classifier (called para) using social media data from twitter, youtube, and instagram (n = 1,241,489 posts). using auditory, tactile, and visual properties of text, para detects nonverbal communication cues, aspects of text often neglected by other word-based sentiment lexica. this work is the first to reveal the importance of textual paralanguage as a critical indicator of sentiment valence and intensity. the authors further demonstrate that automatically detected textual paralanguage can predict consumer engagement above and beyond existing text analytics tools. the algorithm is designed for researchers, scholars, and practitioners seeking to optimize marketing communications and offers a methodological advancement to quantify the importance of not only what is said verbally but how it is said nonverbally.. consumer engagement consumer linguistics emoji nonverbal communication online word of mouth social media text analytics textual paralanguage","systematic differences in online reviews of hotel services between business and leisure travelers. to understand whether the purpose of travel affects differences in the evaluation of hotel services, we analyzed online reviews for 768 hotels in new york city. the results show that leisure travelers are predisposed to leaving online reviews with higher ratings and positive emotions than those posted by business travelers, while the latter's review ratings are more widely dispersed, indicating greater inconsistency in their evaluations of hotel services. based on our empirical evidence about systematic differences in online reviews of hotel services between business and leisure travelers, we can provide the hospitality industry with meaningful business implications.. content analysis hotel service online review service rating text mining","exploring mobile banking adoption and service quality features through user-generated content: the application of a topic modeling approach to google play store reviews. purpose: the primary purpose of this research is to analyze the online user reviews, where real customer experiences can be observed, with text mining and machine learning approaches, which are seen as a gap in the related literature. this study aims to compare the latent themes uncovered by the topic modeling approach with studies focused on both mobile banking (m-banking) adaptation and service quality features, suggest new aspects and examine the effect of latent topics on customer satisfaction. design/methodology/approach: this study analyzed 21,526 reviews posted by customers of private and state banks operating in türkiye. an unsupervised machine learning method, latent dirichlet algorithm (lda), was conducted to reveal topics, and the distribution of all reviews was visualized with the t-sne algorithm. random forest, logistic regression, k-nearest neighbors (knn) and naive bayes algorithms were utilized to predict user satisfaction through the given score. findings: in total, 11 topics were revealed by considering user reviews based on their experience. among these topics, perceived usefulness and convenience and time-saving are much more important in the scoring given to m-banking apps. furthermore, in more detail, seven topics have been identified related to technical and security problems related to m-banking apps. originality/value: this paper is a pioneer study regarding the method used and sample size reached in the m-banking literature. the findings also provide fresh insight into the post-covid-19 era, both academically and practically, by providing new features for mobile bank adoption.. latent dirichlet allocation algorithm machine learning mobile banking service quality technology adoption text mining","development of methodology for classification of user experience (ux) in online customer review. in e-commerce, customer feedback has become an essential source of insight into a product or service's user experience (ux). the study of ux helps to integrate customers' potential needs into the product's design. because customer reviews in e-commerce are not structured and categorized, it is necessary to analyze ux based on customer opinions systematically. this study tries to structure ux in a product's positive/negative context through a neural network-based self-organizing map (som). as a result of analyzing 10,482 reviews on wireless earbuds in bestbuy, an electronic product e-commerce platform, it was confirmed that it is a suitable method for categorizing user experiences between reviews and deriving important factors. in particular, the difference in core ux elements by positive/negative context of the product was verified based on the star rating. the results of this study are expected to contribute to product improvement and business improvement that reflect customer needs by companies or designers who design products for end-users.. online reviews self-organizing map text mining user experience","complaint de-escalation strategies on social media. to date, the literature offers multiple suggestions for how to recover from service failures, albeit without explicitly addressing customers’ negative, high-arousal states evoked by the failure. the few studies that do address ways to improve negative emotions after failures focus on face-to-face interactions only. because many customers today prefer to complain on social media, firms must learn how to effectively de-escalate negative, high-arousal emotions through text-based exchanges to achieve successful service recoveries. with three field studies using natural language processing tools and three preregistered controlled experiments, the current research identifies ways to mitigate negative arousal in text-based social media complaining, specifically, active listening and empathy. in detail, increasing active listening and empathy in the firm response evokes gratitude among customers in high-arousal states, even if the actual failure is not (yet) recovered. these findings provide a new theoretical perspective on the role of customer arousal in service failures and recoveries as well as managerially relevant implications for dealing with public social media complaints.. de-escalation digital service recovery negative arousal social media complaints","text mining-based four-step framework for smart speaker product improvement and sales planning. the smart speaker market, which is considered an early-stage market, is expected to grow rapidly as smart speakers become a part of daily life. consequently, manufacturers are trying to dominate the market. to achieve this, they must analyze users' reactions to their products and find insights for product improvement through comparison with competitors. we propose a four-step methodological framework for identifying meaningful opinions from a large number of online user reviews. first, network analysis is conducted to compare differences between brands. next, through topic modeling, the attributes of speakers that users consider the most crucial are extracted for each brand. third, sentiment analysis is conducted to examine how users' emotional polarities differ for each attribute. through this, product improvement and product sales plans can be derived. finally, in order to clarify the strengths and weaknesses of each brand, brand positioning is conducted and user opinions that have changed along with the evolution of the speaker's generation are analyzed. our study identified the factors that positively or negatively affect the experience of smart speaker users. in addition, the proposed method is highly useful because it can be used to derive insights from a large amount of user opinion data regardless of the search term.. brand positioning decision support online review sales planning smart speaker text analysis","more than a feeling: accuracy and application of sentiment analysis. sentiment is fundamental to human communication. countless marketing applications mine opinions from social media communication, news articles, customer feedback, or corporate communication. various sentiment analysis methods are available and new ones have recently been proposed. lexicons can relate individual words and expressions to sentiment scores. in contrast, machine learning methods are more complex to interpret, but promise higher accuracy, i.e., fewer false classifications. we propose an empirical framework and quantify these trade-offs for different types of research questions, data characteristics, and analytical resources to enable informed method decisions contingent on the application context. based on a meta-analysis of 272 datasets and 12 million sentiment-labeled text documents, we find that the recently proposed transfer learning models indeed perform best, but can perform worse than popular leaderboard benchmarks suggest. we quantify the accuracy-interpretability trade-off, showing that, compared to widely established lexicons, transfer learning models on average classify more than 20 percentage points more documents correctly. to form realistic performance expectations, additional context variables, most importantly the desired number of sentiment classes and the text length, should be taken into account. we provide a pre-trained sentiment analysis model (called siebert) with open-source scripts that can be applied as easily as an off-the-shelf lexicon.. deep contextual language models machine learning meta-analysis natural language processing sentiment analysis text mining transfer learning","automated inference of product attributes and their importance from user-generated content: can we replace traditional market research?. user-generated content, particularly online product reviews by customers, provide marketers with rich data of customer evaluations of product attributes. this study proposes, benchmarks, and validates a new approach for inferring attribute-level evaluations from user-generated content. moreover, little is known about whether and when insights from product reviews gained in such a way are consistent with traditional research methods, such as conjoint analysis and satisfaction driver analysis. to provide first insights into this question, the authors apply their approach to a dataset with almost one million product reviews from 52 product categories and run conjoint and satisfaction driver analyses for these categories. results indicate that the consistency between methods largely varies across product categories. initial exploratory analyses suggest that consistency might be higher for categories characterized by low experience qualities, high hedonic value, and high customer willingness to post online reviews—but further work will be necessary to validate these findings.. conjoint analysis involvement marketing research automation natural language processing satisfaction driver analysis user-generated content","complementing human effort in online reviews: a deep learning approach to automatic content generation and review synthesis. online product reviews are ubiquitous and helpful sources of information available to consumers for making purchase decisions. consumers rely on both the quantitative aspects of reviews such as valence and volume as well as textual descriptions to learn about product quality and fit. in this paper we show how new achievements in natural language processing can provide an important assist for different kinds of review-related writing tasks. working in the interesting context of wine reviews, we demonstrate that machines are capable of performing the critical marketing task of writing expert reviews directly from a fairly small amount of product attribute data (metadata). we conduct a kind of “turing test” to evaluate human response to our machine-written reviews and show strong support for the assertion that machines can write reviews that are indistinguishable from those written by experts. rather than replacing the human review writer, we envision a workflow wherein machines take the metadata as inputs and generate a human readable review as a first draft of the review and thereby assist an expert reviewer in writing their review. we next modify and apply our machine-writing technology to show how machines can be used to write a synthesis of a set of product reviews. for this last application we work in the context of beer reviews (for which there is a large set of available reviews for each of a large number of products) and produce machine-written review syntheses that do a good job – measured again through human evaluation – of capturing the ideas expressed in the reviews of any given beer. for each of these applications, we adapt the transformer neural net architecture. the work herein is broadly applicable in marketing, particularly in the context of online reviews. we close with suggestions for additional applications of our model and approach as well as other directions for future research.. artificial intelligence automation deep learning machine learning online reviews review synthesis wine reviews","an online research approach for a dual perspective analysis of brand associations in art museums. the paper develops a research approach that combines digital ethnography with text mining to explore consumers’ perception of a brand and the degree of alignment between brand identity and image. in particular, the paper investigates the alignment between the art museum’s brand identity and the brand image emerging from visitors’ narratives of their experience. the study adopts a mixed methodology based on netnography and text mining techniques. the analysis concerns an art museum’s brand, with the case of the “opera del duomo museum” in florence. the methodological approach enables a combined investigation of user-generated content in online communities and the company’s online brand communication, contributing to identifying branding actions that can be taken to increase the brand alignment. it also enables the measurement of the degree of alignment between museums and visitors among common brand themes. specific indicators of alignment are provided. a key point is the replicability of the model in other contexts of analysis in which the content produced by consumers in online contexts are relevant and readily available, such as fashion or food.. art museum brand identity brand image digital ethnography text mining user-generated content","analyzing competitive market structures based on online consumer-generated content and sales data. purpose: this study aims to provide a way to derive inter-brand similarities from user-generated content on online brand forums, which enables the authors to analyze the market structures based on consumers' actual information searching and sharing behavior online. this study further presents a method for deriving inter-brand similarities from data on how the sales of competing brands covary over time. the results obtained by the above two methods are compared to each other. design/methodology/approach: in drawing similarities between brands, the authors utilized a newly proposed measure that modified the lift measure. the derived similarity information was applied to multidimensional scaling (mds) to analyze the perceived market structure. the authors applied the proposed methodology to the imported car market in south korea. findings: in light of some clear information such as the country of origin, the market structure derived from the presented methodology was seen to accurately reflect the consumer's perception of the market. a significant relevance has been found between the results derived from user-generated online content and sales data. originality/value: the presented method allows marketers to track changes in competitive market structures and identify their major competitors quickly and cost-effectively. this study can contribute to improving the utilization of the overflowing information in the big data era by proposing methods of linking new types of online data with existing market research methods.. brand positioning inter-brand similarity lift measure perceptual mapping text mining","privacy considerations for online advertising: a stakeholder’s perspective to programmatic advertising. purpose: privacy considerations have become a topic with increasing interest from academics, industry leaders and regulators. in response to consumers’ privacy concerns, google announced in 2020 that chrome would stop supporting third-party cookies in the near future. at the same time, advertising technology companies are developing alternative solutions for online targeting and consumer privacy controls. this paper aims to explore privacy considerations related to online tracking and targeting methods used for programmatic advertising (i.e. third-party cookies, privacy sandbox, unified id 2.0) for a variety of stakeholders: consumers, adtech platforms, advertisers and publishers. design/methodology/approach: this study analyzes the topic of internet user privacy concerns, through a multi-pronged approach: industry conversations to collect information, a comprehensive review of trade publications and extensive empirical analysis. this study uses two methods to collect data on consumer preferences for privacy controls: a survey of a representative sample of us consumers and field data from conversations on web-forums created by tech professionals. findings: the results suggest that there are four main segments in the us internet user population. the first segment, consisting of 26% of internet users, is driven by a strong preference for relevant ads and includes consumers who accept the premises of both privacy sandbox and unified id (uid) 2.0. the second segment (26%) includes consumers who are ambivalent about both sets of premises. the third segment (34%) is driven by a need for relevant ads and a strong desire to prevent advertisers from aggressively collecting data, with consumers who accept the premises of privacy sandbox but reject the premises of uid 2.0. the fourth segment (15% of consumers) rejected both sets of premises about privacy control. text analysis results suggest that the conversation around uid 2.0 is still nascent. google sandbox associations seem nominally positive, with sarcasm being an important factor in the sentiment analysis results. originality/value: the value of this paper lies in its multi-method examination of online privacy concerns in light of the recent regulatory legislation (i.e. general data protection regulation and california consumer privacy act) and changes for third-party cookies in browsers such as firefox, safari and chrome. two alternatives proposed to replace third-party cookies (privacy sandbox and unified id 2.0) are in the proposal and prototype stage. the elimination of third-party cookies will affect stakeholders, including different types of players in the adtech industry and internet users. this paper analyzes how two alternative proposals for privacy control align with the interests of several stakeholders.. google privacy sandbox online tracking privacy privacy sandbox programmatic advertising third-party cookies unified id 2.0","vertical versus horizontal variance in online reviews and their impact on demand. this article examines the differential impact of variances in the quality and taste comments found in online customer reviews on firm sales. using an analytic model, the authors show that although increased variance in consumer reviews about taste mismatch normally decreases subsequent demand, it can increase demand when mean ratings are low and/or quality variance is high. in contrast, increased variance in quality always decreases subsequent demand, although this effect is moderated by the amount of variance in tastes. since these theoretical demand effects are predicated on the assumption that consumers can differentiate between the two sources of variation in ratings, the authors conduct a survey to test this assumption, demonstrating that participants are indeed able to reliably distinguish quality from taste evaluations in two subsets of 5,000 reviews taken from larger data sets of reviews for 4,305 restaurants and 3,460 hotels. the authors use these responses to construct sets of reviews that they use in a controlled laboratory experiment on restaurant choice, finding strong support for the theoretical predictions. these responses are also used to train classifiers using a bag-of-words model to predict the degree to which each review in the larger data sets relates to quality and/or taste, allowing the authors to estimate the two types of review variances. finally, the authors estimate the effects of these variances in overall ratings on establishment sales, again finding support for the theoretical results.. crowdsourced data machine learning quality and taste variance review variance text analysis vertical and horizontal content","understanding the role of entrepreneurial orientation in creating ambidextrous competitive advantage: a comparative-design, longitudinal study. purpose: this research aims to investigate how entrepreneurial orientation influences a firm’s differentiation–cost advantage ambidexterity (dcaa) and performance indirectly through dynamic capabilities, while also investigating the impact of the interactive effect of the level and consistency of entrepreneurial orientation on dynamic capabilities. the goal of this study is to better understand the importance of consistently following an entrepreneurial orientation, as well as the linkage of such a consistently implemented strategy upon gaining both a cost and differentiation positioning enhancement. design/methodology/approach: two empirical studies are conducted to test the proposed hypotheses – one longitudinal with multiple forms of data (i.e. text data, survey data and archival data) from 100 taiwanese electronics firms and the other using primary data from a survey of senior managers. findings: entrepreneurial orientation improves dynamic capabilities, which in turn promote superior dcaa and enhanced firm performance. in addition, as captured through a unique measure of consistency allowed through computer-aided text analysis, the results indicate that the effect of entrepreneurial orientation on dynamic capabilities is amplified when a firm consistently adopts entrepreneurial decisions and actions. research limitations/implications: the specific context was a sample of electronics firms in taiwan. this limits the generalization of findings, as would be possible with assessing the hypotheses in other industries. practical implications: this research clearly demonstrates the significance of consistency in pursuing a strategic orientation. the consistent support and deployment of resources facilitates an organization’s achieving positive outcomes associated with an entrepreneurial orientation. originality/value: while entrepreneurial orientation contributes to firm performance, extant knowledge on the internal process through which entrepreneurial orientation affects performance is relatively limited. the findings not only highlight the full mediating role of dynamic capabilities and dcaa, but also shed light on the importance of consistency in entrepreneurial orientation over time.. ambidextrous competitive advantage computer-aided text analysis dynamic capabilities entrepreneurial orientation longitudinal design","discovering ethnic minority business research directions using text mining and topic modelling. purpose: this study aims to unveil within the current academic literature the principal directions in the ethnic entrepreneurship and small business marketing research context. design/methodology/approach: an automated literature analysis procedure was undertaken, attempting to cover all literature published on the subject since 1962. a total of 188 articles were analysed using text mining and topic modelling. findings: the results show a lack of framing of ethnic entrepreneurship literature outside the narrower scope of migration. some core themes were found (e.g. network, diversity) around which several other themes orbit, including both related issues to the ethnic factor (e.g. barriers and minorities) and managerial issues (e.g. marketing and production). originality/value: ethnic minority business and small business marketing research has seen a growing number of publications. however, a careful review of existing work is missing.. entrepreneurial marketing entrepreneurship ethnic entrepreneurs ethnic entrepreneurship literature analysis small business marketing","the importance of marketing mix planning and customer orientation for venture capital–financed startups: impacts on valuation, performance, and survival. purpose: existing literature shows that marketing capabilities of new ventures are critical success factors affecting venture capital funding, startup performance and business failure. the purpose of this study is to investigate whether venture capitalists reward extensive marketing strategies in their startup valuation and whether the marketing mix planning and early strategies on customer orientation predict long-term development of startups. design/methodology/approach: to address these gaps, this study investigate 107 business plans of new ventures which received venture capital based on these planning documents. the authors use computer-aided text analysis and regression analyses. findings: this study’s findings show that customer orientation has positive effects on new venture performance and intensive marketing mix planning increases the likelihood of survival. however, venture capitalists decrease their startup valuation when they read too much about customer orientation and operative marketing mix planning. originality/value: this study relies on unique internal documents and therefore provides valuable and new insights for research and practice. further, this study investigate various short- and long-term effects from marketing and customer orientation for a startups’ development.. business planning customer orientation marketing strategy startup survival venture capital valuation","from crisis to advocacy: tracing the emergence and evolution of the lgbtqia+ consumer market. although governments and organizations are increasingly addressing the importance of diversity and inclusion policies, lgbtqia+ consumers still experience instances of discrimination and stigmatization in the market. research identifying the barriers and struggles that these consumers face is therefore imperative in order to inspire more inclusive marketplace practices. by combining bibliometric and automated text mining methods, this article systematically reviews the existing scholarship on lgbtqia+ issues at the intersection of marketing and public policy and identifies five thematic clusters: consumer experiences, marginalized consumer identities, imagery creation in advertising, marketplace policies, and minority targeting strategies. further, this article plots the temporal evolution of this literature domain and identifies three substantive phases: crisis, marketization, and advocacy. the outcome is a phasic framework that unpacks how the lgbtqia+ consumer market emerged and evolved. this conceptual framework can be used to understand and strategically invigorate research that leads to more inclusive marketing and public policy efforts.. bibliometric review discrimination diversity inclusion lgbtqia+ consumers text mining","mindminer: uncovering linguistic markers of mind perception as a new lens to understand consumer–smart object relationships. prior research revealed a striking heterogeneity of how consumers view smart objects, from seeing them as helpful partners to merely a useful tool. we draw on mind perception theory to assess whether the attribution of mental states to smart objects reveals differences in consumer–smart object relationships and device usage. we train a language model to unobtrusively predict mind perception in smart objects from consumer-generated text. we provide a rich set of interpretable linguistic markers for mind perception, drawing on a diverse collection of text-mining techniques, and demonstrate that greater mind perception is associated with expressing a more communal (vs. instrumental) relationship with the device and using it more expansively. we find converging evidence for these associations using over 20,000 real-world customer reviews and also provide causal evidence that inducing a more communal (vs. instrumental) relationship with a smart object enhances mind perception and in turn increases the number of tasks consumers engage in with the device. these findings have important implications for the role of mind perception as a novel lens to study consumer–smart object relationships. we offer an easy-to-use web interface to access our language model using researchers own data or to fine-tune the model to entirely new domains.. deep learning interpretable machine learning language models mind perception natural language processing smart objects text mining theory of mind","the effect of subjectivity and objectivity in online reviews: a convolutional neural network approach. user reviews are now an essential source of information for consumers, exerting strong influence on purchase decisions. broadly speaking, reviews rated by consumers as more helpful exert a greater influence downstream. the current research examines how the linguistic characteristics of a review affect its helpfulness score. using a convolutional neural network (cnn), this research analyzes the linguistic subjectivity and objectivity of over 2 million reviews on amazon. the results show that, ceteris paribus, both linguistic subjectivity and objectivity have a positive impact on review helpfulness. however, contrary to consumers' intuition, when subjectivity and objectivity are combined in the same review, review helpfulness increases less than their respective separate effects would predict, especially for hedonic products. we conceptualize that this results from the increased complexity of messages mixing subjective and objective sentences, which requires more effortful processing. the findings extend the literature on online reviews, word-of-mouth, and text analysis in marketing, and offer practical implications for marketing communication and facilitation of reviews.. neural network online reviews subjectivity text analysis word-of-mouth","speedy activists: how firm response time to sociopolitical events influences consumer behavior. organizations face growing pressure from their consumers and stakeholders to take public stances on sociopolitical issues. however, many are hesitant to do so lest they make missteps, promises they cannot keep, appear inauthentic, or alienate consumers, employees, or other stakeholders. here we investigate consumers' impressions of firms that respond quickly or slowly to sociopolitical events. using data scraped from instagram and three online experiments (n = 2452), we find that consumers express more positive sentiment and greater purchasing intentions toward firms that react more quickly to sociopolitical issues. unlike other types of public firm decision making such as product launch, where careful deliberation can be appreciated, consumers treat firm response time to sociopolitical events as an informative cue of the firm's authentic commitment to the issue. we identify an important boundary condition of this main effect: speedy responses bring limited benefits when the issue is highly divisive along political lines. our findings bridge extant research on brand activism and communication, and offer practical advice for firms.. brand activism branding communications sociopolitical issues text analysis","passivepy: a tool to automatically identify passive voice in big text data. the academic study of grammatical voice (e.g., active and passive voice) has a long history in the social sciences. it has been examined in relation to psychological distance, attribution, credibility, and deception. most evaluations of passive voice are experimental or small-scale field studies, however, and perhaps one reason for its lack of adoption is the difficulty associated with obtaining valid, reliable, and replicable results through automated means. we introduce an automated tool to identify passive voice from large-scale text data, passivepy, a python package (readymade website: https://passivepy.streamlit.app/). this package achieves 98% agreement with human-coded data for grammatical voice as revealed in two large validation studies. in this paper, we discuss how passivepy works, and present preliminary empirical evidence of how passive voice connects to various behavioral outcomes across three contexts relevant to consumer psychology: product complaints, online reviews, and charitable giving. future research can build on this work and further explore the potential relevance of passive voice to consumer psychology and beyond.. automated text analysis grammatical voice passive voice","how construal–regulatory mode fit increases social media sharing. how can social media managers engage consumers to share posts with others? extending regulatory mode theory, we demonstrate that high construal levels enable the integration of regulatory mode complementarity orientations, resulting in engagement and shares. regulatory mode complementarity refers to the combination of high assessment (i.e., the motivation to “be right” by critically evaluating options) and high locomotion (i.e., the motivation to “act” by moving toward a goal). specifically, this research proposes that an abstract (vs. concrete) construal allows these two orientations to work together, resulting in regulatory fit. three text analysis field studies on marketer- and consumer-generated facebook and twitter posts show that construal–regulatory mode fit increases social media sharing. three follow-up studies then show generalizability, establish causality, and demonstrate the role of engagement as the underlying mechanism driving the fit effect.. construal level engagement regulatory fit regulatory mode complementarity sharing","but it was supposed to be healthy! how expected and actual nutritional value affect the content and linguistic characteristics of online reviews for food products. consumers are choosing to purchase food products from retailers through online channels rather than brick-and-mortar channels. while online reviews play a crucial role in influencing online purchases, scant work has examined how consumers write reviews for food products. we argue that the nutritional value of the food is a key aspect of product performance and apply expectation-disconfirmation theory to examine whether pre-purchase expectations about a food product's nutritional value and disconfirmation of these expectations have a significant effect on online review content and linguistic characteristics. using text-mining approaches to analyze amazon data, we find that pre-purchase expectations, postpurchase performance, and disconfirmation regarding nutritional value affect both review content and linguistic characteristics, including review length, diversity, readability, subjectivity, and sentiment. while research suggests that postpurchase product performance is the main influence on online review writing behavior, this research shows that the pre-purchase phase also plays a key role.. linguistics nutrition online consumer reviews online food retail text mining","social media messaging for health. social media has become a key information source for most consumers. however, as social media platforms are not highly regulated, it has given rise to mass amounts of health misinformation. many unqualified people, typically social media influencers, are profiting from sharing nonevidence- based messages and qualified health professionals are unable to match their audience engagement. however, social media also holds the potential to be used to promote evidence-based messages to large audiences and contribute to positive change within society. the application of machine learning and natural language processing techniques to understand social media data is common, with increasing health research applications. there are many challenges and opportunities for health as the world progresses through the digital era. this chapter aims to present the potential applications, benefits, and issues of using social media within the health and nutrition space based on the collective expertise of a multidisciplinary team from nutrition, social marketing and data science.. health machine learning natural language processing nutrition social media","the effects of live comments and advertisements on social media engagement: application to short-form online video. purpose: this study aims to examine the moment-to-moment (mtm) effects of in-consumption dynamic comments on consumers' responses to digital engagement and the underlying mechanisms involved, as well as the interactive role of advertisements embedded in short-form online video. design/methodology/approach: this study uses data extracted from 2,081 videos posted on the prominent chinese online live platform, bilibili. the hypotheses are tested using regression models and natural language processing. findings: the results indicate that the intensity of live comments at the beginning negatively affects users' digital engagement, while a corresponding increase in live comments at the end elicits a positive effect. a linear trend and peak difference in live comments intensity positively affect digital engagement, while the variability of live comment intensity exerts a negative effect. these mtm effects were driven by sentiments of live comments. furthermore, in-video advertisements are likely to amplify the negative beginning effect on users' digital engagement and mitigate the negative variability of live comments. originality/value: this study is the first to examine the direct effects of mtm comments from the online temporal sequence perspective, differentiating the process- and performance-based engagement. the mechanism and interactive role of in-video advertisements were identified. these findings contribute to literature on interactive marketing and provide valuable guidance for influencer marketing.. digital engagement in-video advertisements live comments moment-to-moment data","words meet photos: when and why photos increase review helpfulness. are reviews with photos more helpful? if so, do consumers find reviews more helpful when photos and text convey similar or different information? this article examines the effect of content similarity between photos and text on review helpfulness and its underlying mechanism. using a data set of 7.4 million reviews associated with 3.5 million photos from yelp, and applying machine learning algorithms, the authors quantify the similarity of the content between text and photos. they find that, overall, photos increase the helpfulness of a review. more importantly, though, greater similarity between photos and text heightens review helpfulness more. the authors then validate algorithm-based similarity assessments with similarity perceptions of human judges. using real-world reviews from yelp and carefully designed stimuli, they replicate the core findings in five laboratory experiments. further, testing the underlying mechanism, they find that greater similarity facilitates the ease with which consumers can process the review, which, in turn, increases that review's helpfulness to consumers. finally, they show that factors that impede the ease of processing (e.g., language difficulty or poor image quality) can reduce the effect of similarity on helpfulness. these findings provide novel insights into the value of user-generated content that includes text and photos and its underlying mechanism.. helpfulness natural language processing photos reviews similarity user-generated content visuals","what affects the use of green technology in hotels? assessing hotel management viewpoint using natural language processing based qualitative study. hotels across the globe are trying to adopt green technologies for reducing the negative impact of the hospitality segment on the environment. however, the implementation of green technologies in hotels can be affected by the perspectives of managers. there is a need to assess the views of managers regarding the enablers, barriers and skill-sets that affect the adoption of green technologies in hotels. existing studies fail to draw a proper link between the factors that affect green technology use in hotels and the website content of green hotels. semi-structured interviews of fifteen managers and topic-modeling of the website content of forty-one indian green hotels reveal that there is a gap in communications and awareness. the qualitative study using a grounded theory and phenomenology approach has found motivators like, cost reduction, value-for-nature, and barriers like, misperception of customers, high cost. the study highlighted the importance of green skills as a moderator. 全球的酒店都在尝试采用绿色技术来减少酒店业对环境的负面影响. 然而，酒店绿色技术的实施可能会受到管理者观点的影响. 有必要评估管理人员对影响酒店采用绿色技术的推动因素、障碍和技能的看法. 现有研究未能在影响酒店绿色技术使用的因素与绿色酒店网站内容之间建立适当的联系. 对 15 名经理的半结构化访谈和对 41 家印度绿色酒店网站内容的主题建模表明，在沟通和意识方面存在差距. 使用扎根理论和现象学方法的定性研究发现了诸如降低成本、保护自然价值等激励因素，以及诸如对客户的误解、高成本等障碍. 该研究强调了绿色技能作为主持人的重要性.. green hotels green skills green technology natural language processing qualitative approach topic modeling","the role of affective ties in the asymmetrical relationship between student satisfaction and loyalty. comparative study of european business schools. in the context of an increasingly competitive environment in higher education, this article looks at the determinants of student satisfaction in european business schools. we analysed one french and one georgian business school through a survey of 551 students. structural equation modelling was used to test the research hypotheses, with qualitative data analysis further explaining the findings. the results identify the determinants of student satisfaction and confirm a positive and significant influence of student satisfaction on their loyalty. however, the findings reveal an asymmetrical relationship between the two constructs and suggest that, in addition to a high level of satisfaction, affective ties need to be ensured between heis and their students to generate loyalty. the study contributes to the literature by emphasising the importance of customer satisfaction in a service industry from an affective standpoint through the lens of customer expectations. it also demonstrates the importance of dissatisfier elements, especially when it comes to examining loyalty. the results are of vital importance for business school management to develop strategies aimed at attracting and retaining students, which will in turn improve financial performance and successful positioning.. affective ties business school hedperf loyalty nlp service quality student satisfaction","deep learning applications for interactive marketing in the contemporary digital age. this chapter reviews recent research deploying deep learning (dl) to resolve problems in interactive marketing. it takes stock of what marketers know, how and why they do that. from an engagement marketing perspective, this chapter catalogues dl application cases in (1) customer acquisition, expansion, and retention, (2) marketing communication, and (3) product innovation. by identifying challenges from technological advancement and interactive marketing needs, it characterizes eleven issues that marketers have to deal with. they include the curse of dimensionality, complex data processing, language-image nexus, and customer privacy. this chapter elucidates the dl logic and solution techniques to correspondingly address these issues. it also introduces some technical terms for communication with data scientists. they include generative adversarial networks, transfer learning, and federated learning. a wide range of typical dl models is presented, from simple classification to sophisticated language models. marketers have to choose from them according to their application matters. this chapter concludes that dl is indispensable to fulfilling today’s customer needs for performance and interactivity. the review shall help formulate dl projects and it can act as a handy reference for exotic marketing innovations upon new technology trends, like the metaverse. implications and challenges will be discussed in the ending section. hopefully, marketers will find dl easier, for implementation or outsourcing.. big data deep learning engagement marketing interactive marketing nlp","humanizing chatbots for interactive marketing. chatbots, also known as conversational agents, are automated computer programs powered by natural language processing designed to engage consumers in interactive, one-on-one, personalized text- or voice-based conversations. focusing on text-based, anthropomorphic social chatbots that can be easily implemented on various digital platforms, this chapter synthesizes the literature on computer-mediated communication and human-computer interaction to provide a comprehensive review of the pivotal factors that can enhance chatbots’ perceived humanness and the key considerations in consumer-chatbot interaction and relationship. specifically, this chapter first discusses the research findings on the persuasiveness of computer-controlled chatbots in relation to human-controlled avatars. then, the chapter delves into the various anthropomorphic cues used in chatbot design and messaging, including human identity cues, verbal, and non-verbal cues. strategies and examples for chatbots to communicate humility in order to mitigate consumers’ frustration when chatbots fail to meet consumers’ expectations are also provided. the chapter next addresses some of the most widely studied mediators of chatbot anthropomorphism-social presence and parasocial interaction-and the under-researched role of emotion in consumer-chatbot interactions. the chapter concludes with a discussion of the “uncanny valley” effect pertaining to people’s feelings of eeriness toward highly human-like chatbots.. anthropomorphism chatbot human-computer interaction social cues uncanny valley","algorithms propagate gender bias in the marketplace—with consumers’ cooperation. recent research shows that algorithms learn societal biases from large text corpora. we examine the marketplace-relevant consequences of such bias for consumers. based on billions of documents from online text corpora, we first demonstrate that from gender biases embedded in language, algorithms learn to associate women with more negative consumer psychographic attributes than men (e.g., associating women more closely with impulsive vs. planned investors). second, in a series of field experiments, we show that such learning results in the delivery of gender-biased digital advertisements and product recommendations. specifically, across multiple platforms, products, and attributes, we find that digital advertisements containing negative psychographic attributes (e.g., impulsive) are more likely to be delivered to women compared to men, and that search engine product recommendations are similarly biased, which influences consumer's consideration sets and choice. finally, we empirically examine consumer's role in co-producing algorithmic gender bias in the marketplace and observe that consumers reinforce these biases by accepting gender stereotypes (i.e., clicking on biased ads). we conclude by discussing theoretical and practical implications.. customer segmentation digital advertising gender bias natural language processing word embedding","identifying nostalgia in text: the development and validation of the nostalgia dictionary. nostalgia is a prevalent emotion that confers psychological benefits and influences consumer behavior. we developed and validated the 98-word nostalgia dictionary to automatize the assessment of nostalgicity in narratives (e.g., customer reviews, social media). first, we created an initial wordlist by identifying the most frequently used words in nostalgia narratives and by relying on the nostalgia literature. second, we finalized the dictionary by testing experimentally the expanded wordlist for its capacity to differentiate nostalgia from related emotions. third, we validated the dictionary by demonstrating that it corresponds to self-reports and coder-ratings of nostalgia, produces result patterns expected by theory, and predicts favorability ratings of books and consumer experiences, even after adjusting for positive emotion words. we discuss the potential of the nostalgia dictionary to advance research and practice.. consumer behavior consumer experiences nostalgia nostalgia dictionary text mining","chatgpt and the hospitality and tourism industry: an overview of current trends and future research directions. since its launch, chatgpt, an artificial intelligence chatbot developed by open ai based on the premises of generative pre-trained transformer autoregressive language models, has gained widespread popularity and is making significant impact on society with its unique features, such as natural language processing and contextual awareness. chatgpt is viewed as a major disruptive innovation that is likely to revolutionize the operations in many industries including the hospitality and tourism industry. the adoption of chatgpt will result in substantial changes throughout the hospitality and tourism industry by disrupting how customer search for information, make decisions, and how businesses produce, create, and deliver customized services and experiences. this conceptual paper provides a comprehensive discussion on generative pre-trained transformers’ (gpts) benefits, and potential challenges and threats they pose to the hospitality and tourism industry. the feasibility of integrating gpt into different travel stages and decision-making processes is also discussed. the article concludes by proposing a potential future research agenda on using gpt in creating and delivering hospitality and tourism experiences, which can guide further advancements in the field.. benefits challenges and threats chatgpt customer behavior research agenda service experience creation and delivery","research on online shopping contextual cues: refining classification from text mining. purpose: contextual cues have become a hot research topic in the field of mobile consumer behavior, owing to the continuous rise of digital marketing. however, the complex online shopping scene makes it challenging to directly identify the association between the characteristics of contextual cues and consumer behavior. presently, few studies have only systematically extracted and refined the types and characteristics of contextual cues. the purpose of this study is to explore the types and mechanisms of contextual cues in online shopping scenarios. design/methodology/approach: this study uses the word2vec algorithm, grounded theory and co-occurrence cluster method, along with online shopping word-of-mouth (wom) text and consumer behavior theory, in order to explore different types of contextual cues and its efficiency from 5,619 comment corpus. findings: this study puts forward the following conclusions. (1) from the perspective of online shopping, contextual cues comprise aesthetic perception cues, value perception cues, trust-dependent cues, time perception cues, memory attention cues, spatial perception cues, attribute cues and relationship cues. (2) based on the online shopping scenarios, contextual cues and their interaction effects exert an effect on consumer satisfaction, recommendation, purchase and return behavior. originality/value: the study conclusions are helpful to further reveal the deep association between contextual cues and consumer behavior in the process of online shopping, thus providing practical and theoretical enlightenment for enterprises to not only effectively reshape the scene but also promote the consumers' active purchase behavior.. contextual cues online consumer behavior online shopping perception cues text mining","what holds attention? linguistic drivers of engagement. from advertisers and marketers to salespeople and leaders, everyone wants to hold attention. they want to make ads, pitches, presentations, and content that captivates audiences and keeps them engaged. but not all content has that effect. what makes some content more engaging? a multimethod investigation combines controlled experiments with natural language processing of 600,000 reading sessions from over 35,000 pieces of content to examine what types of language hold attention and why. results demonstrate that linguistic features associated with processing ease (e.g., concrete or familiar words) and emotion both play an important role. rather than simply being driven by valence, though, the effects of emotional language are driven by the degree to which different discrete emotions evoke arousal and uncertainty. consistent with this idea, anxious, exciting, and hopeful language holds attention while sad language discourages it. experimental evidence underscores emotional language's causal impact and demonstrates the mediating role of uncertainty and arousal. the findings shed light on what holds attention; illustrate how content creators can generate more impactful content; and, as shown in a stylized simulation, have important societal implications for content recommendation algorithms.. automated textual analysis content consumption digital engagement digital marketing emotion natural language processing online content","understanding customer participation dynamics: the case of the subscription box. although subscription boxes are incredibly popular, box companies often miss out on the benefits of a subscription model. customers routinely skip boxes, and even when they do not, they often return products from each box. hoping to avoid these returns, box companies ask customers to preview upcoming boxes, evaluate delivered boxes, and justify skipped boxes. the authors are interested in how such extensive customer participation can discourage skipping or, even better, encourage spending. an analysis of 30,000 apparel box customers’ repeated preview, feedback, and purchase behavior reveals that, in addition to whether customers participate, the way in which and when they participate matter, and often in counterproductive ways. specifically, customer participation with the delivered box drives future purchases, whereas participation before and after the delivered box appears to decrease box opt-in and spending. further, the double-edged nature of customer participation, especially when such participation involves emotionality, has long-lasting effects, indicating the important role of customer participation dynamics in shaping purchase behavior.. customer journey customer participation dynamics subscription box text analysis","“inside” versus “outside” trends in consumer research. given its multi-faceted nature, consumer research has evinced interest from scholars both within and outside the marketing domain, resulting in ongoing knowledge contributions from a diverse range of disciplines. we conduct a comprehensive review and comparison across both marketing (“inside”) and non-marketing (“outside”) disciplines to identify which topics are most and least focused on within each disciplinary domain and which are emerging as the most impactful. we also identify which of the marketing (ft50) journals are at the forefront of cutting-edge research and demonstrate their contributions to the progression of the most impactful topics in the field. based on our findings, we identify key gaps and under-researched areas of substantive interest, highlight impactful emerging topics in non-marketing journals where marketing could offer unique perspectives, and provide concrete suggestions and directions to further progress and stimulate consumer research.. consumer research consumer research trends multidisciplinary research natural language processing topic modeling","extracting marketing information from product reviews: a comparative study of latent semantic analysis and probabilistic latent semantic analysis. user-generated content (ugc) contains customer opinions which can be used to hear the voice of customers. this information can be useful in market surveillance, digital innovation, or brand improvisation. automated text mining techniques are being used to understand these data. this study focuses on comparing two common text mining techniques namely: latent semantic analysis (lsa) and probabilistic latent semantic analysis (plsa) and evaluates the suitability of the methods in two differing marketing contexts: reviews from a product category and from a single brand from amazon. the objectives of review summarization are fundamentally different in these two scenarios. the first scenario can be considered as market surveillance where important aspects of the product category are to be monitored by a particular seller. the second scenario examines a single product, and it is used to monitor in-depth customer opinions of the product. the results support that depending on the objective, the suitability of the technique differs. different techniques provide different levels of precision and understanding of the content. the power of machine learning methods, domain knowledge and marketing objective need to come together to fully leverage the strength of this huge user-generated textual data for improving marketing performance.. amazon reviews big data lsa plsa text mining user genrated content (ugc)","a systematic review of customer behavior in business-to-business markets and agenda for future research. purpose: the purpose of this paper is to offer a comprehensive overview of current research on customer behavior in the business-to-business (b2b) context and propose a research agenda for future studies. despite being a relatively recent area of interest for academics and practitioners, a literature review that synthesizes existing knowledge into coherent topics and outlines a research agenda for future research is still lacking. design/methodology/approach: drawing on a systematic literature review of 219 papers and using a text-mining approach based on the latent dirichlet allocation algorithm, this paper enhances the existing knowledge of b2b customer behavior and provides a descriptive analysis of the literature. findings: from this review, ten major research topics are found and analyzed. these topics were analyzed through the lens of the theory, context, characteristics and method framework, providing a summary of key findings from prior studies. additionally, an integrative framework was developed, offering insights into future research directions. originality/value: this study presents a novel contribution to the field of b2b by providing a systematic review of the topic of customer behavior, filling a gap in the literature and offering a valuable resource for scholars and managers seeking to advance the field.. b2b business-to-business customer behavior future trends research agenda text mining","differences in online review content between old and new products: an abstract. online product reviews are one of the most important sources of information that influences the consumer decision making process. studies have shown a positive relationship between online reviews and product sales. not surprisingly, extant research has studied the factors that influence reviews such as include reviewer credibility and review content, review volume, valence, and dispersion, the impact of negative versus positive reviews, and the influence of product category. despite the important influence of online reviews on consumer decision making, there are several key empirical and theoretical questions in the domain of online reviews that remain unanswered. extensive research on online reviews has been taken place for movies, books, and television shows. there has been relatively limited research into reviews for new products in other, less well-known product categories. in addition, most research has focused on the volume, valence, and variance of reviews. research investigating review content is emerging, although much of this research is based on standard attributes of the text using packages like liwc (linguistic inquiry and word count). research investigating more specific aspects of review content is less prevalent. finally, there have been several studies focused on the impact of online reviews on new products sales and review-based mining to get the pricing power of different attributes. however, these inquiries have not taken into consideration how reviews for new products differ from reviews for similar products that already exist and are available online. the aim of this paper is to compare content of online reviews for new and established products. we seek to understand how consumers evaluate old and new products in the same category based on online reviews and whether there are systematic differences in the type of content discussed for each type of product. we use amazon review data from two categories to compare review content for a new product and the leading established product in each category. the present study serves as a pilot to examine the efficacy of machine-based coding of product reviews along a number of relevant dimensions and probe for potential differences to inform future research. we find that there is a difference in the way consumers review old and new products in a category. the reviews of new products are highly descriptive and detailed with experiences shared by the users. additionally, when we look at the attribute-wise impact on the ratings, it is found that attributes that are common to both seem to have a higher impact on the ratings than attributes which stand out in the new products.. innovation machine learning new products text mining","young people under ‘finfluencer’: the rise of financial influencers on instagram: an abstract. under the shadow of the covid-19 pandemic, millions of young people have the resulting consequences (e.g., increased uncertainty regarding their careers and economic prospects: mcgrindle, 2020; yuesti et al., 2020) started to think more seriously about how to manage their financial resources. as a result, personal finance has become trendy to speak about. rather than proactive actions coming through the formal financial services industry to address the increasing demand for accessible financial information that does not require an advisor or costs a fortune, we are witnessing the uprising of personal finance influencers. these financial influencers (a.k.a. finfluencers) talk about how to manage various aspects of financial life and do so by promoting their advice on a broad scope of money related topics (ranging from dealing with student loans, buying a house, how to ask for a pay raise, budgeting tips, and tricks, filing for tax returns, stock investment opportunities, and portfolio management) in short, light-hearted video formats that are posted on instagram, tiktok, youtube and other social media platforms. although currently, these finfluencers can be regarded as a niche influencer marketing group, it is expected to grow exponentially in the coming years – especially now that designated sections on social media platforms are taking off (e.g., #fintok, #fintwit, #finmeme, #stocktok, #findependence). in line with this trend, this research explores how consumers consume social media influencers when they offer utilitarian (rather than hedonic) oriented products, services, and advice. using text-mining techniques, we examine the content of personal finance influencers on instagram to investigate how consumers respond to the recommendation of more utilitarian-oriented products. in addition, gender and race are an integral part of the consumer’s perception of financial expertise. this research looks at the moderating role of gender and race on influencers’ financial expertise on consumer response. we found that influencers with financial accreditations generate a more positive affective response compared with financial influencers who mainly share information stemming from personal experience. in addition, we found that influencers’ gender and race moderate the relationship between financial expertise and consumers’ affective responses significantly.. influencer marketing social media marketing source credibility utilitarian product","persuasion using video narratives: case of engagement with videos on youtube about covid-19: an abstract. since november 2019, when the cases of covid-19 were first reported, millions of people have been affected by the pandemic in some way. social media has played a major role in generation and dissemination of information about the pandemic. this information on the social media has been posted by a wide range of individuals and organizations including government agencies, health organizations or professionals, news organizations or reporters, for profit and nonprofit organizations, and political, religious, corporate, or community leaders. irrespective of the content or modality, the main objective of people/organizations posting information on social media is to spread their message to as many people as possible. the objective of this research is to examine the factors that contribute to user engagement with video content posted on youtube on covid-19 related topics. as such, the influence of youtube videos about covid-19 is examined within the context of narrative persuasion as compared with argument-based persuasion. this research is based on a unique approach to first analyze the content of youtube videos in terms of having argument-based persuasive elements versus having narrative-based persuasive elements using text analysis. content of videos is then related to user engagement metrics to assess which type of content (argument-based versus narrative-based) can generate greater user engagement. data for the study were collected from youtube videos about covid-19 (n=831). content of selected videos was extracted in the form of transcripts of posted videos and analyzed using linguistic inquiry and word count 2015 (liwc2015) (pennebaker et al., 2015a; pennebaker et al., 2015b). measures of argument-based content of videos (analytical thinking and clout) and narrative-based (authentic and emotional tone) were based on variables reported by liwc2015. youtube engagement metrics reflect how viewers interact with posted videos. of the four publicly available engagement metrics three were used (number of likes, number of dislikes, and number of comments) as dependent variables. overall, results indicate that narrative-based content in youtube videos related to covid-19 have stronger relationship with viewer engagement compared with argument-based content on the same platform.. covid-19 narrative persuasion social media text analysis","consumer engagement in influencer marketing video campaigns: an abstract. the amount of videos users watch on social media platforms, such as instagram and tiktok, has almost doubled since 2018 (hayes, 2022). in marketing, videos are an increasingly successful means for brand to engage with consumers, with several studies finding that video content leads to more consumer engagement (i.e., liking, commenting, or sharing) than text content, which affects brand attitudes and purchase intentions (e.g., li & xie, 2020). social media influencers generate a significant amount of video content online, while many brands spend at least 10% of their yearly marketing budget on influencer marketing (geyser, 2021). however, there is a gap in research investigating the key characteristics in influencer posts that lead to engagement, particularly when it comes to video rather than textual content. in short, there is a need for more understanding about how best to design video content that engages customers. with this in mind, we explore how video characteristics in influencer marketing campaigns affect consumer engagement (in the form of liking and commenting). specifically, we ask: (1) how emotional facial expressions in video content, and (2) corresponding post descriptions (i.e., textual cues) affect consumer engagement with influencers’ social media posts. to answer our research questions, we collected 2,376 social media posts from instagram that were published from 1,240 influencers in partnership with 145 brands across ten product categories, such as apparel, beauty, and technology. all videos were analysed using schwenzow et al.’s (2020) video tool that enables researchers to analyse a broad range of video characteristics on a large scale. the post descriptions were analysed with language inquiry word count (liwc-22). liwc is a text-analytics software (tausczik & pennebaker, 2010) that uses dictionaries to calculate the degree to which each piece of text contains specific category words, such as first-person pronouns or emotional words (e.g., happy, cried, nice, ugly). our results show that, compared to neutral facial expressions, emotional facial expressions in the video are negatively associated with engagement. on the contrary, we find that emotionality in the post description is positively associated with engagement. finally, our findings suggest a significant moderation effect of product category (hedonic vs. utilitarian) on the relationship between emotionality in the post description and engagement. theoretically, drawing on an interplay of theories including emotional contagion theory and heuristic-systematic model of information processing, we shed light on the nature of the relationship between social media post characteristics and consumer engagement in an influencer context. practically, we provide advice on how a popular form of marketing content, video, can be used in an increasingly popular marketing context; influencer marketing.. consumer engagement emotionality facial expressions hedonic value influencer marketing liwc social media utilitarian value video content","information overload in voice-based alexa shopping: does customer involvement play a role?: an abstract. digital personal assistants (dpas) have become an essential component of our lives, supporting such daily tasks as setting up and maintaining schedules, operating home appliances, controlling “smart” electronic devices, providing communication, information and entertainment, and offering companionship. the number of dpas worldwide is expected to increase from 4.2 billion units in 2020 to 8.4 billion by 2024 (statista 2020). their popularity among consumers can be explained by the intuitive and easy, command-triggered voice interface based on natural language processing software that creates an impression of social presence and offers efficient task assistance and companionship. while consumers are increasingly adopting dpas for voice-based shopping, with over 21 million people expected to have made a dpa-assisted purchase in 2021 (emarketer 2020), this process faces a fundamental challenge due to the limited human information processing capacity in the context of audio-linguistic communication (palma et al. 2020). given the dearth of research on consumer information processing during dpa-assisted shopping and decision-making, it is essential to understand the nature, causes, and boundary conditions of voice-shopping io to assist developers and retailers in offering optimal solutions to their customers. this pioneering study presents a pilot test of the roles of product information complexity and consumer product involvement on io, perceived decision quality, satisfaction and buying decision in the context of voice-shopping using the amazon alexa dpa. the results of a voice shopping experiment confirmed differential roles of cognitive and affective dimensions of consumer involvement in moderating the impact of information overload on shopping decisions. specifically, we found that consumers who are more cognitively involved with the product experience lower information overload when presented with product information characterized by high complexity (greater number of product attributes), potentially employing a central information processing route. on the other hand, consumers who are more emotionally involved with the product are more likely to purchase the suggested product when presented with a high complexity product information, potentially activating a peripheral information processing route. we also confirm the negative impact of information overload on perceived decision confidence and satisfaction, supporting earlier findings in different contexts.. alexa digital assistants information overload voice shopping","a bibliometric analysis and text mining of the entrepreneurial marketing domain: emerging trends and future research directions. purpose: the entrepreneurial marketing (em) literature has grown rapidly during the last decade, yet much is left undiscovered. this paper aims to conduct a bibliometric analysis and text mining of the em domain to provide trends in previous em research to address this issue. future avenues for em research are provided. design/methodology/approach: using the scopus database, a bibliometric analysis and text mining are conducted with em articles published between 1976 and 2020. findings: the paper reports the most frequently published and cited authors and their co-author networks. journals which contain these articles are included with country data, subject areas and use of author keywords related to the em topic. frequencies of keywords and newly-emerging keywords in every three-year period during the past 12 years are also examined. the findings suggest that future researchers should explore the subtopics of em education, legitimation, entrepreneurial networking and social media/digital marketing within the em domain. originality/value: bibliometric analysis and text mining are relatively new methods with which to study the em research domain, allowing for analysis of emerging trends over the past 12 years.. bibliometric analysis entrepreneurial education entrepreneurial marketing entrepreneurial marketing literature entrepreneurial networks legitimation social media/digital marketing","the writing is on the wall: using computerized text analysis to predict customers’ evaluation of their service experience: an abstract. ever-increasing numbers of customers are taking to online platforms to review their experiences offering social proof to other potential customers (kaemingk, 2020), thereby reducing uncertainty for others (mladenovic et al., 2019). these reviews influence the purchase decisions of others (kaemingk, 2020; perkins & fenech, 2014), while also providing a valuable source of information for firms (balducci & marinova, 2018). a growing body of literature is seeking to analyze and develop quantitative insights from this readily available unstructured data (e.g., ferreira et al., 2022; korfiatis et al., 2018; lord ferguson et al., 2020; pitt et al., 2018; robertson et al., 2021). researchers and practitioners alike have acknowledged the importance of the correct analysis and interpretation of this data as a vital input in firm decision-making (boon et al., 2013). the majority of academic research has focused on the impact of online reviews on customer decision-making, with much less literature assessing the methodologies used to analyze and interpret online reviews as a source of customer intelligence for the firm (palese & piccoli, 2016). to this end, this methodological paper examines how service firms can analyze unstructured data by using a computerized text analysis to assess evaluations of their customers’ experiences. the proposed methodological approach recommends the use of linguistic inquiry and word count (liwc) as the computerized text analysis tool, offering a practical application of the tool in the healthcare insurance industry. the liwc analysis establishes four summary variables designed to identify psychological states, thinking styles, emotional tone, and social concerns (pennebaker et al., 2015). these lexical components can be used in a regression analysis to predict evaluations of customers’ experience. the findings of the practical application suggest that the lexical analysis of online customer reviews is able to predict evaluations of their customers’ experiences. the results offer service firms an inexpensive methodology with which to assess real-time, readily available evaluations of customer experiences. in addition, this research is among the first of its kind to make use of a computerized text analysis of customer-generated content as a tool to predict their evaluations of a service experience. the research indicates that firms in the service industry can use unstructured data in the form of online customer reviews and transform it into a numerical format to assess customers’ experiences. this methodology greatly impacts the availability of real time data and cost effectiveness of service quality research.. computerized text analysis customer experience digital technologies liwc online customer reviews","cross-national consumer research using structural topic modelling: consumers' approach-avoidance behaviours. this study introduces structural topic modelling (stm), a sophisticated unsupervised machine-learning algorithm for text analysis, to compare indonesian and malaysian muslim consumers' approach-avoidance behaviours toward korean beauty products using social media data. the stm results revealed 16 topics for each country, including new common themes belonging to k-beauty culture and wannabe korean skin. intriguing differences were also observed between these countries. korea-related constructs, such as korea's image and wannabe korean skin, were approach factors for only indonesians. korean cosmetic brand-specific topics were extracted for only malaysians and were significantly associated with their behavioural responses. unsuitable korean beauty products and domestic product preferences were avoidance factors for indonesians, but new product risks and conflicts between muslim and korean cultures for malaysians. we demonstrate that stm is a helpful tool in cross-national research for corroborating and extending the existing theoretical frameworks. the practical implications are also provided for global marketers.. approach-avoidance behaviours cross-national consumer research indonesia korean beauty products malaysia social media data structural topic modeling","style, content, and the success of ideas. from marketers and consumers to leaders and health officials, everyone wants to increase their communications' impact. but why are some communications more impactful? while some argue that content drives success, we suggest that style, or the way ideas are presented, plays an important role. to test style's importance, we examine it in a context where content should be paramount: academic research. while scientists often see writing as a disinterested way to communicate unobstructed truth, a multi-method investigation indicates that writing style shapes impact. to separate content from style, we focus on a unique class of words linked to style (i.e., function words such as “and,” “the,” and “on”) that are devoid of content. natural language processing of almost 30,000 articles from a range of disciplines finds that function words explain 4–11% of overall variance explained and 11–27% of language content's impact on citations. additional analyses examine particular style features that may shape success, and why, highlighting the role of writing simplicity, personal voice, and temporal perspective. experiments further indicate the causal impact of style. the results suggest ways to boost communication's impact and highlight the value of natural language processing for understanding the success of ideas.. automated textual analysis language linguistic style natural language processing success of ideas","beyond stars: role of discrete emotions on online consumer review helpfulness. traditional word-of-mouth (wom) literature shows that emotions embedded in advertising appeals or referrals/reviews influence consumer buying journey. however, there is a paucity of research exploring the distribution and impact of emotional content in “online” consumer reviews (ocrs). hence, in this study, an attempt is made to examine the emotional content in ocrs and to study the influence of discrete positive and negative emotions on the perceived ocr helpfulness, by classifying them based on their valence and arousal. further, the impact of the inconsistency between the star rating of a review and the qualitative review-related factors (i.e. emotions and valence) is analyzed. this study uses natural language processing (nlp) and text-mining techniques to retrieve valence and emotions from 100,000 reviews from yelp.com and employs model testing methods to verify the hypotheses. the results reveal that: (1) both qualitative/latent (emotions and valence) and quantitative/manifest review message factors (star ratings, word count) are important in determining the ocr helpfulness; (2) the difference in arousal (high/low) and valence (positive/negative) has a differential impact on ocr helpfulness; (3) within high-arousal emotions, negative-valence emotions influence ocr helpfulness more than positive-valence emotions; and (4) consumers’ perceptions of ocr helpfulness depends on the consistency between the qualitative and quantitative content factors.. na","perception and gaze of diaspora: analysis of affective, cognitive, & cultural factors in tourism. this research focuses on exploring the cognitive and affective perception of hokkien ethnic chinese born abroad visiting their hometown, quanzhou, which is a unesco world heritage site. the study examines the interplay between the collective gaze, affective, and cognitive perception of diaspora visitors who visit quanzhou to reclaim their cultural identity and explore new opportunities. the analysis was conducted using a combination of fuzzy set qualitative comparative analysis and natural language processing techniques for sentiment analysis, based on 44 tourist interviews and 10 local key informant interviews. the results suggest that the primary image formed after the visit has a significant effect on the affective image and the affective attribute has a significant impact on the collective gaze and overall perceived image of diaspora visitors. these findings have important implications for destination image marketing and the sustainability of diaspora tourism.. collective gaze diaspora visitors quanzhou sentiment analysis tourist gaze","using sentiment analysis to understand public policy nicknames: obamacare and the affordable care act. in this study, we compared the social media net sentiment of one policy with two names. specifically, we analyzed obamacare and the affordable care act (aca) to understand how social media users engaged with each term on social media from march 2010 to march 2017. the net sentiment was measured with a sample of over 50 million micro-blogs, and the analysis was done using a combination of digital instruments and human validation. we found a significant difference between the social media engagement and sentiment of both terms, with the aca performing significantly better than obamacare, despite obamacare’s higher conversation volume. with the aca having an average of 26% less negative sentiment than obamacare, the findings of this study emphasize the need to be careful when attaching nicknames to public policy. the findings also have implications for policymakers and politicians.. affordable care act natural language processing obamacare public policy sentiment analysis social media","are social media and mobile applications threatening retail websites?. the use of mobile applications (ma) and social media (sm) platforms is reshaping the access to the internet. this study aims to analyse if consumers prefer to use a ma/sm platform or a retail website to search for a product or service and what are the reasons supporting their choice. data was collected through unstructured interviews applied to internet consumers. a total of 770 replies were returned with their perspective analysed through text mining to uncover hidden patterns of knowledge. the outcome revealed that the vital aspect that makes consumers prefer a retail website is due to service quality. the choice for ma/sm platforms is its system quality in the act of searching for a product or service online. the demographic analysis exposed that younger generations prefer ma/sm, suggesting a different future for retail websites.. consumer behaviour mobile application retail website social media text mining","how emotions impact the interactive value formation process during problematic social media interactions. purpose: previous research has studied interactive value formation (ivf) using resource- or practice-based approaches but has neglected the role of emotions. this article aims to show how emotions are correlated in problematic social media interactions and explore their role in ivf. design/methodology/approach: by combining a text mining algorithm, nonparametric spearman's rho and thematic qualitative analysis in an explanatory sequential mixed-method design, the authors (1) categorize customers' comments as positive, neutral or negative; (2) pinpoint peaks of negative comments; (3) classify problematic interactions as detrimental, contradictory or conflictual; (4) identify customers' main positive (joy, trust and surprise) and negative emotions (anger, dissatisfaction, disgust, fear and sadness) and (5) correlate these emotions. findings: despite several problematic social interactions, the same pattern of emotions appears but with different intensities. additionally, value co-creation, value no-creation and value co-destruction co-occur in a context of problematic social interactions (peak of negative comments). originality/value: this study provides new insights into the effect of customers' emotions during ivf by studying the links between positive and negative emotions and their effects on different sorts of problematic social interactions.. emotions interactive value formation social media value co-creation value co-destruction value no-creation","is this advertising or not, and do i care? perceptions of and opinions regarding hybrid forms of content. as media users are trying to avoid traditional forms of advertising like display ads, companies apply content marketing techniques such as native advertising, sponsored content and company-owned media to reach their audiences. such hybrid forms of content lead to a blurring of boundaries between editorial content and advertising and are not as easily recognized as promotional. this qualitative study examines consumers’ perspectives and reactions to different types of content marketing products by taking paid and owned media into account. data from 50 qualitative interviews with media users were analysed by means of thematic qualitative text analysis. the results indicate that consumers often perceive hybrid content as a mix of information and advertising. advertising recognition is most often based on content-driven characteristics of the message, such as mentioning a product and less so on the advertising disclosure label. this study derives four types of content marketing consumers: neutrals, enthusiasts, contemplators and critics. it contributes to the literature by strengthening the understanding of consumers’ perceptions of different types of hybrid content.. advertising content marketing hybrid content media literacy persuasion knowledge","exploring the factors influencing consumer engagement behavior regarding short-form video advertising: a big data perspective. short-form video has attracted users’ attention and been widely adopted for entertainment. recently, short-form video has also been used for advertising. however, how short-form video for advertisement influences consumer engagement behavior remains unclear. this study aims to explore key features of short-form video advertisements that influence consumer engagement behavior. through analyzing data obtained from social media platform tiktok, we discovered that four key features of short-form video—performance expectancy, entertainment, tie strength, and sales approach—are significantly related to consumer engagement behavior. in addition, the results showed that product type moderated the relationship of these effects on consumer engagement behavior. this study is one of the first to investigate the influence of short-form video advertisement features on consumer engagement behavior; thus, it contributes to the social media advertisement literature. it extends consumer engagement behavior research by applying a combination of uses and gratifications theory and signal theory. it also highlights the significance of product type in advertising literature. the use of big data and text analysis contributes from a methodological perspective to social media research. this study also provides practical and managerial implications for sellers and marketers on how to attract consumers to engage in videos and how to make data-driven decisions.. consumer engagement behavior product-type short-form video advertisements text analysis tiktok","designing marketing content for social commerce to drive consumer purchase behaviors: a perspective from speech act theory. with the emergence of content-driven social commerce, designing marketing content that better stimulates consumer purchase behaviors has become increasingly essential. however, it remains unclear what and how linguistic features of marketing content in emerging social commerce influence consumer purchase behaviors. drawing on speech act theory, this paper proposes a multi-level research model to conceptualize the linguistic features of content from the aspects of word usage (micro level), within-content argument development (macro level), and between-content linguistic mimicry (meta level), and investigate their impact on consumer purchase behaviors. with a unique dataset that includes 44,256 textual posts from jd wechat shopping circle (a content-driven social commerce platform), this paper combines text mining methods with a series of regression analyses to test the research model. the empirical analyses find that the number of customers who make a purchase increases 1) at the micro-level due to self-referencing and detailing, 2) at the macro-level due to argument structuring, and 3) at the meta-level due to linguistic style matching, while linguistic content matching negatively affects the number of customers. these findings reveal how content creators strategically use language to design marketing content that encourages consumer purchase behaviors in emerging social commerce. this study has important theoretical contributions and practical implications.. consumer purchase behavior content marketing social commerce speech act theory","exploring customer concerns on service quality under the covid-19 crisis: a social media analytics study from the retail industry. the covid-19 pandemic has triggered a set of government policies and supermarket regulations, which affects customers' grocery shopping behaviours. however, the specific impact of covid-19 on retailers at the customer end has not yet been addressed. using text-mining techniques (i.e., sentiment analysis, topic modelling) and time series analysis, we analyse 161,921 tweets from leading uk supermarkets during the first covid-19 lockdown. the results show the causes of sentiment change in each time series and how customer perception changes according to supermarkets’ response actions. drawing on the social media crisis communication framework and situational crisis communication theory, this study investigates whether responding to a crisis helps retail managers better understand their customers. the results uncover that customers experiencing certain social media interactions may evaluate attributes differently, resulting in varying levels of customer information collection, and grocery companies could benefit from engaging in social media crisis communication with customers. as new variants of covid-19 keep appearing, emerging managerial problems put businesses at risk for the next crisis. based on the results of text-mining analysis of consumer perceptions, this study identifies emerging topics in the uk grocery sector in the context of covid-19 crisis communication and develop the sub-dimensions of service quality assessment into four categories: physical aspects, reliability, personal interaction, and policies. this study reveals how supermarkets could use social media data to better analyse customer behaviour during a pandemic and sustain competitiveness by upgrading their crisis strategies and service provision. it also sheds light on how future researchers can leverage the power of social media data with multiple text-mining methodologies.. crisis management service quality social media crisis communication supermarket retailers text-mining","what consumer complaints should hoteliers prioritize? analysis of online reviews under different market segments. due to rising competition and resource constraints, hoteliers need to understand which consumer complaints are more important and severe to prioritize, an issue receiving less attention in existing online review-based complaint studies. this study introduced sentiment analysis to assess the extent of complaints; the complaint topic importance was also considered; by modeling these two indicators, complaint ipa was constructed to determine complaint severity, and thus assist with prioritization issues. the framework was applied to 99,560 online reviews to demonstrate its effectiveness. further, we analyzed the complaint characteristics in different market segments, including hotel types and consumer types (business vs. leisure). results show that business consumers have a higher complaint index; compared to midscale and luxury hotels, economy hotels’ consumers have more severe complaints about the service and room. this study extends consumer complaint research by proposing a method of complaint severity assessment and revealing complaint characteristics of different market segments.. complaint ipa complaint topic severity market segments online hotel reviews text mining","how regulatory focus–mode fit impacts variety-seeking. variety-seeking research has examined antecedents in terms of contextual factors and individual differences. however, it does not consider the interaction of individual difference factors such as regulatory focus (promotion vs. prevention) and regulatory mode (locomotion vs. assessment) to predict variety-seeking. drawing on regulatory fit theory, this study introduces a new kind of regulatory fit based on the interaction between regulatory focus and mode (i.e., regulatory focus–mode fit), thereby extending previous work examining fit based on either regulatory focus or regulatory mode in isolation. results from five studies, including field data from 10,547 music app consumers (text analysis), two preregistered studies, and two online experiments, show that regulatory focus–mode fit (vs. non-fit) decreases variety-seeking. engagement and attitude certainty serially mediate regulatory focus–mode fit effects. findings provide implications for consumer segmentation and message framing.. attitude certainty choice engagement regulatory fit regulatory focus regulatory mode variety-seeking","are marketing practice and academia in sync? a look at the msi priorities and marketing journal articles. accrediting agencies and industry are calling for more relevant, impactful research. biennially, the marketing science institute (msi) releases research priorities representing important problems in marketing from a practitioner perspective. this study identifies the topics most emphasized by msi and major marketing journals, assessing synergy of focus. employing text analytics, msi’s relative interest in various marketing topics is compared to the topical focus found in journals.while covering the same topics, findings suggest a difference in the allocation of focus on each topic between industry and prominent marketing journals. the ramifications of this disconnect include reduced relevance of academic research.. na","crossing the chasm between green corporate image and green corporate identity: a text mining, social media-based case study on automakers. as people become more aware of environmental problems, companies experience considerable pressure from customers to go green. such a pressure has consequently driven companies to project environmental friendliness as an important part of their identity and create a green image of themselves in customers' minds. from this situation arises the issue of separation between the identity that companies want to convey to the public and the image perceived by public. to address this issue, this study analyzes the world 10 top automakers in 2018 regarding their greenness. the tweets posted from the official twitter accounts of the study companies and those users' tweets posted about them are subjected to text mining. the results for 70% of companies show that there is a significant gap between their claims about their own green identity and how the public perceives such an image. co-occurrence analysis of words contained in the tweets clarify that the company's approach to address green issues always differs from that of an individual. finally, this study demonstrated an innovative application of the most commonly used text mining techniques to compare company and consumer perceptions. the findings can also serve as a basis for creating a dictionary of green issues.. automakers green identity green image social media text mining","internet of things (iot) in smart tourism: a literature review. purpose: although there has been a significant amount of research on smart tourism, the articles have not yet been combined into a thorough literature review that can examine research streams and the scope of future research. the purpose of this study is to examine the literature on the impact of deploying the internet of things (iot) in tourism sector development to attract more visitors using a text mining technique and citation based bibliometric analysis for the first time. design/methodology/approach: this study uses r programming to do a full-text analysis of 36 publications on iot in tourism and visualization of similarities viewer software to conduct a bibliometric citation analysis of 469 papers from the scopus database. aside from that, the documents were subjected to a longitudinal study using excel and word frequency using a trending topic using the r-tool. findings: results from the bibliometric study revealed the networks that exist in the literature of tourism management. with the use of log-likelihood, the findings from text mining identified nine theme models on the basis of relevancy, which is presented alongside an overview of the existing papers and a list of the primary authors with posterior probability using latent dirichlet allocation. originality/value: this study examines tourism literature in which iot plays a significant role. to the best of the authors’ knowledge, this study is the first to combine text mining with a bibliometric review. it significantly analyzes and discusses the impact of technology in the tourism sector development on attracting tourists while presenting the most important and frequently discussed topics and research in these writings. these findings provide researchers, tourism managers and technology professionals with a complete understanding of e-tourism and to provide smart devices to attract tourists.. bibliometric internet of things (iot) smart text mining tourism","how consumer opinions are affected by marketers: an empirical examination by deep learning approach. purpose: the natural language processing (nlp) technique enables machines to understand human language. this paper seeks to harness its power to recognise the interaction between marketers and consumers. hence, this study aims to enhance the conceptual and future development of deep learning in interactive marketing. design/methodology/approach: this study measures cognitive responses by using actual user postings. following a typical nlp analysis pipeline with tailored neural network (nn) models, it presents a stylised quantitative method to manifest the underlying relation. findings: based on consumer-generated content (cgc) and marketer-generated content (mgc) in the tourism industry, the results reveal that marketers and consumers interact in a subtle way. this study explores beyond simple positive and negative framing, and reveals that they do not resemble each other, not even in abstract form: cgc may complement mgc, but they are incongruent. it validates and supplements preceding findings in the framing effect literature and underpins some marketing wisdom in practice. research limitations/implications: this research inherits a fundamental limitation of nn model that result interpretability is low. also, the study may capture the partial phenomenon exhibited by active reviewers; lurker-consumers may behave differently. originality/value: this research is among the first to explore the interactive aspect of the framing effect with state-of-the-art deep learning language model. it reveals research opportunities by using nlp-extracted latent features to assess textual opinions. it also demonstrates the accessibility of deep learning tools. practitioners could use the described blueprint to foster their marketing initiatives.. big data framing effect language model market interaction nlp","offline context affects online reviews: the effect of post-consumption weather. this empirical study investigates whether unpleasant weather - a prominent aspect of a consumer's offline environment - influences online review provision and content. it uses a unique dataset that combines 12 years of data on hotel bookings and reviews, with weather condition information at a consumer's home and hotel address. the results show that bad weather increases review provision and reduces rating scores for past consumption experiences. moreover, 6.5% more reviews are written on rainy days and that these reviews are 0.1 points lower, accounting for 59% of the difference in average rating scores between four- and five-star hotels in our data. these results are consistent with a scenario in which bad weather (i) induces negative consumer mood, lowering rating scores, and (ii) makes consumers less time-constrained, which increases review provision. additional analyses with various automated sentiment measures for almost 300,000 review texts support this scenario: reviews on rainy days show a significant reduction in reviewer positivity and happiness, yet are longer and more detailed. this study demonstrates that offline context influences online reviews, and discusses how platforms and businesses should include contextual information in their review management approaches.. automated text analysis context effect mood online reviews user-generated content weather","does celebrity attachment influence brand attachment and brand loyalty in celebrity endorsement? a mixed methods study. the study tested a celebrity endorsement model to investigate the direct and indirect influences of celebrity attachment on brand loyalty across two culturally distinct countries: the united kingdom and turkiye. the survey was completed by 626 respondents and the data were analyzed using structural equation modeling. the findings show that celebrity attachment positively influences actual self-congruence, ideal self-congruence, brand quality, and brand attachment. brand attachment mediates the effect of celebrity attachment on brand loyalty. furthermore, the effectiveness of the celebrity endorsement process differs between british and turkish consumers. our research complements the meaning transfer model as it confirms that the celebrity endorsement process is influenced by two attachment objects: celebrities and brands. a follow-up study with text mining analysis of the consumer comments about the product reviews posted by social media influencers (smis) supported the validity of the celebrity endorsement model. practitioners should consider the consumer's emotional and cognitive bonding with celebrities, and brands to enhance the effectiveness of the celebrity endorsement process. the more consumers are attached to celebrities (or smis) and brands, the more loyal they become to the latter.. brand attachment brand loyalty brand quality celebrity attachment celebrity endorsement self-congruence social media influencers (smis)","an artificial intelligence analysis of climate-change influencers' marketing on twitter. designing marketing strategies with social media influencers are becoming increasingly relevant for setting political agendas. this study focuses on how two representative social influencers, greta thunberg and bill gates, engage in advising against climate change. the investigation uses 23,294 tweets posted by them or their followers citing them on climate change around the 25th edition of the united nations climate change conference. this study applies artificial intelligence and natural language processing to analyse the marketing mechanism of social influencers. we scrutinize the sentiment of the messages and then identify and analyse the different networks constructed around them to discern how pervasive a social influencer's message is. the results show that thunberg and gates follow different and unconnected strategies to deliver their messages to their followers.. artificial intelligence natural language processing sentiment analysis social influencers social networks twitter","mining arabic twitter conversations on health care: a new approach to analysing arabic language on social media. purpose: this paper aims to equip professionals and researchers in the fields of advertising, branding, public relations, marketing communications, social media analytics and marketing with a simple, effective and dynamic means of evaluating consumer behavioural sentiments and engagement through arabic language and script, in vivo. design/methodology/approach: using quantitative and qualitative situational linguistic analyses of classical arabic, found in quranic and religious texts scripts; modern standard arabic, which is commonly used in formal arabic channels; and dialectical arabic, which varies hugely from one arabic country to another: this study analyses rich marketing and consumer messages (tweets) – as a basis for developing an arabic language social media methodological tool. findings: despite the popularity of arabic language communication on social media platforms across geographies, currently, comprehensive language processing toolkits for analysing arabic social media conversations have limitations and require further development. furthermore, due to its unique morphology, developing text understanding capabilities specific to the arabic language poses challenges. practical implications: this study demonstrates the application and effectiveness of the proposed methodology on a random sample of twitter data from arabic-speaking regions. furthermore, as arabic is the language of islam, the study is of particular importance to islamic and muslim geographies, markets and marketing. social implications: the findings suggest that the proposed methodology has a wider potential beyond the data set and health-care sector analysed, and therefore, can be applied to further markets, social media platforms and consumer segments. originality/value: to remedy these gaps, this study presents a new methodology and analytical approach to investigating arabic language social media conversations, which brings together a multidisciplinary knowledge of technology, data science and marketing communications.. arabic digital marketing health care islamic markets middle east natural language processing social media analysis social media analytics the muslim consumer twitter","cooperator or supporter: how can cross-boundary macau–zhuhai metropolis promote regional tourism together?. purpose: this study initiated an investigation of how the macau–zhuhai tourism cooperated and discussed how macau and zhuhai could join hands to develop tourism in the region. the study demonstrated an approach for destination marketing organizations to explore online tourist-generated content and to understand tourists' perceptions of the destination image (di). design/methodology/approach: a total of 1,291,057 reviews (535,317 for macau and 755,740 for zhuhai) were collected, analyzed and examined to determine how the di s of macau and zhuhai changed during the period of 2015–2019 based on tourist-generated content on travel websites (tripadvisor, ctrip.com and qyer.com) through a text-mining approach. findings: the result revealed that macau and zhuhai were in a hybrid of competition and collaboration on tourism di s. first, macau and zhuhai competed in hotel and catering industry. macau was appealing to international tourists and provided high-end and prestigious offerings; while zhuhai was impressed by cost-effective accommodation and food. second, macau diversified industrial structure with diverse “tourism, leisure and recreation” and “culture, history and art” more than zhuhai did. meanwhile, macau should balance the different demands of international and chinese tourists. third, complementary potentials were found in natural resources, urbanization technology and tourism innovation and related projects. practical implications: the research provides valuable insights for policymakers and industrial managers on their endeavors to develop dis. policymakers should be able to develop supportive mechanisms and tourism facilitators to promote industrial collaboration and mutual dis. managers could refer to the components in the changing dis and identify the developmental gaps and cooperation potentials in their targeted areas. originality/value: the research fulfills the gap in regional tourism studies on macau, in which the evaluation on synergetic influence and neighbor effect from zhuhai has been underexplored. facilitated by up-to-date data mining techniques, the study contributes to both di and coopetition literature in tourism marketing; and this should inspire further studies on the antecedences of di changes, resolutions to the competing interests and dis of different stakeholders in different forms of strategic cooperation in regional tourism. the employment of dis is an explicit demonstration of tourists' immersion and values attached to the destination, providing effective cues on the status of coopetition.. destination image macau – zhuhai metropolis online reviews regional integration text mining tourist-generated content (tgc)","factors influencing tourists’ nightlife experience in belgrade. purpose: this study aims to understand the impact that the experience environment has on the nightlife experience, as well as to identify the factors from the nightlifescape that most influence the tourists’ experience in belgrade. additionally, this study seeks to discover whether these factors changed after the outbreak of the covid-19 pandemic. design/methodology/approach: to achieve the study’s objectives, 679 tourist reviews were collected from one of the most popular travel platforms, tripadvisor, and analyzed using rapidminer, the popular software for data/text mining. findings: the perception of the physical aspects of the experience environment, the presence of other tourists and the feeling of acceptance are identified as the key factors that influence tourists’ nightlife experience. this study also found that certain factors from the social and sensory environment, such as staff, the presence of other people, the atmosphere and music, had a positive impact on the tourist experience and their intention to recommend the nightlife experience in belgrade. moreover, it was discovered that the covid-19 pandemic did not provoke changes in the main factors influencing tourists’ nightlife experience. originality/value: the perception of tourists about belgrade during the night contributes to the growing body of tourism literature on destination image. focusing solely on the perception of tourists about belgrade during the night, this study adds a temporal determinant to the destination image, which can be considered as a valuable add on to the current knowledge in the field.. communitas experience night-time industry nightlife tourism nightlifescape belgrade","text-mining 10-k (annual) reports: a guide for b2b marketing research. advances in the availability and sophistication of software to facilitate the analysis of secondary data have contributed toward the growth of textual analysis. 10-k reports are a particularly salient source of insight into an array of issues in accounting and finance research yet remain underutilized in marketing. therefore, the purpose of this article is to offer a rationale for such analysis and a method that can be applied in b2b marketing. we draw on a strong tradition of textual analysis in finance to outline a method of text mining 10-k reports. we then discuss the downloading of raw texts, parsing raw text files and linking 10-ks to various dependent measures. we provide links for downloading parsed 10-k files and suggest software for textual analysis. the framework offers b2b marketers a rich alternative to primary data and proprietary datasets. ongoing advances in ai-enabled nlp text analysis further increase the future value of the approach for b2b marketers.. 10-k reports marketing method marketing research secondary data analysis text mining","effect of personal branding stereotypes on user engagement on short-video platforms. the emergence of short-video platforms offers individuals an opportunity to build personal branding. this study selected two personal brandings on bilibili.com, li ziqi and huanong brothers, to explore the differences in the effect of personal branding stereotypes on user engagement on short-video platforms. we first identified the perception of users based on the stereotype content model, then adopted regression models to construct the relationship between personal branding stereotypes and user engagement. the results show that perceived warmth exerts a more significant influence on user emotional engagement, while perceived competence impacts more on user cognitive and behavioral engagement. moreover, we find the live comments’ sentiment moderates these effects. this research provided references for individuals and managers to benefit from personal brandings.. personal branding stereotypes text analysis user engagement","the effect of message features on donations in donation-based crowdfunding. crowdfunding platforms have emerged as a popular method to raise funds for both for-profit and non-profits. charities often use donation-based crowdfunding platforms to reach out to a wider audience for fundraising campaigns. key three performance metrics that affect the success of a donation campaign are social media spread of the campaign message, number of donors who decide to donate from seeing the message, and donation amount per donor. this research investigates the effectiveness of negatively framed messages on these three metrics. analysis of message content of more than 3800 charities on gofundme.com via text mining was conducted to form a measure of the negativity of the appeals. also for each charity, the three performance metrics were collected. results showed that negatively framed messages led to a greater number of donors and greater sharing of the message on social media, but smaller donation amounts per donor. implication for charity organizations is that negatively framed messages will be more effective if the goal is to obtain a broad base of support, while positively framed messages will be more effective if the objective is to increase the average donation amount.. na","an overview and empirical comparison of natural language processing (nlp) models and an introduction to and empirical application of autoencoder models in marketing. with artificial intelligence permeating conversations and marketing interactions through digital technologies and media, machine learning models, in particular, natural language processing (nlp) models, have surged in popularity for analyzing unstructured data in marketing. yet, we do not fully understand which nlp models are appropriate for which marketing applications and what insights can be best derived from them. we review different nlp models and their applications in marketing. we layout the advantages and disadvantages of these models and highlight the conditions under which different models are appropriate in the marketing context. we introduce the latest neural autoencoder nlp models, demonstrate these models to analyze new product announcements and news articles, and provide an empirical comparison of the different autoencoder models along with the statistical nlp models. we discuss the insights from the comparison and offer guidelines for researchers. we outline future extensions of nlp models in marketing.. artificial intelligence autoencoder machine learning marketing natural language processing (nlp) text analysis text mining topic modeling unstructured data","how to identify product defects and segment consumer groups on an online auto forum. china's automotive industry has entered a stage of great growth. owing to the widespread and rapid information dissemination of online automotive forums, they have become an important source for manufacturers to collect product defect information and understand consumer preferences. the purpose of this study is to investigate car defect information and consumer preferences from online automotive forums and segment consumers based on consumer preferences, which holds great promise as a means for companies to improve product design and consumer relationship management. based on text data of china's top-ranked pacific auto network, we applied the lda model, sentiment analysis, text classification, and cluster analysis methods to determine the influencing factors of automobile consumers’ purchasing behavior, identified automobile product defects, and subdivided consumer groups. the results show that the factors affecting consumers’ car buying behavior are low fuel consumption, affordable price, superior performance, fashionable appearance, and comfortable use. car product defects are mainly reflected in the three aspects of the car's performance, comfort, and configuration; there are six kinds of consumer groups: price seekers, fuel consumption seekers, performance seekers, comfort seekers, cost performance seekers, and appearance seekers. of these, cost performance seekers make up the largest proportion, and they constitute the largest group of intention purchasers. these findings can help enterprises develop targeted product improvement programs, but also provide a decision-making basis for enterprises to achieve precision marketing.. auto forum consumer groups online review product defects text mining","attribute embedding: learning hierarchical representations of product attributes from consumer reviews. sales, product design, and engineering teams benefit immensely from better understanding customer perspectives. how do customers combine a product's technical specifications (i.e., engineered attributes) to form abstract product benefits (i.e., meta-attributes)? to address this question, the authors use machine learning and natural language processing to develop a methodological framework that extracts a hierarchy of product attributes based on contextual information of how attributes are expressed in consumer reviews. the attribute hierarchy reveals linkages between engineered attributes and meta-attributes within a product category, enabling flexible sentiment analysis that can identify how consumers receive meta-attributes, and which engineered attributes are main drivers. the framework can guide managers to monitor only portions of review content that are relevant to specific attributes of interest. moreover, managers can compare products within and between brands, where different names and attribute combinations are often associated with similar benefits. the authors apply the framework to the tablet computer category to generate dashboards and perceptual maps and provide validations of the attribute hierarchy using both primary and secondary data. resultant insights allow the exploration of substantive questions, such as how apple improved successive generations of ipads and why hewlett-packard and toshiba discontinued their tablet product lines.. attribute embedding attribute hierarchy machine learning meta-attribute natural language processing word2vec","same, same but different! new service development in the context of emerging markets: a review. purpose: the purpose of this paper is to systematically review and critically examine the international marketing and innovation management research on new service development (nsd) in the context of emerging markets (em). research on services in em, a heterogenous set of countries with an increasing contribution to global economic output, is sparse. this paper attempts to underscore the academic and managerial relevance of the field. design/methodology/approach: a systematic review of published empirical literature from peer-reviewed journals focusing on an 11-year period, 2010–2020, was undertaken. further, bibliometric and text mining analyses were conducted using vosviewer and leximancer software programmes. findings: this analysis of 36 journal articles reveals that nsd research is a dynamic field with an increasing number of quantitative, multi-country and multi-method studies encompassing a variety of geographical settings and industries. originality/value: doing justice to this vibrant field of research and its managerial importance, the authors create an overview of existing empirical studies to serve as a repository of knowledge on nsd for both academics and practitioners. further, the authors offer a thematic and temporal overview of the content of existing studies. drawing upon the abovementioned, the authors suggest some promising avenues for future research.. bibliometric analysis bottom-of-the-pyramid emerging markets international marketing new service development service launch subsistence marketplaces text mining","expression modalities: how speaking versus writing shapes word of mouth. consumers often communicate their attitudes and opinions with others, and such word of mouth has an important impact on what others think, buy, and do. but might the way consumers communicate their attitudes (i.e., through speaking or writing) shape the attitudes they express? and, as a result, the impact of what they share? while a great deal of research has begun to examine drivers of word of mouth, there has been less attention to how communication modality might shape sharing. six studies, conducted in the laboratory and field, demonstrate that compared to speaking, writing leads consumers to express less emotional attitudes. the effect is driven by deliberation. writing offers more time to deliberate about what to say, which reduces emotionality. the studies also demonstrate a downstream consequence of this effect: by shaping the attitudes expressed, the modality consumers communicate through can influence the impact of their communication. this work sheds light on word of mouth, effects of communication modality, and the role of language in communication.. automated text analysis communication modality emotion speaking word of mouth writing","does it pay off to communicate like your online community? evaluating the effect of content and linguistic style similarity on b2b brand engagement. business-to-business (b2b) social media efforts have largely focused on creating brand engagement through online content. we propose to analyse company social media texts (tweets) according to its two main dimensions, content and linguistic style, and to evaluate these in comparison to the overall content and style of the company's community of twitter followers. we combine 15 million tweets originating from 254,884 followers of ten company profiles and link these to 10,589 b2b company tweets. using advanced text analytics, we show that content similarity has positive effects on all engagement metrics, while linguistic style similarity mainly affects likes. readability acts as a moderator for these effects. we also find a negative interaction effect between the similarity metrics, such that style similarity is most useful if content similarity is low. this research is the first to integrate content and linguistic style similarity and contributes to the brand engagement literature by providing practical message composition guidelines, informed by the social media community.. b2b brand engagement content matching linguistic style matching social media text analytics","empathy and ego-drive in the b2b salesforce: impacts on job satisfaction. it has long been assumed that empathy and ego-drive are two fundamental characteristics of effective salespeople. taking a wholistic view of sales effectiveness to include job satisfaction in sales positions, this research assesses the relationship between salesperson empathy and ego-drive and sales job satisfaction. while evidence suggests that high empathy and high ego-drive are predictors of sales performance, this does not always translate to job satisfaction based on our findings. using the automated text analysis tool, linguistic inquiry and word count (liwc) to analyze company reviews written by b2b salespeople, our results show that b2b salespeople who are most satisfied with their jobs possess high levels of empathy but low levels of ego-drive.. b2b sales content-analysis ego-drive empathy job satisfaction linguistic inquiry and word count (liwc)","from mining to meaning: how b2b marketers can leverage text to inform strategy. today's marketers are increasingly faced with the need to collect and interpret data to aid firm strategic decision making. at the same time, there has been an explosion of text-based data and numerous advances in big data that enable marketers to mine the collection and aggregation of text. however, for many marketers there is a need to better understand how textual data can go beyond mere descriptive metrics to instead help solve real marketing problems. with this paper, we take a step in this direction. we first review key concepts and terms that are relevant to understanding how text analysis operates, as well as a new development in custom dictionary creation that expands the topics possible with text analysis. next, we develop the ftta grid, a new framework that enables text-derived metrics to inform actionable strategies for marketers. we present two real cases demonstrating how the ftta grid can be employed in action. finally, we discuss implications for both academics and marketing practitioners.. b2b marketing marketing insight marketing strategy text-analysis","text mining applied as business intelligence in the tourism industry. na. na","understanding digital consumer: a review, synthesis, and future research agenda. in the last 20 years, the proliferation of digital technologies has significantly impacted both businesses' operations and how consumers get information, engage, and buy. through the analysis of 74 marketing journals, this article identifies the current situation and possible gaps within digital consumer behavior research. by combining bibliometric analysis and machine learning algorithms, the authors have identified key topics in 5-year intervals and their evolution over time. the study results show that digitalization has overreached its original subsistence and has become an essential aspect of mainstream consumption patterns. the study reveals three domains within digital consumer behavior: (1) the use of classical consumption theories and their application to digital context; (2) transactional subjects within consumers’ digitalization; and (3) digital marketing's impact on the consumption of specific product groups. regression-based time series applied to 42 topics discovered by text mining to define possible future research directions. within 42 topics, significant growth is expected in mobile channel use, sustainable consumption, user-generated content, and social media engagement. in general, the results exhibit a shift from a transactional point of view to a more theoretical and strategic perspective. possible research gaps are expected to emerge from: (1) the impact of interactive and immersive technologies on consumption practices; (2) the possible outcomes of the pandemic on digital consumption patterns; (3) enhancing brand engagement through consumer-to-consumer interactions; (4) enhancing the understanding on issues like consumer collaboration, consumer trust, and service satisfaction.. digital consumers longitudinal study machine learning online consumers topic modeling","revisiting the recent history of consumer behavior in marketing journals: a topic modeling perspective. through the years, a great deal of research had been dedicated to understanding how consumers think, feel, and behave in the marketplace. from its early development to the present, consumer researchers have adopted different methods and followed different approaches to describe the consumption phenomena. by pursuing a probabilistic approach, the current study aims to provide in-depth insight into consumer behavior research's evolution and transformation between 1980 and 2020. the results exhibit a strong increase on subjects like consumer trust, self-identity, digital consumption, social media engagement, consumer culture theory, consumer motivation, and brand-consumer relationships. the citation analysis shows the most impactful articles and topics in consumer behavior research. the results indicate that articles on customer service satisfaction, experiential consumption, consumer trust, digital consumption, and social media engagement tend are heavily cited. and to understand each journals contribution an analysis based on citations, journal impact and topical diversity is also included. the study also includes the possible research directions for the future of consumer behavior.. consumer behavior consumer research consumption studies data visualization latent dirichlet allocation (lda) text mining topic modeling","maqāṣid al-sharī‘ah on islamic banking performance in indonesia: a knowledge discovery via text mining. purpose: in indonesia, subjective issues towards the fundamental of islamic banks (ibs) have been arising. for example, they are claimed to be not in line with the shari‘ah (islamic law). furthermore, the existing scholarly works have not much gained knowledge from the local ibs explaining their efforts in promoting maqasid al-shariah (objectives of islamic law). hence, because religiosity drives the fundamental establishment of ibs, this paper aims to explore the knowledge of how ibs in indonesia promote maqasid al-shariah via their published reports. design/methodology/approach: this paper performs text mining from 24 official reports of 5 ibs in indonesia published from 2015 to 2017. the sample contains 7,162 digital pages and approximately 3,021,618 words. traditional text mining via human intelligence is first performed to analyse for the numerical data required in the maqasid al-shariah index (msi) analysis. furthermore, a computer-driven text mining using the ‘text search’ feature of nvivo 12 plus is conducted to perform qualitative analysis. these approaches are made to gain relevant knowledge of how the sampled ibs promote maqasid al-shariah from their published reports. findings: the analysis using the msi explains a quantified maqasid al-shariah on the sample’s performance, which indictes the lowest and the highest performing banks. furthermore, a qualitative analysis supports the evidence from the quantitative analysis. it explains the authors’ coding process that results in 2 parent nodes and 20 child nodes, which contain 435 references coded from the sampled unstructured and bilingual texts. these nodes explain the information that associates with maqasid al-shariah from the ibs’ reports. these findings explain how maqasid al-shariah is measured mathematically and represent relevant knowledge of how maqasid al-shariah is informed practically via digital texts. research limitations/implications: a positivist generalisation is neither intended nor established in this study. practical implications: this paper gains relevant knowledge of how the sampled ibs in indonesia control and maintain the implementation of maqasid al-shariah from large textual data. such knowledge is practically important for ibs stakeholders in indonesia; moreover to help navigate the shari‘ah identity of bank syariah indonesia (bsi), the new ib established from the merger of 3 state-owned ibs, which are among the sample of this study. social implications: this paper provides evidence that might best challenge the subjective issue of ibs claiming that they are not in line with the shari‘ah, particularly in indonesia. originality/value: this paper is among the pioneers that discover knowledge of how ibs promote maqasid al-shariah in indonesia’s banking sector via a text mining approach.. banking performance indonesia islamic bank maqashid al-shariah text mining","looking through the glassdoor: the stories that b2b salespeople tell. a good understanding of b2b salespeople's employment experiences could better inform strategies to enhance their job satisfaction and reduce job turnover. this study analyzed more than 50 k salespersons' social media posts on the job review website glassdoor.com to identify the types of stories b2b salespeople tell about their jobs and employers. building on narrative theory, we used linguistic inquiry and word count (liwc) – a dictionary-based content analysis tool – to score the “w5” elements (who, what, when, where, and why) that constitute stories. next, we analyzed differences in the patterns of language used and uncovered four clusters representing corporate cultures that we characterize as cultures of work rewards, work-life balance, workplace malaise, and toxic interactions. insights from this study can support the management of b2b salespeople and serve as a model for firms considering new automated text analysis approaches to studying employee engagement on social media.. automated text analysis b2b salespeople corporate culture job satisfaction latent cluster analysis online job reviews sales storytelling","the market value of rhetorical signals in technology licensing contracts. automated text and content analysis in business-to-business (b2b) settings – such as technology licensing – can offer rich insights for both scholars and practitioners. drawing on conceptualizations of rhetorical signals and the principles on cooperative communication, this study uses technology licensing contracts to investigate how contractual terms and formulations lead to investor reactions (i.e., licensee's market value). using automated text analysis, we demonstrate how monitoring and enforcement emphasis can be usefully retrieved from contractual text. substantially we show that greater monitoring (enforcement) emphasis increases (decreases) the licensees' market value. greater concreteness reverses these effects of greater monitoring (enforcement) emphasis on the licensee's market value. combined, the three-way effect of monitoring emphasis, enforcement emphasis, and concreteness generates the greatest increase in the licensee's market value. these insights provide guidance for licensees on how best to formulate their licensing contracts. we show how to conduct automated text analyses to advance research in the area.. automated text analysis contracts enforcement monitoring technology licensing","emerging research trends in marketing: a review of australasian marketing journal. this article reviews papers published in the australasian marketing journal (amj) from 2015 to 2022 (issue 2). about 276 papers were considered in our analysis. adopting text mining, we reveal the key terms associated with research published in amj over the last 6 years. we employ a topic modeling procedure to find 15 topics that have been featured in the journal and to depict the trends in topics over time. for example, in 2021 and 2022, there has been a big increase in “digital,” “artificial intelligence,” “sustainability,” “online engagement,” and “purchase intention” topics, in line with the amj’s desire to capture the current industry and academic trends. a citation analysis shows the growth of articles and citations of papers published in amj and reveals some of the most cited papers. it is demonstrated that methodological articles are more likely to receive a high number of citations. we conclude by suggesting emerging topics and future research directions.. australasian marketing journal australian and new zealand marketing academy emerging marketing research trend","what are airbnb hosts advertising? a longitudinal essay in lisbon. purpose: considering the importance of the content created by the host for airbnb consumers while making purchasing decisions, this study aims to analyze how the airbnb hosts promote their properties by revealing the predominant attributes considered by hosts when advertising them. design/methodology/approach: the unstructured textual content of online airbnb accommodations advertisements (property descriptions) is analyzed through a longitudinal text mining approach. this study defines a pipeline based on a topic modeling approach that allows not only to identity the most prevalent text attributes but also its distribution through time. findings: this research identifies and characterizes the attributes most advertised over time, on about 30,000 accommodations posted monthly over two years, between 2018 and 2020. five main topics were identified in the data reflecting only pull motivations. noteworthy is the slight changes in properties’ descriptions topics along the two years, suggesting that “service” is increasingly being perceived by hosts as an important attribute of airbnb guest experience. originality/value: through a text analysis, this study provides an insight into peer-to-peer accommodation on the key attributes that hosts consider in the description of their properties to leverage the attractiveness of airbnb. in the light of existing research, which has predominantly focused on the trustworthiness and attractiveness of the airbnb advertisement, this research differentiates by analyzing the main attributes in text over time. given the airbnb’s changes since its inception, a longitudinal view is relevant to clarify how hosts advertise their properties and how it evolves in the light of these changes.. airbnb hosts advertisements push and pull factors sharing economy topic modeling","know your guests’ preferences before they arrive at your hotel: evidence from tripadvisor. purpose: toward achieving a better guest experience, the current study aims to use the word frequency comparison technique to evaluate the types of attributes and services that are used most frequently in guests’ five- and one-star reviews on tripadvisor. the working-paper also aims to investigate the differences between reviews written by men and women. design/methodology/approach: a combined sentiment and text analysis was applied to 329,849 uk hotel reviews from uk tripadvisor to identify factors that influence customer satisfaction, including those with gender differences. findings: the present findings reveal important differences between the male- and female-produced terms. the results show that female travelers pay more attention to the hotel’s core products and their comfort compared to male travelers. in terms of food and beverage, men’s comments tended to focus on pubs, beer and certain types of food. in contrast, women’s comments were more likely to be related to healthy eating, such as homemade, vegan and vegetarian foods, as well as fruits and healthy breakfasts. women also pay more attention to the soft skills of staff such as friendliness, helpfulness and welcoming messages. practical implications: while core attributes of a hotel stay remain crucial for all guests, disparities exist between the language men and women use to describe them. for core products, women pay more attention to the room’s cleanliness, comfort and features such as bed, pillow, blanket, towel, toiletries and decoration, whereas men pay more attention to the layout, size and type of room. hotels may use gender as a segmentation variable and use these findings in their marketing campaigns. originality/value: this is one of the first studies offering insights into the differences between the male and female reactions to and preferences for hotel services at a national level. following a novel method, this study has listed and ranked attributes and differentiated them based on gender.. gender guest experience hotel industry online reviews tripadvisor","do sensory reviews make more sense? the mediation of objective perception in online review helpfulness. purpose: do consumers rate reviews describing other consumers' sensory experience of a product (touch, smell, sight, hear and taste) as helpful or do they rate reviews describing more practical properties (product performance and characteristics/features) as more helpful? what is the effect of review helpfulness on purchase intention? furthermore, why do consumers perceive sensory and non-sensory reviews differently? this study answers these questions. design/methodology/approach: the authors analyze 447,792 amazon reviews and perform a topic modeling analysis to extract the main topics that consumers express in their reviews. then, the topics were used as regressors to predict the number of consumers who found the review helpful. finally, a lab experiment was conducted to replicate the results in a more controlled environment to test the serial mediation effect. findings: contrary to the overwhelming evidence supporting the positive effects of sensory elicitation in marketing, this study shows that sensory reviews are less likely to be helpful than non-sensory reviews. moreover, a key reason why sensory reviews are less effective is that they decrease the objective perception of the review, a less objective review then decreases the level of helpfulness, which decreases purchase intention. originality/value: this study contributes to the interactive marketing field by investigating customer behavior and interactivity in online shopping sites and to the sensory marketing literature by identifying a boundary condition, the authors’ data suggest that sensory elicitations might not be processed positively by consumers when they are not directly experienced, but instead communicated by another consumer. moreover, this study indicates how companies can encourage consumers to share more effective and helpful reviews.. data analytics electronic commerce online consumer behavior online marketing online shopping text mining","mining the text of online consumer reviews to analyze brand image and brand positioning. the growth of the internet has led to massive availability of online consumer reviews. so far, papers studying online reviews have mainly analysed how non-textual features, such as ratings and volume, influence different types of consumer behavior, such as information adoption decisions or product choices. however, little attention has been paid to examining the textual aspects of online reviews in order to study brand image and brand positioning. the text analysis of online reviews inevitably raises the concept of “text mining”; that is, the process of extracting useful and meaningful information from unstructured text. this research proposes an unified, structured and easy-to-implement procedure for the text analysis of online reviews with the ultimate goal of studying brand image and brand positioning. the text mining analysis is based on a lexicon-based approach, the linguistic inquiry and word count (pennebaker et al., 2007), which provides the researcher with insights into emotional and psychological brand associations.. brand positioning ewom online reviews text mining","communication in the gig economy: buying and selling in online freelance marketplaces. the proliferating gig economy relies on online freelance marketplaces, which support relatively anonymous interactions through text-based messages. informational asymmetries thus arise that can lead to exchange uncertainties between buyers and freelancers. conventional marketing thought recommends reducing such uncertainty. however, uncertainty reduction and uncertainty management theories indicate that buyers and freelancers might benefit more from balancing—rather than reducing—uncertainty, such as by strategically adhering to or deviating from common communication principles. with dyadic analyses of calls for bids and bids from a leading online freelance marketplace, this study reveals that buyers attract more bids from freelancers when they provide moderate degrees of task information and concreteness, avoid sharing personal information, and limit the affective intensity of their communication. freelancers’ bid success and price premiums increase when they mimic the degree of task information and affective intensity exhibited by buyers. however, mimicking a lack of personal information and concreteness reduces freelancers’ success, so freelancers should always be more concrete and offer more personal information than buyers. these contingent perspectives offer insights into buyer–seller communication in two-sided online marketplaces. they clarify that despite, or sometimes due to, communication uncertainty, both sides can achieve success in the online gig economy.. business-to-business exchange gig economy multisided platforms online freelance marketplaces text analysis uncertainty management","masstige strategies on social media: the influence on sentiments and attitude toward the brand. this paper explores whether mass prestige (masstige) campaigns, providing luxury and exclusivity for the masses, influence sentiments and attitude toward the brand among fashion customers and followers on social media, namely facebook and instagram. two brands are selected within the fashion sector: a luxury fashion brand—gucci—and a mass-market fashion brand—h&m. this research grounds its methodology on a netnography, collecting a total of 336,504 comments. the comments are examined through sentiment analysis based on a text-mining technique. the findings reveal that fashion brands can promote masstige strategies, sustaining attitude toward the brand, widening their range of offers, and taking the chance on these types of campaigns without negatively affecting customers’ and followers’ sentiments on social media platforms. the use of comments to measure these topics empirically is a novelty of this research.. attitude toward the brand facebook gucci h&m instagram mass prestige strategies masstige","using ai predicted personality to enhance advertising effectiveness. purpose: the purpose of this study is twofold: first to demonstrate the application of an algorithm using contextual data to ascertain consumer personality traits; and second to explore the factors impacting the relationship between personality traits and advertisement persuasiveness. design/methodology/approach: a mixed-method approach that comprises two distinct yet complementary studies. the first uses quantitative methods and is based on a sample of 35,264 retail banking customers. study 2 explores the findings that emerge from study 1 using qualitative methods. findings: this paper finds that matching consumer personality with congruent advertising messages can lead to more effective consumer persuasion for most personality types. for consumers who exhibit neurotic personality traits, ameliorating perceived risks during purchasing and providing cues for social acceptance and goal attainment are important factors for advertising effectiveness. these factors also had a positive impact on the purchasing behaviour of extroverted consumers. research limitations/implications: this research focusses on understanding purchasing behaviour based on the most dominant personality trait. however, people are likely to exhibit a combination of most or even all of the big five personality traits. practical implications: building on advances in natural language processing, enabling the identification of personality from language, this study demonstrates the possibility of influencing consumer behaviour by matching machine inferred personality to congruent persuasive advertising. it is one of the few studies to use contextual instead of social media data to capture individual personality. such data serves to capture an authentic rather than contrived persona. further, the study identifies the factors that may moderate this relationship and thereby provides an explanation of why some personality traits exhibit differences in purchasing behaviour from those that are anticipated by existing theory. originality/value: although the idea that people are more likely to be responsive to advertising messages that are congruent with their personality type has already been successfully applied by advertising practitioners and documented by advertising scholars, this study extends existing research by identifying the factors that may moderate this relationship and thereby provides an explanation why some personality traits may exhibit differences in purchasing behaviour from those that are anticipated by existing theory.. advertising artificial intelligence machine learning personality personality traits","influencer-generated reference groups. this article explores the idea that consumer influencers can shape reference group meanings in social media. through a survey in which over 5,000 participants provided open-ended reference group associations for 25 major brands, the authors find that social media influencers can either strengthen or change brand reference group associations. specifically, the typicality of the influencer (relative to a brand's stereotypical consumer) can shape ideas about the perceived homogeneity of the brand's consumers, which ultimately influences the strength and tightness of brand associations. this research combines seminal theories regarding cultural and sociological influences on branding, concepts relating to stereotype change, and a multi-method approach to assess new digital flows of cultural meaning from consumer influencers to brands.. branding influencers natural language processing reference groups social media","attribute sentiment scoring with online text reviews: accounting for language structure and missing attributes. the authors address two significant challenges in using online text reviews to obtain fine-grained, attribute-level sentiment ratings. first, in contrast to methods that rely on word frequency, they develop a deep learning convolutional–long short-term memory hybrid model to account for language structure. the convolutional layer accounts for spatial structure (adjacent word groups or phrases), and long short-term memory accounts for the sequential structure of language (sentiment distributed and modified across nonadjacent phrases). second, they address the problem of missing attributes in text when constructing attribute sentiment scores, as reviewers write about only a subset of attributes and remain silent on others. they develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. using yelp restaurant review data, they show superior attribute sentiment scoring accuracy with their model. they identify three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. surprisingly, attribute mentions in reviews are driven by the need to inform and vent/praise rather than by attribute importance. the heterogeneous model-based imputation performs better than other common imputations and, importantly, leads to managerially significant corrections in restaurant attribute ratings. more broadly, the results suggest that social science research should pay more attention to reducing measurement error in variables constructed from text.. convolutional neural networks customer satisfaction deep learning endogeneity long-short term memory (lstm) missing data online reviews text mining","consumer communications and current events: a cross-cultural study of the change in consumer response to company social media posts due to the covid-19 pandemic. the covid-19 pandemic has changed the lives of consumers in virtually every nation. based upon the theory of psychological reactance and psychoevolutionary theory of emotion, we hypothesize how such lifestyle changes affect consumers perceiving and responding to companies' communications messages. the theories also suggest that consumers in different cultures may respond to covid-19 differently. to test our hypotheses, we implemented a python scraper to collect companies’ instagram posts pre- and during the covid-19 lockdown. a machine learning algorithm was applied on the collected post photos to automatically identify certain photo characteristics, such as indoor versus outdoor, and with a single person versus many people; a text mining and sentiment analysis was implemented on the collected post captions to identify the salient emotion each caption exhibited, such as joy and anticipation. after that, we conducted a regression discontinuity analysis of photo characteristics or caption emotion on number of likes or comments to identify consumers’ response change due to the covid-19 pandemic. the estimation results supported our hypotheses and suggested tactics that could improve consumer communications effectiveness in this changed time. viewing covid-19 as an example of a current event in the ever-changing world, this paper suggests that such events could impact consumer response and behavior, and that companies’ marketing and advertising strategies should be responsive to such events.. consumer communications covid-19 cross-cultural study machine learning regression discontinuity analysis sentiment analysis social media marketing","speaking the same language: the power of words in crowdfunding success and failure. as crowdfunding has emerged as a popular source of funding for new ventures, budding entrepreneurs often struggle to deliver a convincing pitch to attract backers. we adopt an n-gram language model to analyze narratives of over 21,000 film projects from kickstarter and find that the choice of words is critical to crowdfunding success. using penalized logistic regression, we identify the relative power of phrases to predict funding outcome, resulting in a dramatic reduction in error rate. consistent with the language expectancy theory, the linguistic analyses show that successful projects usually include words that reflect the credibility of project creators and meaningful social interactions, whereas failed projects exude negativism or uncertainty. while good word choices vary among movie genres, words of lower cognitive complexity dampen the chances of funding. these findings have broad implications for text analysis and natural language generation for persuasive marketing communications.. crowdfunding language expectancy theory (let) natural language processing (nlp) promotion pitch text mining","the language of brands in social media: using topic modeling on social media conversations to drive brand strategy. this article highlights how social media data and language analysis can help managers understand brand positioning and brand competitive spaces to enable them to make various strategic and tactical decisions about brands. the authors use the output of topic models at the brand level to evaluate similarities between brands and to identify potential cobrand partners. in addition to using average topic probabilities to assess brands’ relationships to each other, they incorporate a differential language analysis framework, which implements scientific inference with multi-test-corrected hypothesis testing, to evaluate positive and negative topic correlates of brand names. the authors highlight the various applications of these approaches in decision making for brand management, including the assessment of brand positioning and future cobranding partnerships, design of marketing communication, identification of new product introductions, and identification of potential negative brand associations that can pose a threat to a brand's image. moreover, they introduce a new metric, “temporal topic variability,” that can serve as an early warning of future changes in consumer preference. the authors evaluate social media analytic contributions against offline survey data. they demonstrate their approach with a sample of 193 brands, representing a broad set of categories, and discuss its implications.. brand engagement brand positioning brand strategy branding latent dirichlet allocation natural language processing social media topic modeling","shedding light on the dark side of firm lobbying: a customer perspective. firms spend a substantial amount on lobbying—devoting financial resources on teams of lobbyists to further their interests among regulatory stakeholders. previous research acknowledges that lobbying positively influences firm value, but no studies have examined the parallel effects for customers. building on the attention-based view (abv) of the firm, the authors examine these customer effects. findings reveal that lobbying negatively affects customer satisfaction such that the positive relationship between lobbying and firm value is mediated by losses to customer satisfaction. these findings suggest a dark side of lobbying and challenge current thinking. however, several customer-focused moderators attenuate the negative effect of lobbying on customer satisfaction, predicted by abv theory, including the chief executive officer’s background (marketing vs. other functional area) and the firm’s strategic use of resources (advertising spending, research-and-development spending, or lobbying for product market issues). these moderators ensure consistency between lobbying and customer priorities or direct firm attention toward customers even while firms continue to lobby. finally, the authors verify that lobbying reduces the firm’s customer focus by measuring this focus directly using text analysis of firm communications with shareholders. collectively, the research provides managerial implications for navigating both lobbying activities and customer priorities, and public policy implications for lobbying disclosure requirements.. attention-based view corporate political activity customer satisfaction lobbying regulation","does online chatter matter for consumer behaviour? a priming experiment on organic food. consumers are increasingly sharing their opinions on societal issues and products online. we studied the implications of such online word-of-mouth for consumer judgement and decision-making. the case used is organic food, which is the most successful among the currently emerging, sustainability-differentiated food product categories. we first analysed the online discussion on organic food by conducting a text-mining study of reader comments (n = 63,379) from the comments section of a major german online news outlet. topics therein are discussed with differing frequency, thereby indicating the salience of the various issues to online readers and consumers. one organic food topic of high salience (animal welfare) and one of low salience (biodiversity) were selected to investigate the behavioural relevance of salient online topics in a subsequent priming experiment (online survey of german consumers; n = 1,118). in particular, we tested whether the relative online salience of the two topics used as primes influenced the likelihood of choosing organic instead of conventional eggs and milk in a choice experiment and the acceptance of policies supporting organic farming. although ineffective for the choice of milk, the priming worked as hypothesized regarding the choice of eggs and policy acceptance. priming the topic with high online salience is more effective at promoting pro-organic behaviour than priming the topic with low online salience. priming effects also depended on prime strength, attitude strength and experience with buying organic food. we discuss how insights from text mining of online word-of-mouth can be employed to promote sustainable consumption behaviour.. associative network consumer choices organic food priming salience text mining","determining banking service attributes from online reviews: text mining and sentiment analysis. purpose: the current study employs text mining and sentiment analysis to identify core banking service attributes and customer sentiment in online user-generated reviews. additionally, the study explains customer satisfaction based on the identified predictors. design/methodology/approach: a total of 32,217 customer reviews were collected across 29 top banks on bankbazaar.com posted from 2014 to 2021. in total three conceptual models were developed and evaluated employing regression analysis. findings: the study revealed that all variables were found to be statistically significant and affect customer satisfaction in their respective models except the interest rate. research limitations/implications: the study is confined to the geographical representation of its subjects' i.e. indian customers. a cross-cultural and socioeconomic background analysis of banking customers in different countries may help to better generalize the findings. practical implications: the study makes essential theoretical and managerial contributions to the existing literature on services, particularly the banking sector. originality/value: this paper is unique in nature that focuses on banking customer satisfaction from online reviews and ratings using text mining and sentiment analysis.. customer satisfaction emotions online ratings online reviews sentiment analysis text mining","hype news diffusion and risk of misinformation: the oz effect in health care. consumers’ choices about health products are heavily influenced by public information, such as news articles, research articles, online customer reviews, online product discussion, and tv shows. dr. oz, a celebrity physician, often makes medical recommendations with limited or marginal scientific evidence. although reputable news agencies have traditionally acted as gatekeepers of reliable information, they face the intense pressure of “the eyeball game.” customer reviews, despite their authenticity, may come from deceived consumers. therefore, it remains unclear whether public information sources can correct the misleading health information. in the context of over-the-counter weight loss products, the authors carefully analyze the cascading of information post endorsement. the analysis of extensive textual content with deep-learning methods reveals that legitimate news outlets respond to dr. oz's endorsement by generating more news articles about the ingredient; on average, articles after the endorsement contain higher sentiment, so news agencies seem to amplify rather than rectify the misleading endorsement. the finding highlights a serious concern: the risk of hype news diffusion. research articles react too slowly to mitigate the problem, and online customer reviews and product discussions provide only marginal corrections. the findings underscore the importance of oversight to mitigate the risk of cascading hype news.. health care hype news misinformation natural language processing oz effect","ai in marketing, consumer research and psychology: a systematic literature review and research agenda. this study is the first to provide an integrated view on the body of knowledge of artificial intelligence (ai) published in the marketing, consumer research, and psychology literature. by leveraging a systematic literature review using a data-driven approach and quantitative methodology (including bibliographic coupling), this study provides an overview of the emerging intellectual structure of ai research in the three bodies of literature examined. we identified eight topical clusters: (1) memory and computational logic; (2) decision making and cognitive processes; (3) neural networks; (4) machine learning and linguistic analysis; (5) social media and text mining; (6) social media content analytics; (7) technology acceptance and adoption; and (8) big data and robots. furthermore, we identified a total of 412 theoretical lenses used in these studies with the most frequently used being: (1) the unified theory of acceptance and use of technology; (2) game theory; (3) theory of mind; (4) theory of planned behavior; (5) computational theories; (6) behavioral reasoning theory; (7) decision theories; and (8) evolutionary theory. finally, we propose a research agenda to advance the scholarly debate on ai in the three literatures studied with an emphasis on cross-fertilization of theories used across fields, and neglected research topics.. ai artificial intelligence big data and robots decision making and cognitive processes machine learning and linguistic analysis memory and computational logic neural networks social media and text mining social media content analytics systematic literature review technology acceptance and adoption","consumers experiencing vulnerability: a state of play in the literature. purpose: the purpose of this paper is to provide a state-of-the-art review of research on consumers experiencing vulnerability to describe the current situation of the consumers experiencing vulnerability literature and develop an up-to-date synthesised definition of consumers experiencing vulnerability. design/methodology/approach: this systematic review, guided by the prisma framework, takes a multi-disciplinary approach to identify 310 articles published between 2010 and 2019 examining consumers experiencing vulnerability. descriptive analysis of the data is undertaken in combination with a thematic and text mining approach using leximancer software. findings: a definition of consumers experiencing vulnerability is developed- “unique and subjective experiences where characteristics such as states, conditions and/or external factors lead to a consumer experiencing a sense of powerlessness in consumption settings”. the findings reveal consumers experiencing vulnerability have often been classified using a uni-dimensional approach (opposed to a multi-dimensional), focussing on one factor of vulnerability, the most prevalent of these being economic and age factors. a lack of research has examined consumers experiencing vulnerability based upon geographical remoteness, gender and sexual exploitation. originality/value: this paper is one of the first to examine consumers experiencing vulnerability using a systematic approach and text mining analysis to synthesise a large set of articles, which subsequently reduces the potential for researchers’ interpretative bias. further, it is the first to generate a data-driven definition of consumers experiencing vulnerability. it provides targeted recommendations to allow further scholarly, policy and practical contributions to this area.. consumers experiencing vulnerability social marketing systematic review transformative well-being","measuring environmental performance in business to business relationships: a bibliometric review. purpose: this study presents a bibliometric review of environmental performance in business to business relationships research. design/methodology/approach: we applied suitable keywords to retrieve relevant peer-reviewed articles from the web of science database between 1992 and 2019. the study uses bibliographic coupling as a tool to screen 358 relevant articles' titles, abstracts, keywords, frameworks and headings for analysis. for visualization analysis, the study applied the visualizing scientific landscapes viewer. findings: our review systematically reports about the evolution of environmental performance in business-to-business literature relationships. bibliometric procedures reveal prominent authors and publication outlets (journals) as well as noteworthy thematic and theoretical contributions to the literature. practical implications: this study provides a comprehensive overview of environmental performance in business relationships and theoretical directions for further research. originality/value: the paper contributes to business literature by outlining emerging research themes and theoretical clusters on environmental performance for future research.. bibliometric review business to business relationships co-occurrence analysis environmental performance text mining","hidden online customer journey: how unseen activities affect media mix modelling and multichannel attribution. the purpose of this paper is to verify the impact of missing the earned media and category media in multichannel conversion attribution models on digital media budget allocation. the analysis is based on a very unique approach: 532 users who declared their will to purchase a selected product in the next 3–5 months agreed to install special add-ons on all their devices connected to the internet. these devices will register all the users’ activities throughout three months. all user activities on the path to purchase were extracted by means of text mining (url analysis) techniques. finally, 5,171 activities were found and assigned to particular media areas and media channels. the average user spends 20 per cent of their time in the paid media and owned media areas. however, from the point of view of the number of touchpoints, 29 per cent of the activities occur in these two areas. the obtained results clearly show how much of consumers’ activity in the decision-making process is beyond the control of marketers who, on the basis of this partial data, have to make daily decisions about allocating advertising budgets. the study compared the results of conversion attribution for the full funnel (paid media, owned media, earned media, category media) with the conversion attribution based only on paid media and owned media. the results indicate that not all attribution models lead to similar conclusions in both approaches.. budgeting decision making measures media planning multichannel attribution","analysing perceptions towards electric cars using text mining and sentiment analysis: a case study of the newly introduced togg in turkey. the electric car market is growing steadily around the world and, accordingly, has become an attractive research area. it is important to understand the consumer perspective on newly introduced electric cars, such as those of turkey’s automobile joint venture group inc. (togg). thus, the purpose of this study is to provide a better understanding of consumers’ perceptions related to the newly introduced togg, which may create a competitive advantage. social media is an abundant source of textual data that allows for very reliable analysis and understanding of consumer opinions. in this study, twitter comments on togg were collected and studied. text mining, sentiment analysis and topic model analysis were then conducted. the results show that togg is a popular product with the public: there are many more positive twitter comments related to togg than negative ones. the topics identified in social media are price expectancy, production facility, design and features. the most frequent topic for both positive and negative comments is price expectancy.. electric car sentiment analysis sustainable competitive advantage text mining topic model analysis","social media analytics and its applications in marketing. the rise of the internet and smartphones in the 21st century has created and developed social media as an extremely effective means of communication in society. in life, business, sports, and politics, social media facilitates the democratization of ideas like never before. social media content gives consumers different information sources that they must decipher to discern its trustworthiness and influence in their own opinions. marketers must be savvy about using social media in their attempts to persuade consumers and build brand equity. as social media has permeated our everyday lives, scholars in various disciplines are actively conducting research into this aspect regarding our way of life. in this scholarly endeavor, marketing has taken a leading role in this research endeavor as a discipline involving human communications and idea persuasion. thus, rather than considering social media broadly across multiple disciplines, in this monograph, we concentrate on social media analytics in marketing. this monograph comprises the following four sections: • first, we provide an overview of social media and social media analytics (sma). while much has already been said about social media generally, relatively less has been said about social media analytics. thus, much of our focus is on sma in terms of contributing to the current understanding of sma in the field. • second, we concentrate on social media analytics in marketing. we discuss practical industry perspectives and examples, as well as recent marketing research by academics. notably, we show how analytics may be used to address concerns about social media privacy and help detect fake reviews. • third, we summarize common tools for social media analytics in marketing. these methods can be complex, but they must be mastered for sound sma practice. they encompass big data, artificial intelligence, machine learning, deep learning, text analytics, and visual analytics. • fourth, we discuss trends and a future research agenda. we also discuss how sma might be better integrated into higher education.. na","making sense of smart tourism destinations: a qualitative text analysis from sweden. the concept of smart tourism destinations has gained increased attention in research literature and among tourism stakeholders. however, the concept is still considered in need of more in-depth explanations for understanding and making sense of the notion. the contribution of the concept of smart tourism destinations to managerially relevant knowledge is particularly difficult to assess due to its complexity. therefore, a qualitative text analysis of stakeholder interviews is proposed and conducted using the gabek® methodology to identify recurring themes in the stakeholders' understanding of smart tourism destinations. the gabek® methodology aims to represent the texts of open interviews as networks of interrelated keywords to make sense of a complex phenomenon. this study explores how destination stakeholders from two different swedish destinations make sense of the concept of smart tourism destinations. study findings show that, from the destination stakeholders' point of view, there is a need to counterbalance the currently dominant focus on technology with softer though more existential values to construct a sustainable path of destination development. as for the theoretical contribution, this study clarifies properties of smart tourism destinations and stakeholders’ sense-making of the concept of smart tourism destination.. gabek® qualitative text analysis sense-making smart tourism destinations","an empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews. the amount of digital text-based consumer review data has increased dramatically and there exist many machine learning approaches for automated text-based sentiment analysis. marketing researchers have employed various methods for analyzing text reviews but lack a comprehensive comparison of their performance to guide method selection in future applications. we focus on the fundamental relationship between a consumer's overall empirical evaluation, and the text-based explanation of their evaluation. we study the empirical tradeoff between predictive and diagnostic abilities, in applying various methods to estimate this fundamental relationship. we incorporate methods previously employed in the marketing literature, and methods that are so far less common in the marketing literature. for generalizability, we analyze 25,241 products in nine product categories, and 260,489 reviews across five review platforms. we find that neural network-based machine learning methods, in particular pre-trained versions, offer the most accurate predictions, while topic models such as latent dirichlet allocation offer deeper diagnostics. however, neural network models are not suited for diagnostic purposes and topic models are ill equipped for making predictions. consequently, future selection of methods to process text reviews is likely to be based on analysts’ goals of prediction versus diagnostics.. automated text analysis machine learning natural language processing online reviews sentiment analysis user generated content","multi-criteria decision-making leveraged by text analytics and interviews with strategists. strategic decision-making in organisations is a complex process affected by preferences, experiences, perspectives, and knowledge, which, in most cases, are ambiguous, contradictory, and represented in unstructured data. this paper develops a methodological framework to address strategic decision-making processes from a multi-criteria perspective, assisted by text analytics and interviews. the framework comprises five stages and 12 steps, and is empirically tested in a decision scenario involving a strategic focus for future analytics initiatives in order to stimulate value generation from analytics. the proposed framework enables the discovery, validation, and prioritisation of strategic patterns from relevant interview data. among six decision alternatives discovered in the validation scenario, customer analytics was the strategic focus most relevant to future analytics initiatives. this article contributes to understanding and addressing complex decision-making processes and mixed research in organisations, through a multi-criteria perspective leveraged by a text-driven computational approach.. analytics customer analytics interviews multi-criteria analysis strategic decisions text analytics","the informational value of multi-attribute online consumer reviews: a text mining approach. in the digital age, customers use online reviews to minimize the risks associated with purchasing a product. major online retailers help customers choose the right product by exposing reviews that received many “helpful” votes at the top of the review section. given that reviews that have received the maximum helpfulness votes are considered more important in purchase decisions, understanding determinants of helpfulness votes offers clear benefits to online retailers and review platforms. this study focuses on the effect of review informativeness, which is measured by the number of attributes discussed in a review, and its interplay of review valence on customers' perception of review helpfulness. we applied a word-level bigram analysis to derive product attributes from review text and examined the influence of the number of attributes on the review's helpfulness votes. more importantly, we also suggested the moderating role of review valence. estimation results of the zero-inflated poisson models on 21,125 reviews across 14 wireless earbuds indicated that as more attributes are discussed in a review, the more the review can earn helpfulness votes from customers. furthermore, the positive association between the number of attributes and helpfulness was enhanced among negative reviews. this study contributes to customers' information processing literature and offers guidelines to online retailers in designing a better decision support system.. big data analysis bigram analysis multi-attribute reviews review helpfulness review informativeness","mining consumer minds: downstream consequences of host motivations for home-sharing platforms. this research sheds light on consumer motivations for participating in the sharing economy and examines downstream consequences of the uncovered motivations. we use text-mining techniques to extract airbnb hosts' motivations from their responses to the question \"why did you start hosting.\"we find that hosts are driven not only by the monetary motivation \"to earn cash\"but also by intrinsic motivations such as \"to share beauty\"and \"to meet people.\"using extensive transaction-level data, we find that hosts with intrinsic motivations post more property photos and write longer property descriptions, demonstrating greater engagement with the platform. consequently, these hosts receive higher guest satisfaction ratings. compared to hosts who want to earn cash, hosts motivated to meet people are more likely to keep hosting and to stay active on the platform, and hosts motivated to share beauty charge higher prices. as a result, these intrinsically motivated hosts have a higher customer lifetime value compared to those with a monetary motivation. we employ a multimethod approach including text mining, bayesian latent attrition models, and lab experiments to derive these insights. our research provides an easy-to-implement approach to uncovering consumer motivations in practice and highlights the consequential role of these motivations for firms.. airbnb customer engagement customer lifetime value motivation sharing economy text mining","the “idea advantage”: how content sharing strategies impact engagement in online learning platforms. in recent years, online learning platforms (e.g., coursera, edx) have experienced massive growth and have reached nearly 200 million learners. although their reach is quite large, the impact of these platforms is constrained by a low level of learner engagement. in traditional face-to-face classrooms, educators aim to engage learners by asking them to participate in class discussions and share information about their identity and ideas. however, the effectiveness of these strategies in online learning platforms is uncertain. the authors examine this issue by assessing the impact of two different types of content sharing on learner engagement. the authors conduct a textual analysis of over 12,000 text postings during an 18-month period (study 1) and a field experiment among over 2,000 learners (study 2) in a popular coursera offering by a large u.s. university. the results indicate that asking learners to share ideas (vs. their identity) has a stronger effect on their video consumption and assessment completion. the authors explain this “idea advantage” by suggesting that learners who share ideas (vs. identity) exhibit a greater degree of elaboration. this idea advantage is strongest for learners from english-speaking countries and those new to online learning.. education engagement field experiment online platforms student retention and performance text mining topic modeling user-generated content","sustainability topic trends in the textile and apparel industry: a text mining-based magazine article analysis. purpose: the purpose of this study is to (1) identify the sustainable practices developed by the textile and apparel industry and (2) investigate the gaps and opportunities in the sustainability implementation process by quantitively analyzing the sustainability topics and the relevant topic trends. design/methodology/approach: this study employed text mining techniques. a total of 1,168 relevant magazine articles published from 2013 to 2020 were collected and then categorized according to their tones. in total, 36 topics were identified by reviewing the sustainability issues in the industry. the frequency of each topic mentioned in the articles and the correlation coefficients between topics' frequencies and published time were calculated. the results were used to examine if the three sustainability dimensions (environment, society, economy) were equally addressed and identify opportunities in the sustainability implementation process. findings: there were much fewer social and economic topics than environmental topics discussed in the articles. additionally, there were not enough practices developed to reduce microfiber pollution, improve consumers' knowledge of sustainability, offset the carbon footprint, build a transparent, sustainable supply chain and avoid animal cruelty. originality/value: there is a lack of research focusing on the whole supply chain and sustainability when investigating sustainable practices and topic trends. this study fills a part of the gap. the results can be used by industrialists to identify sustainable practice opportunities and better manage their sustainable supply chains. researchers can utilize the results to compare the topics in the industry with the topics studied in academia.. magazine articles supply chain sustainability text mining textile and apparel industry topic trends","structural review of relics tourism by text mining and machine learning. purpose: the objective of the paper is to find trends of research in relic tourism-related topics. specifically, this paper uncovers all published studies having latent issues with the keywords “relic tourism” from the web of science database. methods: a total of 109 published articles (2002-2021) were collected related to “relic tourism.” machine learning tools were applied. network analysis was used to highlight top researchers in this field, their citations, keyword clusters, and collaborative networks. text analysis and bidirectional encoder representation from transformer (bert) of artificial intelligence model were used to predict text or keyword-based topic reference in machine learning. results: all the papers are published basically on three primary keywords such as “relics,” “culture,” and “heritage.” secondary keywords like “protection” and “development” also attract researchers to research this topic. the co-author network is highly significant for diverse authors, and geographically researchers from five countries are collaborating more on this topic. implications: academically, future research can be predicated with dense keywords. journals can bring more special issues related to the topic as relic tourism still has some unexplored areas.. artificial intelligence machine learning relic tourism text analysis topic modelling","sustainability in luxury: insights from twitter activities. sustainability presents one of the most pressing challenges across different industries, especially for luxury brands. this study reveals the role of sustainability in luxury consumption by collecting and analyzing data from over half a million tweets with 762 sustainability luxury consumption hashtags. sustainability’s importance in online discussion is steadily increasing. using balance theory, we combined sentiment and text mining to decrypt and reveal online audience’s perceptions of sustainable luxury. the findings highlight what online public considers sustainable, the importance of these perceptions for specific sectors of the luxury industry, and which emotions sustainable luxury perceptions trigger. we reveal online audience’s sentiments in the form of opinions, evaluations, and emotions toward sustainable luxury. this research serves as a critical exploration of the much-needed alignment between luxury and sustainability. this work aids not only scholars but also luxury brand managers in their aim to comprehend and keep pace with the increasing importance of sustainability in the luxury sector.. experience luxury perceptions sentiment analysis sustainability text analytics","co-branding strategies in luxury fashion: the off-white case. increasing competition has induced brands to find ever more innovative ways to raise awareness. among the latter, co-branding has recently become pervasive in the luxury fashion sector. despite the growing interest in co-branding, no study to date has examined how luxury fashion brands can strategically combine different types of co-branding and the amount of media visibility that they can gain by means of these brand alliances. drawing on both case study analysis and automated text analysis, this paper on the longitudinal case of the luxury fashion brand off-white offers important managerial implication. it shows that, to create a successful co-branding portfolio, luxury brands should initially collaborate with renowned brands in the same target market, and only later with brands operating in other sectors. this study also reports empirical findings with which to understand what are the most suitable forms of co-branding to pursue to increase media visibility and consumers’ engagement in social media.. co-branding collaborations fashion luxury luxury brands","a review of social roles in green consumer behaviour. despite the broad interest and rapid increase in studying green consumer behaviour (gcb) from a social perspective, related articles have been presented in a piecemeal fashion and have lacked a review of social roles in gcb. additionally, the research focus may differ from the influence of digital technology. therefore, this study draws on a review of 439 gcb articles that focus on social roles and utilizes both quantitative and qualitative analyses to provide a general picture of theories, social factors, research topics and topic trends with a specific emphasis on the research differences between the traditional and digital contexts. the findings indicate that the gcb field has experienced exponential growth since 2014. subjective norms, social influence, culture, marketing influence and social media influence are the main drivers of gcb. eight research topics are identified, among which topics on the theory of planned behaviour (tpb) and the theory of consumption values (tcv) maintained high popularity. furthermore, the tpb is more prevalent in the traditional context in which scholars prefer to predict gcb using macrosocial factors, such as policy and culture. in comparison, signal theory and commitment-trust theory have received more attention in the digital context in which trust is vital. research interests in online platforms and green hotels have recently increased in the digital context. gaps and suggested directions for future studies are further identified.. bibliometric analysis content analysis green consumer behaviour social roles text mining traditional versus digital contexts","writing more compelling creative appeals: a deep learning-based approach. we present a deep learning algorithm to provide personalized feedback on creative appeals, written content intended to persuade readers to undertake some action. such appeals are widespread in marketing, including advertising copy, rfp responses, call center scripts, product descriptions, and many others. although marketing research has produced several tools to help managers glean insights from online word-of-mouth, less attention has been paid to creating tools to assist the innumerable marketers responsible for crafting effective marketing messages. our approach leverages the hierarchical structure of written works, associating words with sentences and sentences with documents, and the linguistic relationships developed therein. we score each sentence in an appeal by its expected contribution to success accounting for its substance and persuasive impact. the sentences with the lowest scores make the appeal less compelling and are the most effective points to focus a revision. the approach has proved effective in a randomized control trial, with subjects rating essays revised with the aid of algorithmic feedback as being 4.5% more likely to achieve their objec-tives. in addition to providing automated feedback to authors, we leverage the model’s output to derive substantive insights into what makes an appeal compelling.. creative appeals deep learning natural language processing recurrent neural networks","psycho-managerial text mining (pmtm): a framework for developing and validating psychological/managerial constructs from a theory/text-driven approach. this paper addresses two concerns in the construction and validation of psychological/managerial constructs from texts: (1) the procedure for studying content validity is mainly based on human tasks, subjectivity, limited to small data, and difficult to reproduce/replicate; (2) traditional latent variable representation (a merged list or random parcel of words) lacks theoretical rationality and does not guarantee the representativity of valuable qualities, perceptual entities, and actions of the construct. the paper proposes a framework—pmtm—for developing/validating measures of psychological/managerial constructs from texts (three stages and 16 steps) by incorporating linguistic, psychometrical, and computational resources. pmtm includes five new properties to guide content validity analysis automatically, and contains a new method for refining dictionaries, entitled “embedded voting,” which emulates expert judgments. in addition, the paper provides empirical evidence derived from the application of pmtm to the case of transformational leadership using data from organizational documents, interviews, essays, blog posts, surveys, and speeches by former us presidents. the framework can also be used as a comprehensive template with the potential to exploit customer/service texts from a theory/text-driven approach to overcome recent challenges stated by the marketing literature, including the lack of standardization and inconsistency in the definitions and measurement of marketing constructs.. automated dictionary creation constructs analysis text analytics text mining theory/text-driven approach transformational leadership","communication during pandemic: who should tweet about covid and how?. the covid-19 pandemic has devastated the health and wealth of individuals and economies. firms have lost businesses while people have lost their jobs and even lives because of the pandemic. hence, it is critical to understand whether and how firms should communicate with their consumers. the author analyzes social media tweets from 35 major car companies for 21 months during the pandemic, and detects different covid-tweeting effects between luxury and non-luxury brands. while firms often spend much effort providing covid-19 related information during pandemic, this paper finds that tweeting about covid-19 (relative to other content) generally hurts consumer engagement (i.e. decreases retweets). interestingly, this negative effect is moderated if tweets are posted by luxury firms (vs. non-luxury firms). last, if firms tweet about covid-19, they should focus more on stakeholders, such as consumers and employees in their tweets. the author generates implications for social media strategies during tremendous uncertainty and crisis periods.. consumer engagement covid-19 luxury brand marketing communication text analysis twitter","more than words can say: a multimodal approach to understanding meaning and sentiment in social media. the authors develop a multimodal social listening analysis (msla) approach as a framework for managers to understand how meaning is constructed in social media posts using both text and other media. the research adds to ai and text analysis approaches by considering the whole meaning of a post rather than an analysis of subsets of information in text and other media. the use of msla is validated across the social media platforms of facebook, twitter and instagram. the findings show that msla helps (i) reveal structures in what appear to be unstructured multimodal posts; (ii) identify all the sentiment items in a post; (iii) identify implicit meanings, such as irony, humour and sarcasm; and (iv) further identify emotions and judgements in multimodal communication. importantly, this paper explains how decisions and opinions are made online and how marketing strategies can be tailored towards meanings derived from multimodal communication in social media.. consumer sentiment inter-coder reliability multimodal social listening qualitative research social media social networking","content analysis of seafood e-commerce sites using a text mining approach: a case study of japan. matching the values of e-commerce sites and consumers is an effective way to expand online seafood sales. however, research that analyzed seafood e-commerce site data is scarce. this study explores these services to demonstrate how online seafood sales can be promoted. data were obtained from the text of japanese seafood e-commerce sites’ business introduction pages. morphological, frequency, and co-occurrence network analyses were performed. key aspects of the services were identified (safe and high-quality, direct delivery, fresh and delicious, and thoughtfulness); however, individual consumer needs are not currently reflected. further analyses on platform-type sites are required to expand seafood internet sales.. e-commerce marketing seafood text mining","correction to: marketing insights from text analysis (marketing letters, (2022), 10.1007/s11002-022-09635-6). in this article the title was incorrectly given as ’wisdom from words: marketing insights from text’ but should have been ’marketing insights from text analysis’. the original article has been corrected.. na","the effects of conversational agents’ emotion cues on their perceived responsiveness and consumers’ intention to act: an abstract. conversational agents allow products and services to be sold through real-time one-on-one conversations. however, consumers tend to engage with humans and resist conversational agents. further, conversation abandonment without leaving personal information (e.g., email) leaves marketers without the ability to re-engage with potential customers. drawing on the social information processing theory and affect-as-information model, this study investigates how emotion cues: a higher positive tone from the conversational agent and emojis influence (1) the agent’s responsiveness perception, and (2) consumers’ intention to disclose personally-identifying information (email) to the agent retailer. the research uses a computerized text analysis of the chat scenarios and a 2 (low vs higher emotional tone) × 2 (emoji use vs. no emoji use) between-subjects experimental design. this research focuses on the effect of emotional tone and emoji use on the conversational agent’s perceived responsiveness and consumers’ intention to disclose their personal information to the agent in a text-based conversational commerce context. when the conversational agent’s emotional tone was higher, it was perceived as being more responsive. however, the effect of emoji use on the perception of agents’ responsiveness was not significant. finally, the present research found that the effect of higher emotional tone, emoji use, and the interaction term was significant for consumers’ intention to disclose their email to the agent retailer. this study provides several managerial implications; although emoji use did not result in higher perceived responsiveness, emojis and higher emotional tone can be used to promote email disclosure to the agent retailers and thus enable marketers to collect valuable consumer data. further, an emotional tone can benefit a conversational website, particularly one in need of incorporating customers’ personal information to provide personalized products. the findings contribute to the growing research stream on integrating emotion cues into conversational agents and their impact on consumers’ perceptions and personal information disclosure.. conversational agents conversational selling emojis emotional tone information disclosure non-verbal cues perceived responsiveness personalization","applying phrase-level text analysis to measure brand-related information disclosure: an abstract. financial reporting and disclosure are important means for management to communicate firm performance and governance to external stakeholders. existing research provides limited insight into firms’ disclosure of information about their market-based assets such as brands, although brand is an important part of firms’ value proposition. our research intends to address this research gap through (1) a theory-based conceptualization of brand-related information (bri) and (2) an empirical analysis of bri disclosure in corporate reports, more specifically, in us firms’ annual reports on form 10-k. we incorporate automated text analysis to identify and measure bri disclosure in 10-k reports. automated text analysis allows us to process massive amounts of text efficiently and reliably. extant studies using automated text analysis are primarily rely on word-level analysis, yet most concepts entail multiword terms or phrases to convey meaning. we address this limitation of existing methods by applying phrase-level analysis to bri disclosure. first, we draw on the literature on customer-based brand equity and intellectual capital disclosure to develop the conceptualization of bri. building on this conceptualization, we explore the capacity of natural language processing to build a phrase-level bri coding dictionary. second, we use automated text analysis to construct a bri disclosure index that captures the bri disclosure level in firms’ 10-k reports. unlike prior studies that focus on word-level analysis for construct measurement, our method utilizes proximity search to match phrases in the bri coding dictionary with texts and incorporate a proximity-weighing mechanism to conduct a phrase-level analysis of bri disclosures. the contribution of this study is twofold. first, this study fills the research gap in the field of information disclosure concerning bri by conceptualizing a bri framework and providing evidence on whether and how firms disclose bri in financial reporting. second, our method broadens the possibility of utilizing automated text analysis in constructing research-specific coding dictionaries as well as conducting phrase-level text analysis. while current research has made some efforts to provide a structured process for phrase-level text analysis, there is limited guidance regarding the operationalization of phrase match with proximity search.. automated text analysis brand-related information information disclosure natural language processing phrase-level analysis","an artificial intelligence method for the analysis of marketing scientific literature: an abstract. we suggest a machine-based research literature reading method specific to the academic discipline of marketing, adopting artificial intelligence (ai) developments from the field of materials science. keeping up with research publications is untenable due to exponential growth. researchers have become much better at the generation of information than at its deployment. ai can help to simplify the use of such knowledge. in materials science, tshitoyan et al. (2019) have made steps in trying to achieve ‘a generalized approach to the mining of scientific literature’ using text mining and natural language processing. using ai, research can be extracted from documents, classified, tokenised in individual words, and encoded as information-dense word embeddings, which are vector representations of words, without human supervision (tshitoyan et al. 2019). building on these developments we suggest a methodology specific to marketing science. the first step is to compile consolidated bodies of offline marketing research on topics such as branding, retail or advertising following tshitoyan et al. (2019) method of knowledge extraction and relationships for the handling of large bodies of scientific literature. for this we shall use crossref application programming interface (api) for the retrieval of large lists of article digital object identifiers (dois). this is used by a number of publisher apis, such as elsevier https://dev.elsevier.com and springer nature https://dev.springernature.com to download full-text journal articles. secondly, the embeddings will be trained with the scientific abstracts from each of the topics. for this we shall use article abstracts from 1975 to 2021 from more than a thousand journals and also articles likely to contain marketing-related research directly retrieved from the aforementioned databases (i.e. elsevier and science direct) combined with web scraping. the performance of the algorithm is deemed to improve when irrelevant abstracts are removed. the remaining abstracts will then be classified as relevant and tokenised using chemdataextractor (swain and cole 2016). correct pre-processing, especially the choice of phrases to be included as individual tokens should improve the results. in the third and final step, we shall repeat the first two steps integrating offline topics with the equivalent online topics, e.g. online branding. as ai is also capable of predictive writing using bidirectional encoders bert and elmo used to produce contextual word embeddings (devlin et al. 2018), our work in progress will consider developing automated hypotheses formulation in marketing science (spangler et al. 2014). simplification of knowledge could also facilitate its transfer to practice.. artificial intelligence machine learning marketing science natural language processing text mining","opportunities and challenges facing ai voice-based assistants: consumer perceptions and technology realities: an abstract. where ai has become especially pivotal for users’ interactions is in the case of voice-based assistants (vas), such as apple’s siri and amazon’s alexa. from what is initially perceived as being a tool to play music, read out news reports and set timers, vas have developed considerably in recent years and their functionality goes way beyond initial perceptions. for example, amazon alexa will be able to give out health advice to users in the united kingdom as health-related questions will be automatically searched for using the official nhs website (mit technology review, 2020). some reports go as far to discuss how amazon has plans to be able to run someone’s entire life from the alexa on the basis that the systems are getting so sophisticated and the data being collected is so vast that the alexa will be capable of predicting needs (hao, 2019). nevertheless, users still appear to be resistant to use vas, with some even reluctant to engage with this technology entirely (pwc, 2018). most of these constraints to va adoption primarily relate to lack of trust, perceived data privacy and security concerns and lack of knowledge or understanding. despite the benefits and opportunities of ai software, it is inherently limited by capabilities surrounding planning, reasoning, knowledge, natural language processing, ability to move and to empathise. it is this lack of emotional connection that is a fundamental component of users being less trusting towards ai voice-based assistants. this research investigates the role of emerging capabilities, privacy concerns and trust towards vas using a mixed-methodology design. results from the qualitative element of the study reveal that it is more about trusting the capabilities and functionalities than being scared over personal data. this theme will subsequently be examined further in a follow-up experimental study to examine the role of context towards these perceptions. the focus of the experimental design will subsequently consider the direct relationship between perceived behavioural control and intention to continue to use vas with consideration into the indirect effects of privacy concerns and trust and the moderating role of perceived capabilities. the findings, thus far, show how vas, as new technologies and leaders of machine learning capabilities, can enhance customer experiences, improve customer relationships and add value to firms. as privacy concerns are often dismissed, if customers benefit from the sharing of their information, yet their capabilities being under-appreciated, firms can focus attention to the high-quality functions of these devices to encourage their more frequent use.. artificial intelligence privacy trust voice based assistants","the performance of digital ecosystem: the moderating effects of internationalization stage: an abstract. according to gartner's forecast in january 2020, global it spending will reach $3.9 trillion in 2020, an increase of 3.4% over 2019. it is estimated that global it spending will exceed $4 trillion in 2021. this trend signals the tendency of building digital ecosystem globally. an emerging trend among multinational firms is to digitalize global operation and build digital ecosystem. e-commerce platform, as a digital platform for coordination among sellers, online payment providers, logistics providers and customer relations personnel, emerges as an efficient channel to build a digital ecosystem (senyo et al. 2019). the use of e-commerce platform in the development of an ecosystem can enhance the diversity and innovation of complements in the platform (gawer and cusumano 2014; song et al. 2018). with the progress of technology, digitization improves the overall effectiveness of resource integration, thus promoting the construction of enterprise ecosystem (sklyar et al. 2019). the digital platform has become an effective interface to facilitate multilateral transactions and exchanges between users and providers of complementary products and services. the internationalization of a digital platform depends largely on whether the platform can attract ecosystem participants in the local market and align their goals with those of the platform (ojala et al. 2018). by investigating digital-platform firms listed in u.s. stock market, we challenge the doctrine thinking in international marketing that firms have a better chance to make good financial performance when expanding to more foreign markets over time. annual-report data were collected by visiting the corporate websites of chinese tourism firms listed in u.s. stock market. firm performance (return on equity) data were drawn from the annual reports. a text-analytics approach was used in data processing and analysis. we collected annual report data which were processed with python to compile a database with approximately 30,000 sentences. the findings indicate that digital-platform firms gain better performance in early stage internationalization. managers when building digital ecosystem should be aware the potential challenges in their later stage internationalization. digital ecosystem firm performance international marketing text analysis","disclosure of brand-related information and firm value: an abstract. research on voluntary disclosure highlights the role of informative financial reporting for capital markets. information asymmetry impedes the efficient allocation of resources in capital markets while credible disclosure between managers and investors plays an important role in mitigating these problems. existing research provides limited insight into firms’ disclosure of information about their market-based assets such as brands, although brand is an important part of firms’ value proposition. this study aims to fill this research gap by investigating brand-related information (bri) disclosure in us firms’ annual reports with three research steps. first, we develop the conceptualization of bri, based on which, we build a bri coding dictionary. we then conduct text mining on 10-k reports and construct a bri disclosure index to capture the bri disclosure level in firms’ 10-k reports. in the third step, we intend to explore the key factors driving bri disclosure as well as its financial market consequences. we propose to empirically investigate how different levels of bri disclosure are explained by firm characteristics and actual brand performance. we will also examine how bri disclosure influences firm value, as well as the role that bri disclosure plays in explaining the relationship between brand performance and firm value. this study fills the research gap in the field of information disclosure concerning bri by conceptualizing a bri framework and providing evidence on whether firms disclose bri in financial reporting. this study will also contribute to marketing-finance literature by empirically investigating the informational value of bri disclosure to firm value. the findings will provide managers and financial market participants (e.g., analysts, investors) who need to communicate and evaluate the performance implications of branding strategies and related activities with enlightenment on the value relevance of bri disclosures.. brand value brand-related information firm value information disclosure text mining","wisdom from words: marketing insights from text. language is an integral part of marketing. consumers share word of mouth, salespeople pitch services, and advertisements try to persuade. further, small differences in wording can have a big impact. but while it is clear that language is both frequent and important, how can we extract insight from this new form of data? this paper provides an introduction to the main approaches to automated textual analysis and how researchers can use them to extract marketing insight. we provide a brief summary of dictionaries, topic modeling, and embeddings, some examples of how each approach can be used, and some advantages and limitations inherent to each method. further, we outline how these approaches can be used both in empirical analysis of field data as well as experiments. finally, an appendix provides links to relevant tools and readings to help interested readers learn more. by introducing more researchers to these valuable and accessible tools, we hope to encourage their adoption in a wide variety of areas of research.. automated textual analysis language natural language processing","nlp technologies for analysing user generated twitter data to identify the reputation of universities in the valencian community, spain. the measurement of a university's reputation is currently based on rankings published nationally and internationally. these rankings are based on different criteria directly related to research and teaching, thereby creating a reputation. however, the proliferation of digital media has made it is possible to know the real opinion of stakeholders on a range of issues, including brand perception. therefore, analysing datasets from social networks, blogs and the comment sections of websites has become an increasingly worthwhile task to getting a thorough understanding of an organisation's reputation. one way to approach this task is to use natural language processing (nlp) technologies as part of the process of conceiving the social reputation of institutional brands by interpreting a large amount of comments from social users. nlp technologies are useful for identifying and quantifying positive, neutral, negative and other information posted on social networks, i.e., twitter. through this research, we intend to monitor the situation of universities in the valencian community in terms of audience opinions, based on a justified selection of human language technologies that provide the necessary data from tweets, to create different classifications such as reputation, audience, among others.. audience human language technologies natural language processing nlp opinion mining public opinion reputation sentiment analysis social media spain technologies universities","“does it go without saying?” implication of electronic word of mouth in luxury branding: an abstract. the rise of social media has provided firms with ample new opportunities to communicate their brands (carlson et al. 2019; zhang et al. 2018). additionally, because textual and audio-visual data on social media platforms can be preserved, the medium has become a valuable tool for providing insights and informing strategic brand management (kim et al. 2019; greco and polli 2019). within the overall social media communication environment, brand communities specifically provide unique insights, as they can enhance understanding regarding changes in consumer’s opinions and feeling and how they perceive and talk to each other about a brand (kim and lee 2019). using automated text analysis and berthon and his colleagues’ (2009) framework to classify the user-generated brand content, this study investigates consumer attitudes towards luxury products based on the online communication within a facebook brand community. this study includes a sample size of 15,014 comments from 302 posts from a facebook luxury fashion group. the analysis revealed several significant main and interaction effects. this included significant main effects of aesthetics on analytic thinking and tone. more importantly, a significant two-way interaction between aesthetic and ontology occurred on analytic thinking and tone. the study provides useful and interesting insights into the theory and practice of luxury branding. for marketers bringing luxury to the masses could ensure higher sales, however, the perceived loss of exclusivity experienced by the high aesthetic customers could negatively impact the brand’s image in the long run. luxury brands could mitigate these negative feelings and fuel prolonged consumer engagement by providing other options that make this part of their target audience feel special. since the high aesthetic consumers are generally well informed about a company’s products or services, brands should consider selecting these individuals for co-creation and product innovation design purposes. this will not only help in re-building the consumer-brand relationship but because of their status within the social group will likely add to enhanced positive social discourse. this work also adds to the luxury fashion literature by incorporating linguistic inquiry and word count as a research tool to investigating luxury brand discourse in online brand communities, confirming the influence of the social context and conclusions by berthon et al. (2009) that multiple market segments for one luxury brand exist and that marketing strategies should be focused accordingly.. ewom liwc luxury branding social media","artificial intelligence analysis of marketing scientific literature: an abstract. we discuss the development of a machine-based research literature reading methodology for marketing science based on artificial intelligence (ai) developments made in other fields. marketing science would benefit from adopting ai-based technologies for text mining, analysis and predictive writing. this could automate literature review and formulation of hypotheses. scientific progress is disseminated in scientific publications, which are growing exponentially. thousands of papers are published each year. research overload is a bottleneck for scientific development. keeping up is untenable for researchers even within specialised fields. recognising new questions or hypotheses will be increasingly challenging. the rapid increase in the rate of research points out the need for techniques that can simplify its use. ai techniques such as text mining and natural language processing (nlp) can assist in removing the bottleneck. text mining is the use of algorithms for extracting information from text documents such as scientific articles. in ai, nlp is used to analyse large amounts of natural language data. the combination of nlp and text mining techniques enables scientific literature text mining with nlp. in this regard, the most advanced developments have been made in the disciplines of chemistry and materials (e.g. kim et al. 2017; tshitoyan et al. 2019). in these fields, knowledge extraction and relationships are made using massive bodies of scientific literature using crossref application programming interface (api) to retrieve large lists of article digital object identifiers (dois). our suggested method for marketing science will be based on the developments made in chemicals and materials. word embeddings will be trained with marketing science abstracts retrieved from scientific databases (e.g. elsevier and science direct). after removing irrelevant abstracts, the remaining relevant ones will be tokenised. this will result in a vocabulary formed by the extracted contents. we shall then conduct a three-step process adopted from tshitoyan et al. (2019). first, the compilation of consolidated bodies of offline marketing science research such as retail or advertising. second, the embeddings will be trained with scientific abstracts from each topic. the abstracts will then be tokenised to produce individual words. third, we shall repeat the first two integrating the offline topics with the equivalent online topics (i.e. online retail or advertising). lessons learned when dealing with the offline research may help inform analyses made with the online research. our method could eventually facilitate transfer of research knowledge to marketing practice, especially to startups, due to the increasing need for applying research to practice (kumar 2017; lilien 2011).. artificial intelligence machine reading marketing science natural language processing","a text mining approach to assessing company ratings via user-generated and company-generated content: an abstract. retailing and third-party websites serve as online platforms for customers to gather information and interact with other users as well as companies. user-generated content is available through different online platforms, with online reviews being one of the most common type of user-generated content. while previous research has primarily focused on influential characteristics of reviews or on perceived usefulness or helpfulness of reviews from the user perceptive, the current research expands the literature on company-customer online relationship building by investigating the influence of user reviews and manager responses on the overall rating of the corresponding company. user reviews is an important format of user-generated content, while manager responses is a strategically used form of company-generated content. this research implements a text mining approach and sentiment analysis to assess how emotional versus rational user reviews and manager responses impact overall company ratings. specifically, linguistic inquiry and word count (liwc) dictionary is used for the language processing tasks, including sentence segmentation, word tokenization, and lemmatization. data collection implements a python-based web crawler to gather a consistent panel of user-level activity from tripadvisor.com. the final data includes a three-year period from 2016 to 2018 of hotel reviews featuring user information, text reviews, and ratings given by the reviewers. manager responses and corresponding hotel ratings complete the data set. current findings provide further insights into how companies can utilize public manager responses as a business strategy to increase online ratings of their firm. managerial implications include that companies can improve online ratings if managers incorporate emotional responses with long sentences. future studies should incorporate additional cities beyond the us to enhance generalizability of findings. furthermore, this research examines online rating data for one service category, namely hotels. additional industries, service categories, and reviewer characteristics can enhance contributions of future research endeavors. incorporating additional service categories would allow the robustness assessment of the negative influence of rational online content on company ratings. lastly, the current time lag variable remains limited due to the three-year period. assessing how repeated interaction between reviewer and manager influences company ratings over time would provide additional guidance for firms in addressing online content and maintaining positive online reputations.. company rating online reviews rationality sentiment analysis text mining user-generated content","should we continue using intelligent virtual assistants? the role of uses gratifications and privacy concerns: an abstract. intelligent virtual assistants (ivas) are invading homes and workplaces all over the world. continuance advancements and innovations with automation technologies such as robotic process automation, artificial intelligence and natural language processing have boosted the global market of these devices. after first adoption, which could be fostered due to marketing and promotional campaigns and also users’ curiosity, the continuance usage of these tools is tremendously important to build a sustainable market and is therefore challenging for vendors. this article investigates the motivation to continue using these tools that could stem from the gratifications they afford to users. we are also interested in the constraints that hinder this behavior, especially privacy concerns, as ivas are perceived as ‘open-ended’ and community-based devices (ku et al. 2013). building on three areas of research: information technology and information systems (is/it) continuance usage (al-maghrabi et al. 2011; zhou 2013), uses and gratifications theory (ugt) (dolan et al. 2016; ifinedo 2016) and privacy concerns (ku et al. 2013; larose and eastin 2002), we develop an original research model of ivas continuance usage intention. our focus will be put on home usage of ivas, which excludes usage at professional settings (burns and igou 2019; todorov et al. 2018). to test the model, an online survey is conducted within a sample of 258 ivas’ users in france. part least squared structural equation modeling is applied to analyze data. results show that privacy concerns strongly and negatively affect continuance intention. this dependent construct is also positively and directly determined by subjective norms, perceived critical mass, and uses gratifications, i.e. utilitarian gratifications (social interactions and information seeking) and more significantly hedonic gratifications (entertainment and fashion). besides, only subjective norms could be considered as antecedent factors to all our gratifications constructs. this study contributes to the literature on ivas, ugt and privacy concerns. it also provides ivas’ vendors with useful insights on how to retain their users.. continuance intention to use intelligent virtual assistant privacy concerns uses and gratifications theory","consumers’ exposure to the inexpedient message: a systematic analysis to evaluate the consequences on brand reputation in social media. the aim of this study is to evaluate consumers’ moral judgements of the brand reputation of a luxury firm when exposed to wrongdoings in branded communications. a wealth of published research has shown that ill-prepared branding messages may cause confusion to consumers, misinterpretations on behalf of message receivers, generate negative perceptions to wider audiences and ultimately damage the firms’ reputation. to this end, the case of dolce and gabbana 2019 campaign in china is analysed, drawing upon the basics of social media strategy, luxury market features and cross-cultural characteristics. the research evaluates consumers’ instagram posts and relevant reactions to the 2018 dolce and gabbana campaign in their effort to enter the chinese market. a systematic analysis is conducted about the extent to which luxury brand reputation is influenced by consumers’ generated content in social media. drawing on a comprehensive and unique dataset of unsolicited consumers’ communications as response to a certain message, the study evaluates the immediate impact of the wrong message on the brand reputation. results also show that after a negative message comes to light, firm reaction can hardly reduce or eliminate the negative consequences and restore the reputation.. brand reputation cross-cultural communication instagram luxury brands social media strategy systematic text analysis","studying the effects of sponsorship disclosure and message complexity on consumers’ response via text analysis: an abstract. influencer marketing utilizes influential online opinion leaders to disseminate brand related user-generated content and shape audience attitudes through social networking sites (lim et al. 2017), is currently widely adopted by brands to effectively reach their target audiences (evans et al. 2017). despite many benefits of influencer marketing for brands, public policy concerns regarding the covert nature of paid brand endorsements that are masqueraded as unadulterated opinions have risen in the past years (johnson et al. 2019). sparking calls for transparency and disclosure of sponsorship information to facilitate the consumers’ ability to differentiate sponsored content from authentic (i.e., non-sponsored) product reviews and editorial content (veirman and hudders 2019). recent guidelines and (self-)regulatory initiatives (e.g., asa 2018; ftc 2017; iab 2016) prescribe the disclosure of any commercial relationship between the influencer and the brand in their social media posts. using automated text analysis (liwc) on audio-visual branded content, this study explores the role and impact of sponsorship disclosure and message complexity on consumers’ responses. findings suggest that message complexity and sponsorship disclosure can significantly affect consumers’ tone in their responses. additionally, sponsorship disclosure significantly affects consumers’ clout. the results also reveal that different levels of message complexity resulted in different levels of consumers’ analytical thinking and clout when posts with or without sponsorship disclosure. this study makes several contributions to the further understanding of the function of disclosures in sponsored instagram posts as a type of native advertising. the disclosure of sponsorship would generate more confidence for consumers, as it provides information for consumers to arrive at causal explanations for events. however, disclosure of sponsored messages also generates feelings of suspicion and deception for consumers. our finding further supports previous findings suggesting sponsorship disclosure helps consumer recognize sponsored posts as advertising, and in turn reduce the effectiveness of the persuasive (evans et al. 2017; boerman et al. 2017). moreover, mediums with different levels of message complexity result in different clout levels. consumers from video posts appeared to have more confidence and a sense of social status because compare with photos, videos provided involve them in a dynamic environment and provide more comprehensive storylines.. influencer marketing liwc message complexity sponsorship disclosure","neuroscience research in consumer behavior: a review and future research agenda. consumer neuroscience is a growing field in both marketing and consumer behavior research. the number of articles published on the topic has increased exponentially in the last 15 years. however, there is still no comprehensive analysis of the literature highlighting the main constructs, trends and research gaps found in such a large collection of papers. therefore, this paper provides a text mining (tm) analysis that clusters and systematizes the complex and dispersed information of 469 articles, using the correlated topic model algorithm (ctm). results show that “consumer neuroscience”, “brand memory”, and “willingness to buy” are the most relevant topics in the field. this study also reveals that the literature has been focusing on ethical concerns as well as on controversial concerns in the use of consumer neuroscience techniques. we include a final section on future research questions and opportunities that emerged from the conducted research.. consumer behavior consumer research correlated topic models neuroscience text mining","luxury fashion consumption: a review, synthesis and research agenda. purpose: this paper aims to review the topic of “luxury fashion consumption”, a field of recent interest for academics and practitioners. however, a literature review that can map the existing knowledge and aggregate it into relevant topics and offers a research agenda for future research is still lacking. methodology: this paper uses a systematic review and a text mining approach to analyse 73 articles on luxury fashion consumption aiming to clarify, rationalise and critically interpret the literature on luxury fashion consumption; identify the core topic, create an integrative framework of core constructs; and offer research gaps and suggest a research agenda for future studies. findings: from this analysis, eight major research topics are found and analysed (brand desire, authenticity, luxury markets, value perceptions, luxury retail experience, luxury brands communication, responsible consumption and sustainability and status signalling). based on these topics and following the tcm framework, this review offers directions for future research. value: this research offers a text-mining review of luxury fashion consumption to help scholars and managers further develop this field, as there is no comprehensive review on the topic exploring the themes, theories, constructs and methods used in prior studies.. conceptual framework of luxury fashion fashion luxury consumption research agenda systematic review text mining","employing machine learning for capturing covid-19 consumer sentiments from six countries: a methodological illustration. purpose: this paper aims to illustrate the scope and challenges of using computer-aided content analysis in international marketing with the aim to capture consumer sentiments about covid-19 from multi-lingual tweets. design/methodology/approach: the study is based on some 35 million original covid-19-related tweets. the study methodology illustrates the use of supervised machine learning and artificial neural network techniques to conduct extensive information extraction. findings: the authors identified more than two million tweets from six countries and categorized them into pestel (i.e. political, economic, social, technological, environmental and legal) dimensions. the extracted consumer sentiments and associated emotions show substantial differences across countries. our analyses highlight opportunities and challenges inherent in using multi-lingual online sentiment analysis in international marketing. based on these insights, several future research directions are proposed. originality/value: first, the authors contribute to methodology development in international marketing by providing a “use-case” for computer-aided text mining in a multi-lingual context. second, the authors add to the knowledge on differences in covid-19-related consumer sentiments in different countries. third, the authors provide avenues for future research on the analysis of unstructured multi-media posts.. artificial neural network computer-aided analysis consumer sentiments cross-country comparisons multi-lingual tweets multi-media posts pestel supervised machine learning text mining","customer engagement behaviours in a social media context revisited: using both the formative measurement model and text mining techniques. most research in the field of customer engagement employs multi-item measures that are comceptualised as three-dimensional and reflective models. this leaves room to suggest an alternative approach for defining and operationalising the construct. from a behavioural standpoint, we propose a higher-order formative measurement model (fmm) underlying customer engagement behaviours (cebs) in the context of facebook fan pages. data from 259 restaurant customers show that the fmm works well, both theoretically and empirically, and that cebs include eight dimensions and 16 indices. we also apply text mining (tm) techniques to analyse customers’ facebook posts. the findings indicate that some dimensions identified by the fmm could not be extracted using tm, and the tm analysis provided clues regarding the fmm indices; the two approaches complement rather than compete with each other. these results serve as a basis for scale development in future research, and provide guidelines for managers to enhance long-term customer relationships.. customer engagement facebook fan page formative measurement model second-order formative construct social media-based customer engagement behaviours social media-based marketing","understanding consumers’ sentiment expressions in online reviews: a hybrid approach. this study explores consumers’ sentiment and its influencing factors in the context of theme parks by analyzing online reviews. a hybrid approach is employed, including sentiment analysis, logistic regression analysis, and co-occurrence network analysis. the findings provide new insights on the sentiment expression of consumers in different regions, that is, australian consumers express significantly more positive sentiment than us consumers. in addition, the results reveal the crucial experiential factors causing positive and negative sentiment. this study not only contributes to the understanding of consumers’ review behavior and ewom but also provides a reference for the application of data driven approach in intelligent marketing and management.. ewom online reviews sentiment text mining theme park user-generated content","dynamic impact of negative public sentiment on agricultural product prices during covid-19. the covid-19 pandemic has had a significantly negative impact on public sentiment, which has resulted in panic and some irrational buying behavior, which in turn has had a complex impact on agricultural product prices. this study quantified online negative sentiment using micro-blog text mining and a time-varying parameter vector autoregressive model (tvp-var) to empirically analyze the dynamic impact of negative public emotions on agricultural product prices during the covid-19 pandemic in china. it was found that the online negative sentiment impacted agricultural products prices during covid-19 and had significant time-varying, lag, and life cycle characteristics, with the responses being most significant in the spread and recession periods. differences were found in the price responses for different agricultural products and in different risk areas. the online negative sentiment was found to have the greatest impact on vegetable prices, with livestock products and vegetable prices being mainly positively impacted, fruit prices being mainly negatively impacted, and aquatic product prices being negatively impacted in the early stage and positively impacted in the middle and late stages. the online negative sentiment had the greatest impact on medium-risk area agricultural product prices, followed by low-risk areas, with the lowest impact found on the high-risk area agricultural product prices. three policy suggestions for epidemic monitoring, public opinion guidance and control, and the timely release of agricultural product information are given based on the results.. agricultural product prices covid -19 dynamic impact online negative sentiment tvp-var model","whose view is it anyway? media coverage of litigation in for-profit firms’ role in the opioid crisis. the united states opioid epidemic continues to bring suffering to individuals and families as well as a crushing economic toll to communities. stakeholders require resources to combat the epidemic. firms that manufacture, distribute, and market opioids are the primary defendants of lawsuits, which play out in public view. this study investigates the public statements made by firms and media coverage in opioid lawsuits through the lens of their word choices. we use text analysis to uncover the similarities and differences in the language used to discuss the opioid crisis. the results provide guidance for marketing, public relations, and communications.. na","can combined marketing and planning-oriented of community-based social marketing (cbsm) project successfully transform the slum area to tourism village? a case study of the jodipan colorful urban village, malang, indonesia. this study evaluated the branding of a slum project as a social marketing initiative to transform the jodipan slum area into a tourism village in the city of malang. an action–evaluation research project was conducted through the combination of the marketing and planning oriented approaches by the communications department at a major public university in indonesia between june 2016 and october 2017. the study employed a case study supported by qualitative data from primary and secondary sources, including social media text mining analysis. moreover, an evaluation survey was conducted using proportionally random sampling from 650 slum dwellers to assess the impacts of the project in terms of the general welfare of the community. the transformation of the slum area into a new tourist destination was found to have been successfully implemented through the combined marketing and planning-oriented community social marketing project. the tourist destination produced was also observed to have a good impact on environmental sustainability and economic value for the local community. jodipan branding project is unique as observed in its ability to transform an unhealthy settlement into a healthy settlement. theoretically, it has contributed to new knowledge on slum upgrading in urban studies, especially with the focus on the successful improvement of the physical, social, and economic conditions of the area. this research further contributed to a new understanding and novelty of knowledge on the ability of place branding, as a social marketing strategy, to change the behavior of slum dwellers. however, this research has limitations in exploring the local government’s response to the project, therefore, future studies need to clarify issues relating to local policies and official budgets for its sustainability.. jodipan place branding slum area social marketing social media tourism","voluntary simplicity: an exploration through text analysis. overconsumption poses severe ethical problems and causes harm to the environment. today many individuals change their consumption practices and actively limit their overall consumption due to ethical and environmental concerns. this article focuses on voluntary simplicity, a sustainable lifestyle phenomenon that comprises environment-friendly consumption practices as an innate characteristic. this paper offers a fresh and nuanced understanding of voluntary simplicity through the lens of social cognitive theory (sct) and by examining textual content generated by voluntary simplifiers on facebook. five thousand three hundred thirty-six facebook posts and comments were scrapped and examined using sentiment analysis, associative networks and cluster analysis. results further our understanding of this lifestyle in the three interconnected domains of sct (i.e., personal, behavioural and environmental). insights obtained from this study can help develop a more complete conceptualization of voluntary simplicity and consumers who consider adopting this lifestyle choice.. na","artificial intelligence in utilitarian vs. hedonic contexts: the “word-of-machine” effect. rapid development and adoption of ai, machine learning, and natural language processing applications challenge managers and policy makers to harness these transformative technologies. in this context, the authors provide evidence of a novel “word-of-machine” effect, the phenomenon by which utilitarian/hedonic attribute trade-offs determine preference for, or resistance to, ai-based recommendations compared with traditional word of mouth, or human-based recommendations. the word-of-machine effect stems from a lay belief that ai recommenders are more competent than human recommenders in the utilitarian realm and less competent than human recommenders in the hedonic realm. as a consequence, importance or salience of utilitarian attributes determine preference for ai recommenders over human ones, and importance or salience of hedonic attributes determine resistance to ai recommenders over human ones (studies 1–4). the word-of machine effect is robust to attribute complexity, number of options considered, and transaction costs. the word-of-machine effect reverses for utilitarian goals if a recommendation needs matching to a person’s unique preferences (study 5) and is eliminated in the case of human–ai hybrid decision making (i.e., augmented rather than artificial intelligence; study 6). an intervention based on the consider-the-opposite protocol attenuates the word-of-machine effect (studies 7a–b).. algorithms artificial intelligence augmented intelligence hedonic and utilitarian consumption recommendations technology","a multistep strategy for analyzing consumer generated data. the significant amount of on-line users generated contents (ugcs) represents an inexhaustible mine of useful information for companies. if written about specific transactions or commercial experiences, such contents constitute the so-called electronic word of mouth (ewom), able to influence the purchase intentions of other potential buyers. when expressed in natural language, ewom can be analyzed for a variety of purposes. in this study, a multistep strategy of analysis is proposed, in order to highlight the multiplicity of useful information for companies that can be drawn from ewom content, as well as the potential of text mining techniques. to meet these objectives, about 850,000 reviews were collected on best performing 320 products belonging to the 32 macro-categories present on the e-commerce amazon.com platform. this large corpus was the source for extracting customers' sentiment, and assessing the levels of their satisfaction regarding products. furthermore, it was useful to evaluate the effectiveness of the reviews according to their polarity, and identify the salient aspects around which ewom revolve on the e-commerce platform.. big data customer satisfaction ewom machine learning sentiment analysis","the power of brand selfies. smartphones have made it nearly effortless to share images of branded experiences. this research classifies social media brand imagery and studies user response. aside from packshots (standalone product images), two types of brand-related selfie images appear online: consumer selfies (featuring brands and consumers’ faces) and an emerging phenomenon the authors term “brand selfies” (invisible consumers holding a branded product). the authors use convolutional neural networks to identify these archetypes and train language models to infer social media response to more than a quarter-million brand-image posts (185 brands on twitter and instagram). they find that consumer-selfie images receive more sender engagement (i.e., likes and comments), whereas brand selfies result in more brand engagement, expressed by purchase intentions. these results cast doubt on whether conventional social media metrics are appropriate indicators of brand engagement. results for display ads are consistent with this observation, with higher click-through rates for brand selfies than for consumer selfies. a controlled lab experiment suggests that self-reference is driving the differential response to selfie images. collectively, these results demonstrate how (interpretable) machine learning helps extract marketing-relevant information from unstructured multimedia content and that selfie images are a matter of perspective in terms of actual brand engagement.. deep learning image analysis interpretable machine learning natural language processing social media user-generated content","emotional expressions and brand status. this project investigates emotionality by brands on social media. first, a field data set of over 200,000 text and images posts by brands across two major platforms is analyzed. using recent automated text analysis (study 1a) and computer vision methods (studies 1b and 1c), the author provides initial documentation of a negative relationship between brand emotionality and status. exploring this relationship further, in studies 2, 3, and 4, the author finds that brands can leverage this association, reducing emotionality in brand communications to increase perceived brand status. this strategy is effective because reduced emotionality is associated with high-status communication norms, which evoke high-status reference groups. this finding is moderated by the status context of the brand (study 2) and the product type (study 4).. computer vision emotional expression reference groups social media status text analysis","construal matching in online search: applying text analysis to illuminate the consumer decision journey. as consumers move through their decision journey, they adopt different goals (e.g., transactional vs. informational). in this research, the authors propose that consumer goals can be detected through textual analysis of online search queries and that both marketers and consumers can benefit when paid search results and advertisements match consumer search–related goals. in bridging construal level theory and textual analysis, the authors show that consumers at different stages of the decision journey tend to assume different levels of mental construal, or mindsets (i.e., abstract vs. concrete). they find evidence of a fluency-driven matching effect in online search such that when consumer mindsets are more abstract (more concrete), consumers generate textual search queries that use more abstract (more concrete) language. furthermore, they are more likely to click on search engine results and ad content that matches their mindset, thereby experiencing more search satisfaction and perceiving greater goal progress. six empirical studies, including a pilot study, a survey, three lab experiments, and a field experiment involving over 128,000 ad impressions provide support for this construal matching effect in online search.. abstract vs. concrete mindsets construal level consumer decision journey goal progress online search processing fluency search queries","the dynamics of distortion: how successive summarization alters the retelling of news. this work advances and tests a theory of how news information evolves as it is successively retold by consumers. drawing on data from over 11,000 participants across ten experiments, the authors offer evidence that when news is repeatedly retold, it undergoes a stylistic transformation termed “disagreeable personalization,” wherein original facts are increasingly supplanted by opinions and interpretations with a slant toward negativity. the central thesis is that when retellers believe they are more (vs. less) knowledgeable than their recipient about the information they are relaying, they feel more compelled to provide guidance on its meaning and to do so in a persuasive manner. this enhanced motivation to guide persuasively, in turn, leads retellers to not only select the subset of facts they deem most essential but, critically, to provide their interpretations and opinions on those facts, with negativity being used as a means of grabbing their audience’s attention. implications of this work for research on retelling and consumer information diffusion are explored.. information diffusion natural language processing retelling social media word of mouth","facebook, trust and privacy in an election year: balancing politics and advertising. prior to the us 2020 presidential election, both twitter and google announced changes to their political advertising policies. this article explores trust and privacy issues relating to social media while evaluating current public opinion regarding political advertising on facebook, which unlike twitter and google, has remained steadfast in its support for political advertising. the study uses a textual analysis of twitter data collected from conversations in october 2019 to provide a snapshot of public rhetoric at this point in time. results from the text analysis identify trends around the upcoming election, political advertising, russia and trump. topics are further investigated using an online survey panel of 300 participants. anova results indicate that privacy concerns are a key driver of desire to remove political advertising. the results suggest that user perception of facebook advertising is decreasing as a result of privacy concerns related to trust in the platform. political advertisers should therefore reevaluate their communication strategy with regard to facebook advertising.. facebook misinformation online advertising online trust political advertising privacy public opinion","creating a ‘customer centricity graph’ from unstructured customer feedback. certain industries, such as car insurance, do not have many customer touch points and do not offer a great deal of differentiation in the market. marketers in such industries must therefore analyse vast amounts of customer-generated feedback in order to analyse customer preference in a quantitative manner. at present, this is done via market research or manual work, as an automated tool for summarising unstructured texts is as yet unavailable for certain european languages, including german. this paper discusses how insaas and lmu munich have used publicly available feedback on car insurance in germany to develop a dedicated pipeline for the computation and visualisation of customer opinions. this paper provides an overview of the various steps of the procedure.. ai b2c customer centricity dashboard nlp","thesis and antithesis — innovation and predictive analytics: ∑ (past + present) data ≠ future success. predictive analytics (pa) is a tool routinely used by companies to help chart a future product path. it makes extensive use of algorithms and data mining to sort out market desires and trends. it also combines a robust host of artificial intelligence tools, including machine learning, pattern recognition, natural language processing, sentiment analysis and emotion recognition, among others, to achieve more precise results. pa, though, is imperfect, as it is often subject to the whims of the marketplace. analysing past and present data does not, in any manner, guarantee positive results. indeed, when it comes to innovation, particularly ‘disruptive innovation’, relying on pa can lead a company down a disastrous path. data analytics requires a method that validates innovation and uses pa as something other than an infallible crystal ball. but does the possibility of innovation automatically disavow any insights into future market trends that pa may supply? this paper attempts to place both innovation and pa into proper perspective. it considers when, where, how and why pa and innovation are paramount, but reiterates the importance of instinct, originality and creativity. to illustrate its argument, the paper draws on the history of the sony walkman and apple ipod.. artificial intelligence (ai) data analytics disruption disruptive innovation innovation market analytics predictive analytics","negative online reviews, brand equity and emotional contagion. purpose: despite the growing consensus that consumers extensively use online reviews and that negative reviews can significantly damage brand equity, it remains uncertain whether negative online reviews that focus on different aspects of a service have a similar or differential effect on brand equity. this study aims to fill this gap and explores the mediating role of emotional contagion and what kind of response helps better deter their negative effect. design/methodology/approach: this research is conducted through a one-panel study and three experimental studies. sas enterprise miner is used for text mining analysis and analysis of variance (anova) and process macro models are used to analyze the experimental data. findings: negative reviews related to the tangibility, responsiveness and empathy dimensions have a more detrimental effect on brand equity than negative reviews related to the assurance and reliability dimensions. the results also provide evidence that emotional contagion is more prevalent when consumers read reviews that are specific to the empathy and responsiveness dimensions. finally, accommodative responses from the service provider are more effective in deterring the effect of a negative online review on brand equity. research limitations/implications: the generalizability of this study is limited to the restaurant and hotel industry. practical implications: the findings will also help the brand manager in understanding the comparative effect of service quality-specific negative reviews on brand equity and also the type of responses that brand managers should give to negative reviews. originality/value: despite online reviews receiving increased attention in academic research, service quality (servqual) dimension-specific reviews have not been studied until now. this study contributes to the service quality-related literature by providing evidence that not all negative online reviews related to different service quality (servqual) dimensions equally affect brand equity.. accommodative and defensive response brand equity data analysis emotional contagion online reviews service quality servqual","modeling dynamic user interests: a neural matrix factorization approach. in recent years, there has been significant interest in understanding users’ online content consumption patterns. but the unstructured, high-dimensional, and dynamic nature of such data makes extracting valuable insights challenging. here we propose a model that combines the simplicity of matrix factorization with the flexibility of neural networks to efficiently extract nonlinear patterns from massive text data collections relevant to con-sumers’ online consumption patterns. our model decomposes a user’s content consumption journey into nonlinear user and content factors that are used to model their dynamic interests. this natural decomposition allows us to summarize each user’s content consumption journey with a dynamic probabilistic weighting over a set of underlying content attributes. the model is fast to estimate, easy to interpret, and can harness external data sources as an empirical prior. these advantages make our method well suited to the chal-lenges posed by modern data sets used by digital marketers. we use our model to under-stand the dynamic news consumption interests of boston globe readers over five years. thorough qualitative studies, including a crowdsourced evaluation, highlight our model’s ability to accurately identify nuanced and coherent consumption patterns. these results are supported by our model’s superior and robust predictive performance over several competitive baseline methods.. deep learning digital marketing machine learning natural language processing user profiling","standing up for or against: a text-mining study on the recommendation of mobile payment apps. mobile payment systems offer enormous potential as alternative payment solutions. however, the diffusion of mobile payments over the years has been less than optimal despite the numerous studies that have explored the reasons for its adoption. consequently, there is an increased interest in exploring alternative actions for promoting its diffusion, especially user recommendation of the technology. this is because positive recommendations can enormously influence the decisions of potential consumers to use the technology while negative recommendations can increase resistance to it. the few extant studies in this domain have followed the traditional survey approach with hypothetic-deductive reasoning, thus limiting an understanding of factors outside their conceptual models that could influence recommendations. to address this shortcoming, this study uses a qualitative text-mining approach that explores themes from user reviews of mobile payment applications (apps). using 5955 reviews from 16 mobile payment apps hosted on the google play store, this study applied the latent dirichlet allocation (lda) text-mining method to extract themes from the reviews that help to explain why users provide positive or negative recommendations about mobile payment systems. a total of 13 themes (i.e. ease of use, usefulness, convenience, security, reliability, satisfaction, transaction speed, time-saving, customer support, output quality, perceived cost, usability and trust) were generated from the lda model which provides both theoretical and practical insights for advancing mobile payments diffusion and research.. mobile payment negative recommendation positive recommendation text-mining","tell me a story about yourself: the words of shopping experience and self-satisfaction. in this paper we investigate the verbal expression of shopping experience obtained by a sample of customers asked to freely verbalize how they felt when entering a store. using novel tools of text mining and social network analysis, we analyzed the interviews to understand the connection between the emotions aroused during the shopping experience, satisfaction and the way participants link these concepts to self-satisfaction and self-identity. the results show a prominent role of emotions in the discourse about the shopping experience before purchasing and an inward-looking connection to the self. our results also suggest that modern retail environment should enhance the hedonic shopping experience in terms of fun, fantasy, moods, and emotions.. self-identity self-satisfaction semantic brand score shopping experience text analysis","using structural topic modelling to predict users’ sentiment towards intelligent personal agents. an application for amazon's echo and google home. despite growing levels of usage of intelligent personal assistants (hereinafter, ipa), their in-home usage has not been studied in depth by scholars. to increase our understanding of user interactions with ipa, our research created a theoretical framework rooted in technology acceptance models and uses and gratification theory. our empirical method designs an ambitious analysis of natural and non-structured narratives (user-generated content) on amazon's echo and google home. and to identify key aspects that differentially influence the evaluation of ipa our method employs machine-learning algorithms based on text summarisation, structural topic modelling and cluster analysis, sentiment analysis, and xgboost regression, among other approaches. our results reveal that (hedonic and utilitarian) benefits gratification, social influence and facilitating conditions have a direct impact on the users’ sentiment for ipa. to sum up, designers and managers should recognise the challenge of increasing the customer satisfaction of current and potential users by adjusting doubtful users’ technical skills and the (hedonic, cognitive, and social) benefits and functionalities of ipa to avoid boredom after a short lapse of time. finally, the discussion section outlines future lines of research and theoretical and managerial implications.. intelligent personal assistants sentiment analysis structural topic model technology acceptance models text analytics uses and gratification theory xgboost regression","virtual reality and gamification in marketing higher education: a review and research agenda. purpose: the purpose of this paper is to review studies on the use of virtual reality (vr) and gamification to engage students in higher education for marketing issues to identify the research topics, the research gaps and to prepare a future research agenda. design/methodology/approach: a literature review is performed based on two search terms applied to web of science, resulting in a final pool of 115 articles. a text-mining approach is used to conduct a full-text analysis of papers related to vr and gamification in higher education. the authors also compare the salient characteristics presented in the articles. findings: from this analysis, five major research topics are found and analysed, namely, teaching methodologies and education, experience and motivation, student engagement, applied theories in vr and gamification. based on this and following the theory concept characteristics methodology framework, the paper provides directions for future research. originality/value: there is no comprehensive review exploring the topics, theories, constructs and methods used in prior studies concerning vr and gamification applied to higher education services based on all the articles published in well-regarded academic journals. this review seeks to provide deeper insights, to help scholars contribute to the development of this research field.. education gamification learning process student engagement text-mining virtual reality","wordify: a tool for discovering and differentiating consumer vocabularies. this work describes and illustrates a free and easy-to-use online text-analysis tool for understanding how consumer word use varies across contexts. the tool, wordify, uses randomized logistic regression (rlr) to identify the words that best discriminate texts drawn from different pre-classified corpora, such as posts written by men versus women, or texts containing mostly negative versus positive valence. we present illustrative examples to show how the tool can be used for such diverse purposes as (1) uncovering the distinctive vocabularies that consumers use when writing reviews on smartphones versus pcs, (2) discovering how the words used in tweets differ between presumed supporters and opponents of a controversial ad, and (3) expanding the dictionaries of dictionary-based sentiment-measurement tools. we show empirically that wordify's rlr algorithm performs better at discriminating vocabularies than support vector machines and chisquare selectors, while offering significant advantages in computing time. a discussion is also provided on the use of wordify in conjunction with other textanalysis tools, such as probabilistic topic modeling and sentiment analysis, to gain more profound knowledge of the role of language in consumer behavior.. language natural language processing sentiment analysis text analysis","understanding service quality attributes that drive user ratings: a text mining approach. studies based on online textual data are increasingly used in the hospitality sector to gain better consumer insights. using text mining of tripadvisor reviews and mapping it to hard and soft attributes of servqual dimensions, this research attempts to identify service quality characteristics that influence the customers’ online ratings of hotels. the moderating effects of the reviewer characteristics, namely (a) domestic vs foreign (b) novice vs experienced, (c) less vs more popular and (d) brief vs elaborate writing, on the relationship between quality attributes and online hotel ratings are also analyzed. the results imply that reviewers emphasize hard attributes of service quality in general, such as tangibles, reliability, and responsiveness, while providing online ratings to hotels. furthermore, reviewer characteristics also influence the attributes emphasized in reviews and the resultant ratings. finally, along with the theoretical contributions, managerial contributions, such as the usefulness of service planning outcomes, are discussed.. hard and soft attributes hotel ratings online reviews servqual text mining","what are consumers saying online about your products? mining the text of online reviews to uncover hidden features. thanks to the growth of the internet and the increasing use of social networks, companies can now access huge volumes of online texts in order to understand consumers’ preferences and needs. this article illustrates some methods to extrapolate information from such texts. the text-mining analysis covers such issues as word frequency, sentiment analysis, paired words, similarities in textual content and the main topics discussed in online reviews. from a practical point of view, brand managers can use the proposed methods to gain consumer insights into products and brands, to be able to improve and adapt their marketing strategies.. ewom online reviews sentiment analysis text mining topic modelling","the impact of increase in minimum wages on consumer perceptions of service: a transformer model of online restaurant reviews. we study the impact of a mandated increase in minimum wages on consumer perceptions of multiple dimensions of service quality in the restaurant industry. when faced with higher minimum wages, firms might reduce the number of employees, resulting in poorer consumer service. alternatively, higher-paid workers might be more motivated to improve consumer service. using a combination of human annotation and several transformer models, we estimate the incidence of discussion of several service quality attributes (and their valence) in a textual data set of 97,242 online reviews of 1,752 restaurants posted over two years. we exploit a natural experiment in the county of santa clara, california, wherein only the city of san jose legislated a 25% minimum wage increase in 2013. by comparing restaurant reviews in san jose with those of synthetic controls, we find an improvement in the perceived service quality of san jose restaurants. specifically, we find reduced negative discussion of the courtesy and friendliness of workers. this decrease is present in independent restaurants and not in chains. this finding appears to be consistent with agency theory–based predictions of greater incentives to improve service in independent restaurants. we discuss alternative mechanisms for our results. we also discuss implications for consumers, restaurants, and policy makers.. agency theory minimum wages natural experiments service quality text analysis transformer models","r2m index 1.0: assessing the practical relevance of academic marketing articles. using text-mining, the authors develop version 1.0 of the relevance to marketing (r2m) index, a dynamic index that measures the topical and timely relevance of academic marketing articles to marketing practice. the index assesses topical relevance drawing on a dictionary of marketing terms derived from 50,000 marketing articles published in practitioner outlets from 1982 to 2019. timely relevance is based on the prevalence of academic marketing topics in practitioner publications at a given time. the authors classify topics into four quadrants based on their low/high popularity in academia and practice —“desert,” “academic island,” “executive fields,” and “highlands”—and score academic articles and journals: journal of marketing has the highest r2m score, followed by marketing science, journal of marketing research, and journal of consumer research. the index correlates with practitioner judgments of practical relevance and other relevance measures. because the index is a work in progress, the authors discuss how to overcome current limitations and suggest correlating the index with citation counts, altmetrics, and readability measures. marketing practitioners, authors, and journal editors can use the index to assess article relevance, and academic administrators can use it for promotion and tenure decisions (see www.r2mindex.com). the r2m index is thus not only a measurement instrument but also a tool for change.. information retrieval marketing marketing practice marketing theory relevance topic modeling","big social data and customer decision making in vegetarian restaurants: a combined machine learning method. customers increasingly use various social media to share their opinion about restaurants service quality. big data collected from social media provides a data platform to improve the service quality of restaurants through customers' online reviews, where online reviews are a trustworthy and reliable source that helps consumers to evaluate food quality. developing methods for effective evaluation of customer-generated reviews of restaurant services is important. this study develops a new method through effective learning techniques for customer segmentation and their preferences prediction in vegetarian friendly restaurants. the method is developed through text mining (latent dirichlet allocation), cluster analysis (self organizing map) and predictive learning technique (classification and regression trees) to reveal the customer’ satisfaction levels from the service quality in vegetarian friendly restaurants. based on the obtained results of our experiments on the data vegetarian friendly restaurants in bangkok, the models constructed by classification and regression trees were able to give an accurate prediction of customers' preferences on the basis of restaurants' quality factors. the results showed that customers’ online reviews analysis can be an effective way for customers segmentation to predict their preferences and help the restaurant managers to set priority instructions for service quality improvements.. food quality online reviews segmentation text mining vegetarian friendly restaurants","marketing ideas: how to write research articles that readers understand and cite. academia is a marketplace of ideas. just as firms market their products with packaging and advertising, scholars market their ideas with writing. even the best ideas will make an impact only if others understand and build on them. why, then, is academic writing often difficult to understand? in two experiments and a text analysis of 1,640 articles in premier marketing journals, this research shows that scholars write unclearly in part because they forget that they know more about their research than readers, a phenomenon called “the curse of knowledge.” knowledge, or familiarity with one’s own research, exacerbates three practices that make academic writing difficult to understand: abstraction, technical language, and passive writing. when marketing scholars know more about a research project, they use more abstract, technical, and passive writing to describe it. articles with more abstract, technical, and passive writing are harder for readers to understand and are less likely to be cited. the authors call for scholars to overcome the curse of knowledge and provide two tools—a website (writingclaritycalculator.com) and a tutorial—to help them recognize and repair unclear writing so they can write articles that are more likely to make an impact.. citations methods readability relevance text analysis writing","connecting with the future: the role of science fiction movies in helping consumers understand privacy-technology trade-offs. this article examines the ways in which sci-fi films help consumers understand the tradeoffs between personal privacy concerns and other societal concerns that arise due to new technologies. drawing upon media priming theory, the authors present a conceptual framework and accompanying research questions that suggest how priming from a futuristic movie influences consumers sentiment toward technology and privacy, from its release date and throughout its availability on movie streaming services. through a series of studies, including qualitative analysis and text-based sentiment analysis of reviews in the imbd database, the authors provide evidence to support this theoretical conceptualization. the findings demonstrate that online reviews are useful as a data source to gauge emotional intensity and text analysis of science fiction film reviews provides a useful methodology for understanding consumer perceptions about the future. science fiction movies may help consumers frame privacy-technology tradeoffs and take action to protect their privacy.. consumer privacy future qualitative and text analysis technology","reading between the lines: understanding customer experience with disruptive technology through online reviews. a customer’s experience with a brand, as evidenced in online customer reviews, has attracted multidisciplinary scholarly attention. customer experience plays an important role as an antecedent to brand engagement, brand adoption, and eventual brand loyalty. thus, it is important for businesses to understand their customers’ experiences so that they can make changes as necessary. the covid-19 pandemic has brought unprecedented changes to the business landscape, forcing businesses to move online, with many utilizing enterprise video conferencing (evc) to maintain daily operations. to ensure efficient digitization, many turned to the online reviews of others’ experiences with evc before engaging with it themselves. this research examined how the customer experience is portrayed through emotional tone and word choice in online reviews for the evc platform zoom. using computerized text analysis, key differences were found in the emotional tone and word choice for low- and high-rated reviews. the complexity and emotionality expressed in reviews have implications on the usability of the review for others. the results from this study suggest that online customer reviews with a high rating express a higher level of expertise and confidence than low-rated reviews. given the potential dissemination and impact, digital marketers may be well advised to first and foremost respond to online reviews that are high in emotional tone.. artificial intelligence computerized text analysis customer experience disruptive technology enterprise video conferencing online reviews zoom","what makes content engaging? how emotional dynamics shape success. some cultural products (e.g., books and movies) catch on and become popular, while others fail. why? while some have argued that success is unpredictable, we suggest that period-to-period shifts in sentiment - what we term sentiment volatility - enhance engagement. automated sentiment analysis of over 4,000 movies demonstrates that more volatile movies are evaluated more positively. consistent with the notion that sentiment volatility makes experiences more stimulating, the effect is stronger in genres where evaluations are more likely to be driven stimulation (i.e., thrillers rather than romance). further, analysis of over 30,000 online articles demonstrate that people are more likely to continue reading more volatile articles. by manipulating sentiment volatility in follow-up experiments, we underscore its causal impact on evaluations, and provide evidence for the role of stimulation in these effects. taken together, the results shed light on what drives engagement, the time dynamics of sentiment, and cultural analytics or why some cultural items are more successful.. automated textual analysis experiences movies narratives natural language processing","artificial intelligence in marketing: a bibliographic perspective. the concept of artificial intelligence (ai) was born in the mid-twentieth century to describe endeavours in computer science focusing on the simulation of human learning. since then, advances in computing, data collection and data storage have made ai an increasingly important area for researchers and practitioners across a range of disciplines in business and the social sciences. despite that, there appears to be very few attempts at cataloging and condensing prior research in this area. as such, this paper uses the vosviewer data visualizer to determine the major authors in the field and to identify themes and concepts emanating from prior research.. ai artificial intelligence bibliographic data machine learning natural language processing sentiment analysis","building a sustainable brand image in luxury fashion companies. building a sustainable brand image for a luxury fashion company is a very complex and tangled marketing process. brand image is a set of perceptions and beliefs surrounding a particular brand fixed in the mind of the consumer. it represents the result of the associations that the consumer recognizes in a brand and summarizes the positioning, personality and reputation of the brand itself. companies can derive fundamental insights by analysing the associations and how they are formed and, in particular, whether they are related to their positioning choices. in the luxury fashion business, brand image communication is often delegated to offline events to convey specific values, ideals and messages to consumers and build brand associations. the salvatore ferragamo museum, in particular, with its \"sustainable thinking\" exhibition, is seen as the emblem of this vision, the goal of which is to deliver a specific concept of a sustainable brand image to its customers. the objective of this empirical research was to seek correspondences or discrepancies between consumer brand perceptions and the identity conveyed by the ferragamo brand in terms of sustainability. by using a netnographic and text mining methodology we found that the consumer associates the brand with a company committed to the environment and involved in social issues; a company that traditionally shows creative and experimental skills, using unusual materials, artisanry, innovative techniques and unique designs. the image that the company wants to convey, however, also includes other characterizations. specifically, the company considers the \"made in italy\" aspect, the importance of its history and its family identity as decisive associations. these are all elements that, as far as this survey is concerned, were not found in the consumer perspective. as a result, from the comparison between the brand associations of both subjects, gaps emerge on which the company must intervene by designing new communication strategies or correcting existing ones in order to improve brand image and the overall brand value in terms of sustainability.. brand identity brand image luxury brands netnography sustainability text-mining","application of automated text analysis to examine emotions expressed in online support groups for quitting smoking. online support groups offer social support and an outlet for expressing emotions when dealing with health-related challenges. this study examines whether automated text analysis of emotional expressions using linguistic inquiry and word count (liwc) can identify emotions related to abstinence expressed in online support groups for quitting smoking, suggesting promise for offering targeted mood management to members. the emotional expressions in 1 month of posts by members of 36 online support groups were related to abstinence at month end. using the available liwc dictionary, posts were scored for overall positive emotions, overall negative emotions, anxiety, anger, sadness, and an upbeat emotional tone. greater expressions of negative emotions, and specifically anxiety, related to nonabstinence, while a more upbeat emotional tone related to abstinence. the results indicate that automated text analysis can identify emotions expressed in online support groups for quitting smoking and enable targeted delivery of mood management to group members.. na","the power of indirect appeals in peer-to-peer fundraising: why “s/he” can raise more money for me than “i” can for myself. the proliferation of peer-to-peer fundraising platforms (e.g., gofundme, rally, fundly) poses conceptual and substantive challenges for behavior scientists and fundraisers. this article explores how fundraisers should craft their appeals to maximize their chance of success. four field- and laboratory-studies find that direct appeals (i.e., narratives written in the first person by the intended recipient) raise less money than otherwise-identical indirect appeals (i.e., narratives written in the third person, seemingly by a third party on behalf of the intended recipient). the cause? prospective donors ascribe lesser (greater) credibility to direct (indirect) appeals, which in turn curtails (increases) their giving. since the narrative voice (direct vs. indirect) in which appeals are crafted is often discretionary (i.e., adjustable), our findings offer prescriptive guidelines for fundraisers.. altruism charitable behavior peer-to-peer charitable-fundraising pronouns prosocial behavior psychology of giving text-mining","from pirates to subscribers: 20 years of music consumption research. the last two decades have witnessed an increasing scholarly interest on music consumption. this interest can be explained, at least partly, to the relevance of music as a form of cultural consumption and the profound changes the sector has undergone. this paper performs a bibliometric analysis of the literature on music consumption research. in doing so, a database comprising 455 academic documents on the fields of business, economics, and management, was reviewed following a systematic procedure. through it we identify the intellectual roots and the methodological evolution of the field. furthermore, text mining was applied to analyse the themes included in the research agenda and their evolution. as a result, potential topics, approaches, and methods for future music consumption research are proposed.. bibliometric coupling bibliometrics co-citation live music music consumer recorded music text mining thematic analysis web of science","segmentation of both reviewers and businesses on social media. employing online consumer reviews, this research develops a market segmentation procedure that is feasible to businesses present on social media. because online reviews typically encompass large numbers of both reviewers and businesses, this data structure allows for both reviewer segmentation and business segmentation. this two-side segmentation approach segments not only reviewers in the preferences expressed in their reviews, but also businesses in their business practices specified in the reviews. whereas common existing segmentation approaches predominantly use survey and transaction data, the proposed procedure uses publicly available and detailed consumption information in such reviews. a large number of product features elicited from such reviews lead to rich and detailed profiling of both reviewer segments and business segments. using restaurant reviews on yelp, this research demonstrates how the proposed procedure can help businesses develop segmentation strategies on social media.. business segmentation market segmentation reviewer profiling social media text mining","double dutch finally fixed? a large-scale investigation into the readability of mandatory financial product information. with the introduction of short-form disclosure documents, financial regulation in the eu emphasizes the use of plain language to facilitate comprehensibility. we evaluate whether these documents and the accompanying plain language guidelines improve the readability of mandatory product information addressed to mutual fund investors. applying advanced text mining algorithms, we benchmark the readability of product information by means of objective and readily replicable methods. while mutual fund information on average does not come in plain language, we find that readability improved significantly following the introduction of key investor information documents (kiids). improvements are driven by simpler syntax and writing style. by contrast, the authors find that the use of jargon remains pervasive and report noncompliance with mandatory design requirements. we discuss our results and propose potential disclosure improvements.. information model investor protection mandatory information disclosure readability regulation retail finance","incentives increase relative positivity of review content and enjoyment of review writing. a series of controlled experiments examine how the strategy of incentivizing reviews influences consumers’ expressions of positivity. incentivized (vs. unincentivized) reviews contained a greater proportion of positive relative to negative emotion across a variety of product and service experiences (e.g., videos, service providers, consumer packaged goods companies). this effect occurred for both financial and nonfinancial incentives and when assessing review content across multiple natural language processing tools and human judgments. incentives influence review content by modifying the experience of writing reviews. that is, when incentives are associated with review writing, they cause the positive affect that results from receiving an incentive to transfer to the review-writing experience, making review writing more enjoyable. in line with this process, the effect of an incentive on review positivity attenuates when incentives are weakly (vs. strongly) associated with review writing (i.e., incentive for “participating in an experiment” vs. “writing a review”) and when the incentive does not transfer positive affect (i.e., when an incentive is provided by a disliked company). by examining when incentives do (vs. do not) adjust the relative positivity of written reviews, this research offers theoretical insight into the literature on incentives, motivation, and word of mouth, with practical implications for managers.. enjoyment incentives motivation natural language processing reviews word of mouth","happiness begets money: emotion and engagement in live streaming. live streaming offers an unprecedented opportunity for content creators (broadcasters) to deliver their content to consumers (viewers) in real time. in a live stream, viewers may send virtual gifts (tips) to the broadcaster and engage with likes and chats free of charge. these activities reflect viewers’ underlying emotion and are likely to be affected by the broadcaster’s emotion. this article examines the role of emotion in interactive and dynamic business settings such as live streaming. to account for the possibility that broadcaster emotion, viewer emotion, and viewer activities influence each other, the authors estimate a panel vector autoregression model on data at the minute level from 1,450 live streams. the results suggest that a happier broadcaster makes the audience happier and begets intensified viewer activities, in particular tips. in addition, broadcasters reciprocate viewer engagement with more smiles. further analyses suggest that these effects are pronounced only after a live stream has been active for a while, and they manifest only in streams by broadcasters who have more experience, receive more tips, or are more popular in past live streams. these results help platforms and broadcasters optimize marketing interventions such as broadcaster emotion enhancement in live streaming and quantify the financial returns.. digital marketing emotion engagement live streaming social media text analysis video analysis","bert: a sentiment analysis odyssey. the study investigates relative effectiveness of four sentiment analysis techniques: (1) unsupervised lexicon-based model using sentiwordnet, (2) traditional supervised machine learning model using logistic regression, (3) supervised deep learning model using long short-term memory (lstm), and (4) advanced supervised deep learning model using bidirectional encoder representations from transformers (bert). publicly available labeled corpora of 50,000 movie reviews originally posted on internet movie database (imdb) were analyzed. sentiment classification performance was calibrated on accuracy, precision, recall, and f1 score. the study puts forth two key insights: (1) relative efficacy of four sentiment analysis algorithms and (2) undisputed superiority of pre-trained advanced supervised deep learning algorithm bert in sentiment classification from text. the study is of value to analytics professionals and academicians working on text analysis as it offers critical insight regarding sentiment classification performance of key algorithms, including the recently developed bert.. bert logistic regression lstm sentiment analysis sentiwordnet","content analysis of fake consumer reviews by survey-based text categorization. as the influence of online consumer reviews grows, deceptive reviews are a worsening problem, betraying consumers' trust in reviews by pretending to be authentic and informative. this research identifies factors that can separate deceptive reviews from genuine ones. first, we create a novel means of detection by contrasting authentic versus fake word patterns specific to a given domain (e.g., hotel services). we use a survey on a crowdsourcing platform to obtain both genuine and deceptive reviews of hotels. we learned the word patterns from each category to discriminate genuine reviews from fake ones for positively and negatively evaluated reviews, respectively. we show that our all terms procedure outperforms current benchmark methods in computational linguistics and marketing. our extended analysis reveals the factors that determine fake reviews (e.g., a lack of details, present- and future-time orientation, and emotional exaggeration) and the factors influencing people's willingness to write fake reviews (including social media trust, product quality consciousness, deal proneness, hedonic and utilitarian consumption, prosocial behavior, and individualism). we also use our procedure to analyze more than 250,000 real-world hotel reviews to detect fake reviews and identify the hotel and review characteristics influencing review fakery in the industry (e.g., star rating, franchise hotel, hotel size, room price, review timing, and review rating).. fake review hotel liwc online consumer review survey text mining","style and fit customization: a web content mining approach to evaluate online mass customization experiences. purpose: this study intends to examine consumers' fashion customization experiences through a web content mining (wcm) approach. by applying the theory of customer value, this study explores the benefits and costs of two levels of mass customization (mc) to identify the values derived from style (i.e. shoe customization) and fit customization experiences (i.e. apparel customization) and further to compare the dominating dimensions of value derived across style and fit customization. design/methodology/approach: a wcm approach was applied. also, two case studies were conducted with one focusing on style customization and the other focusing on fit customization. the brand vans was selected to examine style customization in study 1. the brand sumissura was selected to examine fit customization in study 2. consumers' comments on customization experiences from these two brands were collected through social networks, respectively. after data cleaning, 394 reviews for vans and 510 reviews for sumissura were included in the final data analysis. co-occurrence plots, feature extraction and grouping were used for the data analysis. findings: the emotional value was found to be the major benefit for style customization, while the functional value was indicated as the major benefit for fit customization, followed by ease of use and emotional value. in addition, three major themes of costs, including unsatisfied service, disappointing product performance and financial risk, were revealed by excavating and evaluating consumers' feedback of their actual clothing customization experiences with sumissura. originality/value: this study initiates the effort to use web mining, specifically, the wcm approach to thoroughly investigate the benefits and costs of mc through real consumers' feedback of two different types of fashion products. the analysis of this study also reflects the levels of customization: style and fit. it provides an in-depth text analysis of online mc consumers' feedback through the use of feature extraction analysis and word co-occurrence networks.. fit customization mass customization real experiences style customization the theory of customer value web content mining","understanding retail quality of sporting goods stores: a text mining approach. purpose: sporting goods retailing is a significant sector within the sport industry with the total revenue of this sector reaching $52.2 billion in 2018. beset with formidable competition, sporting goods stores are compelled to augment their merchandise with service and improve retail quality. the purpose of this study is to investigate retail quality of sporting goods stores (rqsgs). design/methodology/approach: based on 27,793 online reviews of 1481 stores in the united states, this study used leximancer 4.0, a text mining software, to identify critical retail quality dimensions associated with sporting goods stores, and further explored the most salient dimensions among different levels of ratings. findings: customer service and store aspects are the two higher-order dimensions of rqsgs; holistic experience, manager and staff are three themes under customer service, and product, b&m store and online–offline integration are three themes under store aspects. furthermore, extreme reviews focus more on customer service, whereas lukewarm reviews focus more on store aspects. practical implications: knowledgeable staff, managers and online–offline integration are instrumental in creating superior retail quality. sporting goods stores should enhance hedonic and social values for consumers in order to ward off online competitions. originality/value: this study explored retail quality dimensions that are pertinent to sporting goods retailing utilizing text mining methods. this study to certain extent cross-validated the existing retailing literature that is developed on alternative methods.. e-commerce leximancer retail quality sporting goods stores text mining","understanding changes in a brand’s core positioning and customer engagement: a sentiment analysis of a brand-owned facebook site. the increasing power of social media has created unprecedented opportunities for marketers. in particular, brand-owned social media seems to be an increasingly popular way of enhancing a brand’s position, connecting with customers, and improving customer engagement with the brand. to guide strategic marketing communication decision-making on social media, the current study extends the relationship communication model and offers an analytical workflow to gain new insights from unstructured textual data available on brand-owned social media. the workflow utilizes an eclectic mix of analytical tools such as word clouds, and cluster and word association analyses, which collectively allow for identification of main topics and their temporal evolution in unstructured textual data from a brand’s social media. in doing so, the proposed workflow offers researchers and practitioners a step by step procedure to make sense of such textual data, which may prove unwieldy and overwhelming otherwise. furthermore, to manifest the utility of the proposed workflow, it is applied to illustrative data collected from a brand’s facebook page. results from this example analysis point to a slight fading of the brand’s perceived core position as an event avenue, as well as an evolution of customer sentiments that may reflect different levels and types of customer engagement with the page. finally, we discuss the implications of our findings for research and brand management practice, as well as the study’s limitations and future research opportunities.. cluster analysis nlp relationship communication sentiment analysis social media word association analysis","political marketing with data analytics. social media played a significant role in past presidential elections, and it is very likely that this form of communication will continue to influence political campaigns. can analytics uncover the linguistic “plot arcs” and resulting sentiment or emotion in political text? this paper examines how natural language processing (nlp) and data visualization tools and methods in analytics can play a key role in marketing political candidates. using publicly available text messages, the authors employ nlp techniques to transform the text observations from the past campaigns of hillary clinton, barack obama, and donald trump into a linguistic “corpus” and story arc visualizations. the methodology includes the use of syuzhet and latent dirichlet allocation (lda) models. the resulting data visualizations reveal the story arcs associated with the candidate’s communications, and they provide a means to analyze the unbiased political sentiment or hidden emotion in the text. in an analysis of the results, the authors found distinctly different story arcs and vocabulary usage among the three presidential candidates. the contribution to the literature is a methodology for extracting the story and the resulting sentiment from text messages for marketing campaigns. the authors suggest that the techniques used in this paper can assist future research on marketing other products or services that utilize computer-mediated communications.. analytics natural language processing political marketing text mining visualization story arcs","how concrete language shapes customer satisfaction. consumers are often frustrated by customer service. but could a simple shift in language help improve customer satisfaction? we suggest that linguistic concreteness-the tangibility, specificity, or imaginability of words employees use when speaking to customers-can shape consumer attitudes and behaviors. five studies, including text analysis of over 1,000 real consumer-employee interactions in two different field contexts, demonstrate that customers are more satisfied, willing to purchase, and purchase more when employees speak to them concretely. this occurs because customers infer that employees who use more concrete language are listening (i.e., attending to and understanding their needs). these findings deepen understanding of how language shapes consumer behavior, reveal a psychological mechanism by which concreteness impacts person perception, and provide a straightforward way that managers could help enhance customer satisfaction.. concreteness customer service language listening social perception","reviewing experts' restraint from extremes and its impact on service providers. this research investigates reviewing experts on online review platforms. the main hypothesis is that greater expertise in generating reviews leads to greater restraint from extreme summary evaluations. the authors argue that greater experience generating reviews facilitates processing and elaboration and enhances the number of attributes implicitly considered in evaluations, which reduces the likelihood of assigning extreme summary ratings. this restraint-of-expertise hypothesis is tested across different review platforms (tripadvisor, qunar, and yelp), shown for both assigned ratings and review text sentiment, and demonstrated both between (experts vs. novices) and within reviewers (expert vs. pre-expert). two experiments replicate the main effect and provide support for the attribute-based explanation. field studies demonstrate two major consequences of the restraint-of-expertise effect. (i) reviewing experts (vs. novices), as a whole, have less impact on the aggregate valence metric, which is known to affect page-rank and consumer consideration. (ii) experts systematically benefit and harm service providers with their ratings. for service providers that generally provide mediocre (excellent) experiences, reviewing experts assign significantly higher (lower) ratings than novices. this research provides important caveats to the existing marketing practice of service providers incentivizing reviewing experts and provides strategic implications for how platforms should adopt rating scales and aggregate ratings.. expertise online word-of-mouth platform strategy sentiment analysis text analysis user rating average","past, present, and future of electronic word of mouth (ewom). communication platforms are undergoing a transition from physical to digital spaces. the internet has radically changed the business scenario wherein people have become the “media” for collaborating to share information. practitioners have started using electronic word of mouth (ewom) for consumer insight through text analytics, sentiment, hashtag analytics, and other machine learning tools. due to the growing importance of the topic as a research area, it is vital to map the research domain. this paper uses performance analysis tools (quantitative and qualitative) and science mapping tools (conceptual and intellectual structures) for literature review and the identification of future research directions. the content analysis of emergent topics and themes offers directions for future researchers. recommender systems, data mining (artificial intelligence and machine learning), semantics, and sentiment analysis are emergent topics. we contribute to the ewom literature by canvassing state-of-the-art research and proposing future research directions.. conceptual structure electronic word of mouth (ewom) intellectual structure performance analysis","certainty in language increases consumer engagement on social media. brands regularly attempt to stimulate consumer engagement by posting messages on social media platforms (e.g., facebook), but their messages are often ignored. how can managers write social media messages that engage consumers? the present research sheds light on how the language of brand messages influences consumer engagement. text analyses of brand messages posted on facebook and twitter shows that brand messages that include words that express certainty (e.g., always, everything, forever) are associated with higher levels of consumer engagement (e.g., likes, comments, shares/retweets). the results of two controlled experiments demonstrate that messages expressing certainty increase engagement by influencing how consumers perceive brands. specifically, expressing certainty makes brands seem more powerful, and perceptions of power exert a positive effect on engagement. in addition, the indirect effect of certainty on consumer engagement is stronger (weaker) among consumers with higher (lower) power distance beliefs. these results assist marketing managers by demonstrating how language influences consumer engagement on social media.. na","using text mining to track changes in travel destination image: the case of macau. purpose: travel websites allow tourists to share their thoughts, beliefs and experiences regarding various travel destinations. in this paper, the researchers demonstrated an approach for destination marketing organisations to explore online tourist-generated content and understand tourists' perceptions of the destination image (di). specifically, the researchers initiated an investigation examining how the destination image of macau changed during the period of 2014–2018 based on user-generated content on travel websites. design/methodology/approach: web crawlers developed by python were employed to collect tourists' reviews from both ctrip and tripadvisor regarding the theme of “macau attraction”. a total of 51,191 reviews (41,352 from ctrip and 9,839 from tripadvisor) were collected and analysed using the text-mining technique. findings: the results reveal that the frequency of casino-related words decreased in reviews by both international and mainland chinese tourists. additionally, international and mainland chinese tourists perceive the di of macau differently. mainland chinese tourists are more sensitive to new attractions, while international tourists are not. the study also shows that there are differences between the government-projected di and the tourist-perceived di. only the “city of culture” and “a world centre of tourism and leisure” have built recognition with tourists. originality/value: given the easy accessibility of online information from various sources, it is important for destination marketing organisations to analyse and monitor different di perspectives and adjust their branding strategies for greater effectiveness. this study uncovered the online di of macau by using text mining and content analysis of two of the largest travel websites. by analysing and comparing the differences and relationships among the frequently used words of tourist-generated content on these websites, the researchers revealed some interesting findings with important marketing implications.. destination image online reviews text mining tourist-generated content","what improves customer satisfaction in mobile banking apps? an application of text mining analysis. consumer-generated reviews reflect consumers' experiences and perceptions toward a product or service. in this context, we propose a text mining approach to identify factors that improve customer satisfaction in the mobile banking app service. to do so, we collect 96,140 mobile app reviews for four u.s. banks: bank of america, capital one, chase, and wells fargo. using the latent dirichlet allocation (lda) topic model, we first derive the critical quality dimensions such as ease of use, convenience, security, and customer support. analysis of weekly panel data shows that positive responses to the security and convenience of mobile banking apps improve app ratings. however, increased comments about insecurity, negative customer support experiences, discomfort, and complexity lower user ratings. overall, the empirical results support that security is the most influential factor in customer satisfaction with mobile financial services.. customer reviews customer satisfaction financial services mobile banking application text mining","reconceptualizing and modeling sustainable consumption behavior: a synthesis of qualitative evidence from online education industry. in recent years, the concept of sustainable consumption behavior (scb) is largely introduced and changed continuously to be discussed with industries such as energy sector, agriculture, transportation, sharing economy, and tourism. however, the study of sustainable consumption behavior in the online education industry (scboei) remains a research gap. thus, this paper aims to identify the significance of sustainable consumption behavior and theoretically conceptualize scboei. this study employs content and text analysis to reconceptualize the major theories that contribute to the research of scb. the findings in this study show that scboei is contributed of great significance to promote sustainable development. in addition, this study introduces a model of scboei, in which the consumer value theory, social exchange theory, and planned behavior theory are integrated. additionally, this paper suggests that value, environmental attitude, identity, consumer engagement, and contextual factors (government behavior, market conditions, and environmental education) are highly important to extend the research on scboei. in conclusion, this study strongly recommends that scb research shall be directed to the online education industry. furthermore, future studies shall emphasize the empirical effects of psychological factors, activity factors, and contextual factors for scboei to find the correlation.. chongqing consumer environmental education identify scboei value","a critical review of international print advertisements: evolutionary analysis, assessment and elucidations, from 1965 to 2020. purpose: amidst the plethora of mass communication methods that technology bestowed business with, print advertisements still remain an effective and widely utilized advertising tool, and retain a diachronically venerable position in international marketing practice. bar and transcending mere academic fascination or curiosity, this research provides insights into the past, an understanding of the present and an outlook into the future. in this vein, through a methodical and comprehensive critical review of extant literature on print advertisements since 1965, this research aims to identify gaps in extant knowledge, to map its trends and divergences, to trace its paradigm shifts and to ultimately develop agendas for truly significant future research. design/methodology/approach: this spatial-temporal study reviews 256 methodically selected articles, using vantagepoint software, and adopts a novel methodology through natural language processing (nlp), text mining, auto-correlation maps, and bubble maps to conduct and present a robust analysis and explicit findings. findings: using also the vosviewer for density and network visualization, the results identify the predominant literature themes and, conversely, the relatively under-researched areas, and provide a more insightful collective interpretation of extant works, while laying the foundation for future research of greater value and significance to academia and industry. originality/value: this study transcends the partial and/or limited analyses and perspectives of extant literature to present scholars with the first comprehensive and long term meta-analysis or systematic study of print advertising, with explicit findings of both scholarly and executive worth.. evolution literature review mapping print advertisements research spatial-temporal study vantagepoint vosviewer","exploring the role of the amazon effect on customer expectations: an analysis of user-generated content in consumer electronics retailing. while amazon's disruption of the retail market has been associated with significant changes in consumer behavior, empirical studies on how interacting with amazon has changed customers' expectations toward other offline/online retailers remain scarce. such amazon-driven perceptions of service attributes are sometimes referred to as the ‘amazon effect’. after clarifying the meaning of the amazon effect and reviewing the studies on consumer complaints online, this paper aims to identify key triggers for the amazon effect from consumer comments on social media. based on natural language processing techniques, a content and sentiment analysis of users' comments drawn from the facebook pages of three leading consumer electronics retailers in italy over a two-year span (2016–2018) was used to evaluate the dissatisfaction toward these retailers associated to amazon-related service attributes. the findings show that there is a wide diffusion of consumer comments and service complaints related to the amazon effect on consumer electronics retailers, especially regarding price, customer service, in-store staff, and post-purchase support. compared with corresponding evaluations on the italian amazon website, the negative sentiments revealed in consumers' comments on facebook suggest that the amazon's service standards have raised consumer expectations and have made consumers less satisfied when they interact with other retailers. we argue the need for further research to better clarify amazonification in terms of customer impatience and dissatisfaction in general, also going beyond price and logistics issues, which are usually considered as the main constitutive factors.. na","is this the beginning of the end for retail websites? a professional perspective. this paper expects to understand professionals' opinion concerning the impact of the increasing use of social media (sm) and commercial mobile applications (ma) instead of retail websites in their online strategy. unstructured interviews with internet professionals were applied on the linkedin professional sm platform, and 127 professionals provided their perspective. data were analysed using a text mining approach and the outcome revealed professionals' resistance to set sm in the centre of the online strategy and highlighted the preference of users to use search engines that, in turn, will lead them to a retail website.. commercial mobile application consumer behaviour retail website social media text mining.","past, present, and future research on self-service merchandising: a co-word and text mining approach. purpose: this study aims to discern emerging trends and provide a longitudinal perspective on merchandising research by identifying relationships between merchandising-related subdomains/themes. design/methodology/approach: this study sourced 657 merchandising-related articles published since 1960, from the scopus database and 425 from web of science. after processing and normalizing the data, this study performed co-word and thematic network analyses. taking a text mining approach, this study used topic modeling to identify a set of coherent topics characterized by the keywords of the articles. findings: this study identified the following merchandising-related themes: branding, retail, consumer, behavior, modeling, textile and clothing industry and visual merchandising. although visual merchandising was the first type of merchandising to be used in-store, only recently has it become an emerging topic in the academic literature. there has been a further trend over the past decade to understand the adoption of simulation technology, such as computer-aided design, particularly in supply chain management in the clothing industry. these and other findings contribute to the discussion of the merchandising concept, approached from an evolutionary perspective. research limitations/implications: the conclusions of this study hold implications at the intersection of merchandising, sectors, new technologies, research methodologies and merchandising-practitioner education. research trends suggest that, in the future, virtual reality and augmented reality using neuroscientific methods will be applied to the e-merchandising context. practical implications: the different dimensions of merchandising can be used to leverage store managers’ decision-making process toward an integrated store-management strategy. in particular, by adopting loyalty merchandising tactics, the store can generate emotional attachment among consumers, who will perceive its value and services as unique, thanks to merchandising items designed specifically with that aim in mind. the stimulation of unplanned purchases, the strategic location of products and duration of each merchandising activity in the store, the digitalization of merchandising and the application of findings from neuroscience studies are some of the most relevant practical applications. originality/value: this study provides the first-ever longitudinal review of the state of the art in merchandising research, taking a holistic perspective of this field of knowledge spanning a 60-year period. the work makes a valuable contribution to the development of the marketing discipline.. bibliometric analysis co-word analysis point-of-sale marketing scopus web of science","small donor contributions in response to email outreach by a political campaign. this research examines variation in response to mass email communications by a large and sophisticated political campaign over the course of the 2018 election cycle. computational text analysis is utilized to generate and catalogue the most frequent themes across a body of 690 separate email messages. we find open rates vary somewhat by the subject key words of messages as well as when they were sent. in addition, small donations received through email are also responsive to the contents of messages, controlling for timing and other relevant factors. given the very low cost associated with using email to communicate with supporters, it appears to be an effective means for harvesting small contributions and of providing opportunities for large numbers of voters to elevate their level of participation in the electoral process.. campaign contributions campaign strategy digital strategy email political campaigns political participation small donations text analysis","structural topic modelling segmentation: a segmentation method combining latent content and customer context. this research introduces a method for segmenting customers using structural topic modelling (stm), a text analysis tool capable of capturing topical content and topical prevalence differences across customers while incorporating metadata. this approach is particularly suitable for contexts in which textual data is either a critical component or is the only data available for segmentation. the ability to incorporate metadata by using stm provides better clustering solutions and supports richer segment profiles than can be produced with typical topic modelling approaches. we empirically illustrate the application of this method in two contexts: 1) a context in which related metadata is readily available; and 2) a context in which metadata is virtually non-existent. the second context exemplifies how ad-hoc generated metadata can increase the utility of the method for identifying distinct segments.. cluster analysis market segmentation social media strategic marketing structural topic modelling","how features embedded in ewom predict hotel guest satisfaction: an application of artificial neural networks. electronic-word-of-mouth (ewom) plays a crucial role not only in analyzing customer decision-making but also in enhancing the revenue and brand image of service providers in the market. understanding guest satisfaction is one of the important aspects because it augments the guest experience and their future intention to book. many studies carried out in past applied regression-based and qualitative methods for analyzing the satisfaction levels of guests, but data analytics methods are still lacking in the literature and how these types of approaches can be applied to benefit hoteliers has not been completely explored. the objective of this study is to identify the important features that predict hotel guest satisfaction (hgs) through the ann-based hgs prediction model. the feature importance with respect to guest satisfaction has also been estimated and discussed. we have discussed the managerial and theoretical implications of our research. recommendations for the hospitality industry are also provided.. ann ewom feature importance guest satisfaction online reviews text analytics","capturing marketing information to fuel growth. marketing is the functional area primarily responsible for driving the organic growth of a firm. in the age of digital marketing and big data, marketers are inundated with increasingly rich data from an ever-expanding array of sources. such data may help marketers generate insights about customers and competitors. one fundamental question remains: how can marketers wrestle massive flows of existing and nascent data resources into coherent, effective growth strategies? against such a backdrop, the marketing science institute has made “capturing information to fuel growth” a top research priority. the authors begin by discussing the streetlight effect—an overreliance on readily available data due to ease of measurement and application—as contributing to the disconnect between marketing data growth and firm growth. they then use the customer equity framework to structure the discussion of six areas where they see substantial undertapped opportunities: incorporating social network and biometric data in customer acquisition, trend and competitive interaction data in customer development, and unstructured and causal data in customer retention. the authors highlight challenges that obstruct firms from realizing such data-driven growth opportunities and how future research may help overcome those challenges.. analytics biometrics competitive intelligence field experiments growth social network text analysis trendspotting","social media influencers: a route to brand engagement for their followers. social media influencers (smis) are micro-celebrities with large followings on social media platforms who engage consumers and hold the potential to promote customer-brand relationships across different product categories. smis have an existing relationship of trust with consumers, and consumers seek out the content created by smis for valuable information and advice. this study explores the process of brand engagement between consumers and brands in the digital content marketing environment, specifically examining the research question: do smis act as a route to brand engagement for their followers? the context for this study is the beauty community on youtube; over 60,000 user comments were analyzed through automated text analysis. this study is among the first to provide empirical evidence that smis do act as a route to brand engagement through the three dimensions of cognitive processing, affection and activation.. automated textual analysis brand engagement social influence social media marketing","an empirical study on anthropomorphism and engagement with disembodied ais and consumers' re-use behavior. the integration of artificial intelligence (ai) into the human race has gradually become a norm. ai entails technology assemblages such as machine learning, natural language processing, and reasoning. the influence of ai systems has intensified in consumers' daily lives. many consumers have interacted with the notions of ai through advertisements or having personal experiences. many consumers are curious about the use of ai. this paper reports three studies conducted to determine whether anthropomorphism (anth) and engagement play a role in consumers' intention to re-use a voice assistant (va; a machine-learning ai). the second study will determine if anth and engagement play a role when consumers are using the vas for different activities (task completion vs. information gathering). in addition, in study 2, actual re-use behavior was also tested in the model, which encouraged a stronger overall model fit. the results show that in general effort expectation (ee) has a strong positive impact on consumers' usage experience of the va. however, between the two types of activities, ee has a stronger impact on consumers' usage experience for information-gathering activities, whereas performance expectation has a stronger impact on usage experience when consumers use the va for task-completion purposes. the third study used internet usage experience as a moderating variable to determine the boundaries of the mediating effects in the study. the results show that the mediators yield results similar to prior studies.. anthropomorphism artificial intelligence engagement intention to re-use realism maximization theory","exploring values affecting e-learning adoption from the user-generated-content: a consumption-value-theory perspective. the aim of this study is to utilise the user-generated content from social media platforms and merchandise websites to explore various values affecting behavioural intention in context of e-learning services from the consumption-value-theory perspective. this study has utilised a novel mixed-method approach based on natural language processing (nlp) techniques for the both the qualitative and quantitative analysis. this study has used user-generated content of coursera (an e-learning service) consisting of online reviews from coursera-100 k-dataset and tweets about coursera. some of the important themes generated from the thematic-based analysis of tweets are ‘value addition’, ‘course content’, ‘topic cover’, ‘reliability of course’, ‘course quality’, ‘enjoyed course’, ‘recommend the course’, ‘value for money’, ‘facilitator skills’, etc. results of the empirical study reveal that offers and deals, emotional connect, facilitator quality, course reliability, platform innovativeness, and compatibility are important predictors of behavioural intention. this study concludes with the various limitations and future directions.. consumption value theory (cvt) e-learning services latent dirichlet allocation (lda) natural language processing (nlp)-based approach topic-modeling user generated data","lines of scientific research in the study of blood donor behavior from a social marketing perspective. although blood is essential in healthcare systems for medical and surgical use, it is still a scarce resource. given that blood cannot be produced artificially, donors are the backbone of the system, which is why it is crucial for transfusion centers to understand the factors that determine their behavior. the goal of this study is to help decision-makers at transfusion understand what lines of research have been developed in the literature and which ones might be useful to define and assess actions related to the attributes of the donation system and donor behavior. to that end, this work aims to present an overview of the available literature on blood donor behavior from a social marketing perspective, which is of paramount importance in the context of blood donation. based on the results of this review, which was performed by using the text mining methodology, this study presents current lines of investigation, and proposes additional future lines.. blood donation blood donor behavior content analysis social marketing text mining","gender and the cmo: do the differences make a difference?. despite efforts to address the lack of female representation in executive leadership roles, the number of women in leadership roles is still low in comparison to men. data in the form of online interviews were collected for a sample of 69 cmos from the world’s most influential cmo’s report by forbes magazine and analyzed using the automated text analysis software, liwc, employing the dimensions of analytic, clout, authentic, and tone. a series of anovas was conducted to determine the impact of gender on these dimensions. clout and authentic were significantly impacted by the gender of the cmo. the paper considers two gender communication theories that could potentially explain these differences. the paper concludes by discussing managerial implications, acknowledging the limitations, and identifying avenues for future research.. chief marketing officer cmo gender liwc","a consumer engagement systematic review: synthesis and research agenda. purpose: this paper aims to review the existing literature about consumer engagement, provide an accurate mapping of this research field, propose a consumer engagement typology and a conceptual framework and offer a research agenda for this domain. design/methodology/approach: a systematic literature review using several quality filters was performed, producing a top-quality pool of 41 papers. after that, a text mining analysis was conducted, and five major research streams emerged. findings: this paper proposes five distinct research streams based on the text mining analysis, namely, consumer engagement, online brand community engagement, consumer-brand engagement, consumer engagement behaviours and media engagement. based on this, a consumer engagement typology and a conceptual framework are suggested and a research agenda is proposed. originality/value: this paper presents scientific value and originality because of the new character of the topic and the research methods used. this research is the first study to perform a systematic review and using a text-mining approach to examine the literature on consumer engagement. based on this, the authors define consumer engagement typology. a research agenda underlining emerging future research topics for this domain is also proposed.. consumer engagement consumer engagement typology consumer-brand engagement online brand community engagement systematic literature review text mining","social media listening. from text analysis to the interpretation of the meanings. the research methods based on the analysis of users-generated contents are an increasingly widespread practice in some companies. these contents analysis is able to observe the «living voice» of customers/users because they represent more spontaneous opinions and preferences than those found through traditional data collection settings. this paper discusses a qualitative-quantitative methodological approach for the study of users-generated contents. the proposed method combines the outputs that emerge from automated social media monitoring platforms, based on the semantic analysis, with an ethnographic approach deriving from the analysis of online contexts in which users' opinions are shared. the methodological model adopts an integrated and multi-methods approach and it is divided into three phases (con-textual analysis, textual analysis, extra-textual analysis). the method was applied to a case study of an italian wine brand: the study aims to analyze the meanings associated with the brand, evaluating the alignment between the brand values and the brand image that it disseminates as perceived by users in their online conversations.. brand analysis brand value online marketing research social media monitoring text analysis","how safety affects destination image projected through online travel reviews. destination image is a key factor for attracting tourists and wealth to destinations. with the spread of web 2.0 and traveller-generated content, destination management organisations do not control the information regarding destination image formation through its cognitive, affective and conative components. the aim of the study is to analyse, using natural language processing techniques, whether the terrorist attack of august 2017 and the catalan sovereignty process, which took place during the last quarter of 2017 in barcelona, harmed the online destination image perceived and transmitted by tourists, through language recognition, frequency analysis and term categorisation of more than 150,000 online travel reviews, written in english, spanish, french, german or italian, posted on a shared-lodging platform. the findings show that visitors did not fear for their personal safety despite the seriousness and immense international media coverage of both events. the opinion of tourists, shared through online reviews, can help companies to improve the supply of goods and services.. airbnb barcelona big data analytics destination image online image-formation agent political instability sentiment analysis terrorism","negative customer experience in lifestyle hotels: a netnography perspective. the primary purpose of the present research is to develop a methodology which can accurately analyse online public reviews on google using netnography studies combined with text mining analyses. by analysing the current techniques applied to a lifestyle hotel brand in nine properties in different countries and carefully studying how negative reviews are expressed online by costumers, this study aims to create a pattern of lifestyle customer complaints. this research seeks to demonstrate patterns of consumer behaviour that are not fully satisfied with the hotel service and how it can negatively affect the brand. this study identifies the areas that five stars lifestyle hoteliers and hotel managers need to pay attention to improve services, considering online reviews on online platforms, such as social networks and other tourism sites. today, online reviews and customer experiences have a significant impact on the choice of a hotel.. na","social media marketing of it service companies: analysis using a concept-linking mining approach. the it service industry values the experience of social familiarity, which is based on routine interactions with suppliers and customers and is at the frontier of social media marketing. to further understand how it service companies use social media to engage their customers or potential leads, the objectives of this research are to delineate the differences between it service companies’ use of social media and users’ expectations based on knowledge extracted from user-generated content on twitter. we applied a text mining approach called two-tier concept-linking analysis to extract patterns in twitter posts from top it service companies as well as the related tweets from the twitter users. we further compare the yearly similarities and differences of the key concepts from the company's official account and from the users. besides, the key concepts between users’ expectations and it service companies’ social media use were compared on the basis of first-tier concepts and further elaborated by their corresponding second-tier concepts. our approach contributes to further understand the socio-cognitive insights underlying the dynamic social media data, from which organizations and individuals in charge can note the objectives they wish to maintain and the marketing gaps they wish to improve on.. concept links it service industry social media mining twitter","service quality in airport hotel chains through the lens of online reviewers. airport hotel chains target the specific and important segment of accommodation near airports, thus benefiting from travelers seeking to stay near an airport. this study addresses service quality by analyzing tripadvisor online reviews over units from both a high-end and a low-end chain in five european cities (amsterdam, brussels, frankfurt, london, paris). using text mining and topic modeling, ten heat matrices were drawn (one per unit) to summarize the main services characterizing the computed topics. seven hypotheses grounded on existing literature were tested, from which some interesting findings emerged (e.g., related to transfer services, staff, food and beverage, cleanliness, and punctuality). this study contributes to the standardization versus adaptation debate by unveiling a globalized strategy in staff management and breakfast services, while bar services adopt a localized strategy. transportation services, while not offered by the hotels, are frequently mentioned, which signals hoteliers to interact with local authorities to improve accessibilities.. airport hotel customer feedback hotel chain online reviews service quality","intensive wom-behavior in the healthcare sector – the case of an austrian hospital’s facebook site. patients frequently use facebook for health-related reasons, like seeking of information or the recommendation of practitioners or hospitals. in this way, facebook provides a powerful communication platform for electronic word-of-mouth (ewom). hospitals increasingly use facebook to positively influence the perception of their brand reputation and performance. the present explorative study provides detailed information regarding ewom behavior of persons on the facebook page of an upper austrian hospital. data of the hospital’s facebook page was gathered and analyzed with nodexl. by using a text analysis, we categorized the hospitals’ posts. reactions towards the different types of postings were analyzed by counting emojis, the number of shares and comments. within the study, there was an in-depth evaluation of communication data (313 posts of the hospital, more than 14,000 ewom actions by 3327 women, men and organizations). the study shows how heterogeneous users are in their ewom behavior and that a variety of topics on the facebook page stimulates electronic recommendations. one major finding of the study is that a significant part of the ewom is done by only a few users. according to this, a so-called intensive wom behavior (iwb) can be identified. users of the iwb-group behave heterogeneously. most react either with an emoji, a comment or a share. only a few iwb-users respond with a combination of these ewom-reactions. by providing first insights into the existence of iwb-users as well as their ewom-behavior, this study offers new insights to ewom in facebook.. ewom facebook healthcare hospital recommendation social media word-of-mouth","new product introductions for low-income consumers in emerging markets. facing growth pressures, firms attempt to target the large low-income consumer segment present in emerging markets. this multi-method study develops research insights regarding consumer-, retailer-, firm-, category- and country-level factors that enhance the acceptability, awareness, availability, and affordability of products that facilitate the low-income consumer adoption of and firms’ introduction of new products for low-income consumers. study 1 uses a qualitative grounded-theory approach by interviewing company managers and low-income consumers in india and chile. study 2, empirically tests an integrated multi-level model of several category factors identified in study 1, combined with country-level factors drawn from the literature, using a unique 12-year longitudinal panel dataset of new product introductions in 27 emerging market countries from africa, asia, eastern europe, and south america. the research identifies consumer aspirations, region-based versioning, visible packaging and the product demonstrations as critical motivating factors for adoption of products by low income consumers. consumers’ knowledge of the product category, the concentration of branded products, availability of global brands, and the presence of traditional retail stores motivate firms to launch products for low income consumers in emerging markets.. consumer aspirations emerging markets low-income consumers multi-method approach multilevel model natural language processing new products qualitative research topic modeling","what makes a hotel review helpful? an information requirement perspective. with the dramatic proliferation of online user-generated content (ugc), research has emerged to gain understanding of what makes a review helpful, and how useful knowledge can be filtered from over-loaded ugc. hospitality literature has widely examined the characteristics of helpful online reviews for identifying the determinants of helpfulness. in contrast, the influence of embedded opinions in reviews on helpfulness voting has rarely been examined. this study investigated the distribution of 28 identified hotel attributes ranging between helpful and not-helpful categories, in 85,963 hotel reviews. the findings suggest that customer information requirements differ according to hotel class. some attributes are reported as uniquely helpful for a specific hotel class, such as proximity to a station (3-stars), bed and lobby (4-stars), and experience and drinks (5-stars), whereas other attributes apply to multiple groups, such as walking distance (3–4 stars), english fluency (4–5 stars), and front-desk, street-road, taxi, and value (3–5 stars).. hotel information requirements review helpfulness text mining user-generated content","capturing changes in social media content: a multiple latent changepoint topic model. although social media has emerged as a popular source of insights for both researchers and practitioners, much of the work on the dynamics in social media has focused on common metrics such as volume and sentiment. in this research, we develop a changepoint model to capture the underlying shifts in social media content. we extend latent dirichlet allocation (lda), a topic modeling approach, by incorporating multiple latent changepoints through a dirichlet process hidden markov model that allows for the prevalence of topics to differ before and after each changepoint without requiring prior knowledge about the number of changepoints. we demonstrate our modeling framework using social media posts from brand crises (volkswagen’s 2015 emissions testing scandal and under armour’s 2018 data breach) and a new product launch (burger king’s 2016 launch of the angriest whopper). we show that our model identifies shifts in the conversation surrounding each of these events and outperforms both static and other dynamic topic models. we demonstrate how the model may be used by marketers to actively monitor conversations around their brands, including distinguishing between changes in the conversation arising from a shift in the contributor base and underlying changes in the topics discussed by contributors.. changepoint models social media text analysis topic models","improving text analysis using sentence conjunctions and punctuation. user-generated content in the form of customer reviews, blogs, and tweets is an emerging and rich source of data for marketers. topic models have been successfully applied to such data, demonstrating that empirical text analysis benefits greatly from a latent variable approach that summarizes high-level interactions among words. we propose a new topic model that allows for serial dependency of topics in text. that is, topics may carry over from word to word in a document, violating the bag-of-words assumption in traditional topic models. in the proposed model, topic carryover is informed by sentence conjunctions and punctuation. typically, such observed information is eliminated prior to analyzing text data (i.e., preprocessing) because words such as “and” and “but” do not differentiate topics. we find that these elements of grammar contain information relevant to topic changes. we examine the performance of our models using multiple data sets and establish boundary conditions for when our model leads to improved inference about customer evaluations. implications and opportunities for future research are discussed.. bayesian analysis customer satisfaction analysis latent dirichlet allocation (lda) syntactic covariates topic dependency user-generated content","marketing excellence: nature, measurement, and investor valuations. marketing excellence is a foundational principle for the discipline that is gaining increasing attention among managers and investors. despite this, the nature of marketing excellence and its effectiveness remain unclear. this research offers insight by addressing two questions: (1) how do managers understand and exercise marketing excellence? and (2) how do investors evaluate marketing excellence? study 1 merges insights from 39 in-depth interviews with senior executives and secondary data from 150 firm strategies to find that marketing excellence is a strategy type focused on achieving organic growth by executing priorities related to the marketing ecosystem, end user, and marketing agility. study 2 quantifies the impact of marketing excellence on firm value by using a machine learning algorithm and text analysis through an original dictionary to classify the text from 8,317 letters to shareholders in 1,727 u.s. firm annual reports. calendar-time portfolio models reveal abnormal one-year returns of up to 8.58% for marketing excellence—returns that outpace those associated with market orientation and marketing capabilities. findings offer guidance to managers, educators, and investors regarding how marketing excellence manifests—paving the way for the allocation of firm resources to ensure that marketing drives organic growth.. agility ecosystem end user grounded theory marketing excellence marketing–finance interface organic growth text analysis","exploring customer sentiment regarding online retail services: a topic-based approach. user-generated content is a valuable source for understanding online shoppers' emotions. using text-mining techniques, this study identifies seven topics regarding online retail services in online posts: product, retailer promotion, delivery, payment, communication, return/refund, and price. the topics are associated with the sentiment polarity of online shoppers' posts. this study further explores whether the emotional responses from domestic and cross-border online shoppers differ with regard to these topics. the results show that differences exist in these two groups' sentiments concerning product and payment. furthermore, there are differences in the two groups’ respective negative emotions (i.e., anger, sadness, and fear) concerning delivery, communication, and return/refund. the findings of this study provide online retailers with important managerial implications.. cross-border online shopping latent dirichlet allocation online retailing sentiment analysis topic modeling","big data analysis of volatility spillovers of brands across social media and stock markets. volatility is an important metric of financial performance, indicating uncertainty or risk. so, predicting and managing volatility is of interest to both company managers and investors. this study investigates whether volatility in user-generated content (ugc) can spill over to volatility in stock returns and vice versa. sources for user-generated content include tweets, blog posts, and google searches. the authors test the presence of these spillover effects by a multivariate garch model. further, the authors use multivariate regressions to reveal which type of company-related events increase volatility in user-generated content. results for two studies in different markets show significant volatility spillovers between the growth rates of user-generated content and stock returns. further, specific marketing events drive the volatility in user-generated content. in particular, new product launches significantly increase the volatility in the growth rates of user-generated content, which in turn can spill over to volatility in stock returns. moreover, the spillover effects differ in sign depending on the valence of the user-generated content in twitter. the authors discuss the managerial implications.. multivariate garch model natural language processing spillover effects stock market performance user-generated content volatility","the use of twitter for innovation in business markets. purpose: the purpose of this research is to investigate the use of twitter in business as a medium for knowledge sharing and to crowdsource information to support innovation and enhance business relationships in the context of business-to-business (b2b) marketing. design/methodology/approach: this study uses a combination of methodologies for gathering data in 52 face-to-face interviews across five countries and the downloaded posts from each of the interviewees' twitter accounts. the tweets were analysed using structural topic modelling (stm), and then compared to the interview data. this method enabled triangulation between stated use of twitter and respondent's actual tweets. findings: the research confirmed that individuals used twitter as a source of information, ideas, promotion and innovation within their industry. twitter facilitates building relevant business relationships through the exchange of new, expert and high-quality information within like-minded communities in real time, between companies and with their suppliers, customers and also their peers. research limitations/implications: as this study covered five countries, further comparative research on the use of twitter in the b2b context is called for. further investigation of the formalisation of social media strategies and return on investment for social media marketing efforts is also warranted. practical implications: this research highlights the business relationship building capacity of twitter as it enables customer and peer conversations that eventually support the development of product and service innovations. twitter has the capacity for marketers to inform and engage customers and peers in their networks on wider topics thereby building the brand of the individual users and their companies simultaneously. originality/value: this study focuses on interactions at the individual level illustrating that twitter is used for both customer and peer interactions that can lead to the sourcing of ideas, knowledge and ultimately innovation. the study is novel in its methodological approach of combining structured interviews and text mining that found the topics of the interviewees' tweets aligned with their interview responses.. business to business marketing crowdsourcing innovation social media topic modelling twitter","full disclosure: how smartphones enhance consumer self-disclosure. results from three large-scale field studies and two controlled experiments show that consumers tend to be more self-disclosing when generating content on their smartphone versus personal computer. this tendency is found in a wide range of domains including social media posts, online restaurant reviews, open-ended survey responses, and compliance with requests for personal information in web advertisements. the authors show that this increased willingness to self-disclose on one’s smartphone arises from the psychological effects of two distinguishing properties of the device: (1) feelings of comfort that many associate with their smartphone and (2) a tendency to narrowly focus attention on the disclosure task at hand due to the relative difficulty of generating content on the smaller device. the enhancing effect of smartphones on self-disclosure yields several important marketing implications, including the creation of content that is perceived as more persuasive by outside readers. the authors explore implications for how these findings can be strategically leveraged by managers, including how they may generalize to other emerging technologies.. natural language processing self-disclosure technology user-generated content","development of a service blueprint for the online-to-offline integration in service. online-to-offline (o2o) integration refers to the incorporation of separate online and offline service processes into a single service delivery. advances in mobile devices and information and communication technology enable the o2o integration, which has been applied to many services. this study proposes a new service blueprint, called the o2o service blueprint (o2o sb), which is specialized in visualizing and analyzing the service processes of the o2o integration. a comprehensive literature review and text mining analysis are conducted on massive quantities of literature, articles, and application introductions to understand characteristics of the o2o integration and extract keywords relevant to the o2o integration. comparisons of the o2o sb with the conventional service blueprint and information service blueprint validate that the o2o sb can address the limitations of existing service blueprints. an evaluation through expert interviews confirms the completeness, utility, and versatility of the o2o sb. the proposed o2o sb presents a complete picture of the entire service delivery process, whether online or offline. this sb helps users systematically understand the processes and formulate strategies for service improvement.. o2o sb online-to-offline (o2o) integration service blueprint (sb) service process analysis service process visualization","an integrated approach to estimate brand association matching and strength in virtual settings. this paper proposes an innovative method for estimating brand association matching and strength. it is based on a dual analytical perspective as it compares consumer-perceived with company-defined brand associations, thereby filling a research gap in the offline and online brand studies. in order to build this method, we investigate online communities as authentic interactive environments by combining text-mining with hybrid log-linear models. what emerges is an interdisciplinary method that contributes to extending the use of the user-generated content (ugc) for marketing purposes. its application is useful for verifying the conditions for customer brand equity and more systematically controlling the adopted brand communications. the analysis also takes into consideration consumers who, after experiencing the virtual setting, have become free-brand tellers and real co-creators of brand meaning. in the study, we focus on online fashion communities as digital platforms where consumer interactions are particularly intensive, and the narratives are rich in brand-related perceptions.. brand association hybrid log-linear models online community text -mining user-generated content-ugc","text mining analysis roadmap (tmar) for service research. purpose: the purpose of this paper is to offer a step-by-step text mining analysis roadmap (tmar) for service researchers. the paper provides guidance on how to choose between alternative tools, using illustrative examples from a range of business contexts. design/methodology/approach: the authors provide a six-stage tmar on how to use text mining methods in practice. at each stage, the authors provide a guiding question, articulate the aim, identify a range of methods and demonstrate how machine learning and linguistic techniques can be used in practice with illustrative examples drawn from business, from an array of data types, services and contexts. findings: at each of the six stages, this paper demonstrates useful insights that result from the text mining techniques to provide an in-depth understanding of the phenomenon and actionable insights for research and practice. originality/value: there is little research to guide scholars and practitioners on how to gain insights from the extensive “big data” that arises from the different data sources. in a first, this paper addresses this important gap highlighting the advantages of using text mining to gain useful insights for theory testing and practice in different service contexts.. artificial intelligence machine learning natural language processing qualitative research service research text mining","summarizing opinions with sentiment analysis from multiple reviews on travel destinations: summarizing opinions with sentiment analysis from multiple reviews on travel destinations. recently, the web has been crowded with growing volumes of various texts on every aspect of human life. it is difficult to rapidly access, analyze, and compose important decisions using efficient methods for raw textual data in the form of social media, blogs, feedback, reviews, etc., which receive textual inputs directly. it proposes an efficient method for summarization of various reviews of tourists on a specific tourist spot towards analyzing their sentiments towards the place. a classification technique automatically arranges documents into predefined categories and a summarization algorithm produces the exact condensed input such that output is most significant concepts of source documents. finally, sentiment analysis is done in summarized opinion using nlp and text analysis techniques to show overall sentiment about the spot. therefore, interested tourists can plan to visit the place do not go through all the reviews, rather they go through summarized documents with the overall sentiment about target place.. na","computerized content analysis of online data – opportunities for marketing scholars and practitioners. purpose: the purpose of this paper is to summarize the main developments from the early days of manual content analysis to the adoption of computer-assisted content analysis and the emerging artificial intelligence (ai)-supported ways to analyze content (primarily text) in marketing and consumer research. a further aim is to outline the many opportunities these new methods offer to marketing scholars and practitioners facing new types of data. design/methodology/approach: this conceptual paper maps our methods used for content analysis in marketing and consumer research. findings: this paper concludes that many new and emerging forms of unstructured data provide a wealth of insight that is neglected by existing content analysis methods. the main findings of this paper support the fact that emerging methods of making sense of such consumer data will take us beyond text and eventually lead to the adoption of ai-supported tools for all types of content and media. originality/value: this paper provides a broad summary of nearly five decades of content analysis in consumer and marketing research. it concludes that, much like in the past, today’s research focuses on the producers of the words than the words themselves and urges researchers to use ai and machine learning to extract meaning and value from the oceans of text and other content generated by organizations and their customers.. artificial intelligence automated text analysis cata consumer research content analysis diction ibm watson liwc machine learning marketing text analysis","financial constraints and marketing investment: evidence from text analysis. purpose: the purpose of this study to examine the effect of financial constraint on intangible investment because intangible investment provides an overall picture of marketing investment and activity. intangible investment also plays a significant role in facilitating future sales. using a new measure of intangible investment (peters and taylor, 2017), the authors first establish that intangible investment is positively related with future sales. then, using a new text-based measure of financial constraint, the authors show that financial constraint has a significant negative effect on future intangible investments after controlling for other factors. intangible investment has three components. the first is r&d, the second is 30 per cent of selling and general administrative expense (sga) and the third is other intangibles. the authors find that the negative and significant effect of financial constraint on 30 per cent sga is stronger. this indicates that financially constrained firms reduce marketing related investments. the authors then considered firm size and found that smaller firms facing financial constraint continue to increase their intangible investments, whereas larger firms reduce their intangible investment. as a robustness test, the authors use advertising expenditure as a measure of promotion related investment and find that financial constraint has a negative effect on advertising spending. the authors then use two traditional measures of financial constraint in their analysis to compare with the new text-based measure. design/methodology/approach: the authors use ordinary least squares with cluster robust standard error to conduct their empirical analysis. findings: first the authors establish that intangible investment positively affects future sales. further the authors find that financial constraint negatively affects intangible investment. moreover, financial constraint negatively affects the brand capital of intangible investment. research limitations/implications: the authors did not conduct any industry specific analysis to see how financial constraints affect intangible investment across different industries. industry specific analysis is important because in some industries/sectors intangibles are clearly more important than in others, so this is an important avenue for future research. it will also be interesting to explore if and how financial constraint has a mediating effect on sales growth via intangible investment and different components of intangibles. practical implications: this study identifies another important factor that can negatively affect brand capital investment. originality/value: the authors have used a measure of financial constraint and text mined all the annual reports of us firms for the period of 1994-2016 to compute this measure.. brand capital financial constraints intangible investments marketing investments text analysis","winning box office with the right movie synopsis. purpose: consumers often search for movie information and purchase tickets on the go. a synopsis is often provided by producers and theatres in mobile apps and websites. however, to the best of the authors’ knowledge, little research has investigated whether the synopsis has an impact on a movie’s box office. this research uses computerized text analysis in examining the influence of linguistic cues of a synopsis on the movie’s financial performance. this paper aims to show that language choice in a synopsis is a significant factor in predicting box office performance. design/methodology/approach: a total usable sample of 5973 movies was collected using a web crawler. computerised text analysis using linguistic inquiry and word count was adopted to analyse the movie synopses data. the empirical study comprises two phases. phase 1 used exploratory factor analysis on 50 per cent of the sample (sample 1) to establish the dimensionality of psychological processes as reflected in the linguistic expressions. the analysis identified 11 linguistic variables that loaded on four dimensions. the factor structure was replicated on an independent sample (sample 2) using confirmatory factor analysis. phase 2 tested the hypotheses using structure equation modelling. findings: results show that consistency between movie genres and linguistic cues in a film synopsis promotes movie box office revenue when linguistic cues shown in the synopsis confirm a consumer’s expectancies about a focal movie genre. conversely, a synopsis reduces the movie box office revenue when the linguistic cues shown disconfirm the genre-based expectancies. these linguistic cues exert similar effects on action and crime films but different effects on comedies and drama films. research limitations/implications: it is likely that consumer tastes and linguistic styles of film synopses have evolved over time. as a cross-sectional study, such changes were not taken into consideration in the current research. a longitudinal study in the future can reveal the dynamic relationship between film synopses and audience. practical implications: managerially, the findings show that a synopsis is an effective communication touch point to position a movie. this research provides concrete guidelines in crafting synopses with the “rights words’ aligned with movie-goers’ expectations within each specific genre. beyond movie consumption, the research findings can be applied to other entertainment products, such as tv series and books. originality/value: to our knowledge, this research is the first in studying the linguistic cues in synopses and its relation to box office performance. it addresses this knowledge gap by answering the basic question of whether movie synopses matter. methodically, the paper marks the first attempt to use the two-step structural equation modelling method on computerised content analysis data.. box office performance computerised content analysis expectancy confirmation linguistic cues liwc movie synopsis structure equation modelling","how executives talk: exploring marketing executive value articulation with computerized text analysis. purpose: this paper aims to explore how chief executive officers (ceos) and c-suite marketing executives (chief marketing officers [cmos], chief customer officers [ccos], chief branding officers [cbos], etc.) talk about marketing concepts to better understand how marketers can more effectively articulate their value and increase their strategic influence within the firm. design/methodology/approach: artificial intelligence-enabled computerized text analysis was used to identify and weight keywords from 266 ceo and c-suite marketing executive interviews. custom marketing concept dictionaries were used to gauge overall marketing focus. findings: the analysis revealed opportunities for c-suite marketers to align specific marketing concepts with that of ceos for increased strategic influence. comparisons between c-suite marketing roles showed that cmos are more focused on marketing strategy than specialized c-suite marketing positions, such as cco and cbo. this points to a potential decrease in strategic impact for marketing executives dependent on the specialization of their position. research limitations/implications: using ibm watson’s black-box artificial intelligence may limit the ability to replicate results from the content analysis; however, the results identify important ways that marketing executives can use to increase their ability to articulate their value within the firm. practical implications: c-suite marketing executives who want to increase the strategic alignment of their role with their firm must pay close attention to the marketing concepts they talk about, and how those align with their ceo’s marketing knowledge. the creation of specialized c-suite marketing roles may unintentionally limit the strategic thinking and firm-level impact of marketers. originality/value: this paper represents the first use of artificial intelligence-enabled computerized text analysis to explore and compare executive speech acts to help increase marketing’s influence in the firm. it is also the first to explore differences in marketing concept use between c-suite marketing roles.. artificial intelligence computerized text analysis marketing influence strategic marketing strategic planning","a machine-learning based approach to measuring constructs through text analysis. purpose: this paper aims to develop a novel and generalizable machine-learning based method of measuring established marketing constructs through passive analysis of consumer-generated textual data. the authors term this method scale-directed text analysis. design/methodology/approach: the method first develops a dictionary of words related to specific dimensions of a construct that is used to assess textual data from any source for a specific meaning. the method explicitly recognizes both specific words and the strength of their underlying sentiment. findings: results calculated using this new approach are statistically equivalent to responses to traditional marketing scale items. these results demonstrate the validity of the authors’ methodology and show its potential to complement traditional survey approaches to assessing marketing constructs. research limitations/implications: the method we outline relies on machine learning and thus requires either large volumes of text or a large number of cases. results are reliable only at the aggregate level. practical implications: the method detail provides a means of less intrusive data collection such as through scraped social media postings. alternatively, it also provides a means of analyzing data collected through more naturalistic methods such as open-response forms or even spoken language, both likely to increase response rates. originality/value: scale-directed text analysis goes beyond traditional methods of conducting simple sentiment analysis and word frequency or percentage counts. it combines the richness of traditional textual and sentiment analysis with the theoretical structure and analytical rigor provided by traditional marketing scales, all in an automatic process.. construct measurement machine learning text mining text scraping","business-to-business e-negotiations and influence tactics. e-negotiations, or sales negotiations over email, are increasingly common in business-to-business (b2b) sales, but little is known about selling effectiveness in this medium. this research investigates salespeople’s use of influence tactics as textual cues to manage buyers’ attention during b2b e-negotiations to win sales contract award. drawing on studies of attention as a selection heuristic, the authors advance the literature on mechanisms of sales influence by theorizing buyer attention as a key mediating variable between the use of influence tactics and contract award. they use a unique, longitudinal panel spanning more than two years of email communications between buyers and salespeople during b2b sales negotiations to develop a validated corpus of textual cues that are diagnostic of salespeople’s influence tactics in e-negotiations. these e-communications data are augmented by salesperson in-depth interviews and survey, archival performance data, and a controlled experimental study with professional salespeople. the obtained results indicate that the concurrent use of compliance or internalization-based tactics as textual cues bolsters buyers’ attention and is associated with greater likelihood of contract award. in contrast, concurrent use of compliance and internalization-based tactics is prone to degrade buyer attention and likely to put the salesperson at a disadvantage in closing the contract award.. business to business buyer attention e-communication linguistic machine learning negotiation selling text analysis","does the cmo’s personality matter for web traffic? evidence from technology-based new ventures. this study investigates whether the personalities of chief marketing officers (cmos) of technology-based new ventures affect how the increasing maturity of new ventures translates into web traffic. drawing on upper echelon theory and the interactionist theory of job performance, we explain how certain personality traits from the five-factor model are relevant to the job demands a cmo faces in technology-based new ventures. we build a multi-source dataset on 627 new ventures and use a novel approach to measuring personality that is based on computer text analysis—specifically, the liwc application—which we apply to the cmos’ twitter accounts. our findings indicate that a cmo’s extraversion positively moderates the relationship between a new venture’s maturity and web traffic, while a cmo’s conscientiousness is a negative moderator of this relationship. these results have useful theoretical and practical implications for the role of the cmo and for marketing new ventures in general.. chief marketing officer firm maturity personality technology-based new ventures","new approaches to psychographic consumer segmentation: exploring fine art collectors using artificial intelligence, automated text analysis and correspondence analysis. purpose: while the motivation for collecting art has received considerable attention in the literature, less is known about the characteristics of the typical art collector. this paper aims to explore these characteristics to develop a typology of art consumers using a mixed method approach over several studies. design/methodology/approach: this is achieved by analyzing qualitative data, gathered via semi-structured interviews of art collectors, and quantitatively by means of natural language processing analysis and automated text analysis and using correspondence analysis to analyze and present the results. findings: the study’s findings reveal four distinct clusters of art collectors based on their “big five” personality traits, as well as uncovering insights into how these types talk about their possessions. research limitations/implications: in addition to contributing to the arts marketing literature, the findings provide a more nuanced understanding of consumers that managers can use for market segmentation and target marketing decisions in other markets. the paper also offers a methodological contribution to the literature on correspondence analysis by demonstrating the “doubling” procedure to deal with percentile data. practical implications: in addition to contributing to the arts marketing literature, the findings provide a more nuanced understanding of art collectors that managers can use for market segmentation and target marketing decisions. the paper also offers a methodological contribution to the literature on correspondence analysis by demonstrating a non-traditional application of correspondence analysis using the “doubling” procedure. buyer behavior in the fine art market is not exhaustively studied. by understanding the personality traits of consumers in the art market, sales forces can better provide assistance and product to consumers. further, understanding the personalities of consumers is better for art retail spaces to better serve consumers. originality/value: this paper demonstrates a unique mixed methods approach to analyzing unstructured qualitative data. it shows how text data can be used to identify measurable market segments for which targeted strategies can be developed.. art collectors artificial intelligence automated text analysis correspondence analysis psychographic consumer segmentation psychographic segmentation quantitative analysis of qualitative data","is a picture worth a thousand words? an empirical study of image content and social media engagement. are social media posts with pictures more popular than those without? why do pictures with certain characteristics induce higher engagement than some other pictures? using data sets of social media posts about major airlines and sport utility vehicle brands collected from twitter and instagram, the authors empirically examine the influence of image content on social media engagement. after accounting for selection bias on the inclusion of image content, the authors find a significant and robust positive mere presence effect of image content on user engagement in both product categories on twitter. they also find that high-quality and professionally shot pictures consistently lead to higher engagement on both platforms for both product categories. however, the effect of colorfulness varies by product category, while the presence of human face and image–text fit can induce higher user engagement on twitter but not on instagram. these findings shed light on how to improve social media engagement using image content.. image processing natural language processing social media analytics user-generated content visual marketing","mindful consumption: three consumer segment views. as consumers are moving away from mindless consumerism, a mindful consumption literature has emerged that is based on buddhist and psychological perspectives of mindfulness. while the idea of mindful consumption has great potential, there is little empirical research to date that comprehensively examines the consumer perspective on the role of mindfulness on consumption. to provide a grounded consumer perspective, the authors segment mindful consumption views from open-end text using a mixed method of clustering and text mining. by analyzing the segmentation structure, the authors discover various consumer views of mindful consumption, such as careful economic based consumption, monitoring activities of firms, and being informed about the impact of consumption choices. the authors compare the empirical results with the academic literature to provide directions for future research.. mindful consumption mindfulness segmentation sustainability text mining","a text mining investigation of the presence and emotional impact of religious language in service organizations’ websites. christian religious language in marketing communications is becoming more common, and many christian-based service firms communicate religiosity via company websites. the impact of religious language in company websites has not been directly addressed, however. consequently, using a novel text mining methodology, this research examines the presence and impact of religious language on organizations’ websites over a 10-year period, including the emotional sentiments of the language and their intensity. the most frequently used religious words are identified and discussed, as is how the religious language contributes to the emotional tone of the message.. emotions religious firms service firms text mining web discourse","understanding brand image from consumer-generated hashtags. social media has emerged as a major hub of engagement between brands and consumers in recent years, and allows user-generated content to serve as a powerful means of encouraging communication between the sides. however, it is challenging to negotiate user-generated content owing to its lack of structure and the enormous amount generated. this study focuses on the hashtag, a metadata tag that reflects customers’ brand perception through social media platforms. online users share their knowledge and impressions using a wide variety of hashtags. we examine hashtags that co-occur with particular branded hashtags on the social media platform, instagram, to derive insights about brand perception. we apply text mining technology and network analysis to identify the perceptions of brand images among consumers on the site, where this helps distinguish among the diverse personalities of the brands. this study contributes to highlighting the value of hashtags in constructing brand personality in the context of online marketing.. brand image hashtag marketing research text mining","marketers; unintended consequences of improving the consumer food experience: discovering the key drivers. this study aims to explore the key drivers of the unintended consequences of marketers’ strategies to improve the consumer food experience. with a focus on the evolution of studies between 2000 and 2019, this study adopts a comparative automated content analysis using leximancer, a text-mining tool for analyzing and visualizing concepts and themes in texts. for this analysis, we select 52 relevant journal articles. the findings reveal that marketing strategies and policies are the key drivers of unintended consequences of improving the consumer experience, engendering critical issues including obesity, materialistic orientation, overeating, and customers’ unaware decision processes. we conclude by highlighting the importance of interdisciplinary and multidisciplinary studies to advance the research activity in this field.. automated content analysis consumer food experience food marketing leximancer unintended consequences","sense and sensibility: what are customers looking for in online product reviews? an abstract. online product reviews have become the single, largest depository of supplementary information that customers use in their product search, evaluation, and purchase process. the innate value of online reviews lays in the valuable information they provide to prospective customers in their decision making. this has inspired researchers to identify the characteristics of helpful reviews. extent research suggests that helpful reviews are often of certain numeric features, such as lengthy details and unequivocal rating (karimi and wang 2017; mudambi and schuff 2010). however, these numeric features do not reveal the nature of the review content. recognizing the importance of review content, recent studies gear toward examining review content, with a focus on sentiment, using text analysis techniques (cao et al. 2011; salehan and kim 2016). apart from sentiment, the impact of other content characteristics of online reviews is largely unknown. this research explores online review content by decomposing and comparing three fundamental information components that a review may contain: sensory information (i.e., reviewer’s observation), cognitive information (i.e., thoughts/analysis), and affective information (i.e., emotions). these components are directly associated with three fundamental psychological processes (observation, thinking, and emotion) that people experience to interact with and make sense of the world. when writing a review, reviewers tangle various types of information together to construct narratives and express opinions, creating a complex review content. readers, on the other side, retrieve and evaluate the information of all types to form an opinion on the quality and helpfulness of the review. distinguishing these different information components and analyzing their direct and combined effects can significantly enhance our knowledge of consumer’s information needs and online search behavior. this research performs text analysis to capture the three types of information in online product reviews, analyze their patterns and effects on perceived information value. results from analyzing a sample of 56,752 reviews from amazon.com indicate that sensory information in online review content has a significantly positive effect on online review helpfulness; whereas, this effect is insignificant for cognitive information, and significantly negative for affective information. this indicates that review readers highly value reviewer’s observations and their expression of sensory experience, are indifferent toward reviewer’s thoughts and analysis, and dislike expression of emotions in review content. this pattern is more salient in reviews of search goods than those of experience goods.. affective information cognitive information online review sensory information text analysis","patron sentiment of employee–customer interaction: exploring hotel customer reviews through machine learning: an abstract. experiences are a critical element in creating value for customers of service companies. in the tourism industry, employee–tourist encounters are particularly important as a lever for experience value creation. however, typically such encounters are based on a service quality logic that is standardised and functional (based on standard quality theory), thus missing considerable opportunities for employee-related experience creation. in this research, we seek to apply big data analytics to identify the types of customer–employee interactions that are the most influential in improving customers’ perceptions of service, value and overall satisfaction. a popular and well-known review website was selected to provide data for a range of hotel rankings (one- to five-stars) and sentiment performance. english language reviews and related variables associated with each review were used. this provided more than a quarter of a million reviews for analysis. a dictionary of terms was created by collecting and compiling synonyms associated with the types of hotel customer–employee interaction based on personalisation, flexibility, co-creation, emotions and knowledge gain/learning. dictionary terms were also developed for mentions of employees. the process helped us to develop a final list of 639 words. to improve the computational efficiency of the analysis, the data were pre-processed using python’s natural language toolkit. in order to focus on the most objective and reliable reviews, we reduced the sample to those reviews with a subjectivity level less than or equal to 0.5. each review was then content-analysed for sentiment and interaction type in order to explore these important relationships statistically. anova tests were applied to examine differences in service quality, satisfaction and value based. the overall assessment of our results appears to suggest that hotel customers are difficult to please; positive employee–customer interactions receive significant positive improvements in customer perceptions of satisfaction, values and service, but customers are extremely sensitive to any problems in employee–customer interactions. amongst the types of employee interactions, emotional intelligence and co-creation of customer experiences appeared to be the most promising for increasing the three outcome variables, whilst flexibility appeared to be a critical element of employee interactions not to get wrong. the research has significant implications for future research and practice.. consumer experience employee interactions text analytics tourism","what makes the difference? employee social media brand engagement: an abstract. through the employee lens of business-to-business (b2b) firms, we explore word use through brand engagement and social media interaction to understand what makes the difference of those employees who rate their employer brands highly on social media, and those who do not. this content becomes a valuable source of information for marketing decision makers, as well as an interesting and rich new source of data for b2b marketing scholars. fortunately, the recent past has not only seen a significant rise in the amount of unstructured textual data available to researchers, but also a noteworthy increase in the number and sophistication of tools available to perform textual content analysis using computers. one of the major computerized text analysis tools in use today is liwc (linguistic inquiry and word count), the development of which is described by pennebaker and his colleagues (pennebaker et al. 2001). we conducted a textual content analysis of social media job evaluation site glassdoor.com using the liwc software package to analyze 30 of the top 200 b2b brands listed on brandwatch using four variables, namely, analytical thinking, clout, authenticity, and emotional tone. the results show that employees who rate their employer’s brand low use significantly more words, are significantly less analytic, and write with significantly more clout because they focus more on others than themselves. employees who rate their employer’s brand highly, write with significantly more authenticity, exhibit a significantly higher tone, and display far more positive emotions in their reviews. brand engagement drives brand equity. brand equity is not only a significant indicator of marketing effectiveness, it is also a fundamental driver of firm value. this research demonstrates that b2b brand managers and b2b branding scholars should treat social media data disseminated by individual stakeholders, like the variables used in this study (tone, word count, and frequency), as an opportunity to tap a rich source of data with powerful automated text analysis tools to better understand and manage brand insight, brand engagement, and brand equity now and over time.. brand engagement business-to-business content analysis social media","examining country image in expert electronic word-of-mouth: an abstract. experts guide consumers’ preferences and purchase decisions because they are often perceived as unbiased and trustworthy. countries spend billions on marketing their agricultural industries to not only their own citizens but also foreign citizens all over the world, including these experts. the wine industry is a high-value agricultural industry from the standpoints of both the main raw agricultural products (i.e., grapes) and the value added in the finished products. this paper explores the role of country image in expert ratings and reviews (i.e., expert ewom) using large data sets from the global wine industry and the automated text analysis diction. we find significant differences between countries regarding price, rating, and all of the diction content dimensions using anova tests at p < 0.001. furthermore, using regression analysis, we find significant effects for the price, all five review content dimensions, all major countries with the exception of australia and france. the “france” variable was significant until we controlled for the influence of price. the australia variable was nonsignificant in all regression analysis indicating that the australian wine ratings were not statistically different than minor wine-producing countries. this paper makes several contributions to the expert ewom and country image literatures. producers in prestigious wine-producing countries (e.g., italian or german) can extract a price premium related to the image of that country that is perceived by the experts as high quality. however, producers in less prestigious wine-producing countries (e.g., south african or chilean) can signal quality through a higher price that can help them overcome their country image bias and aid the perception by experts of high quality. producers can also manage expert ewom by priming these reviewers to write reviews that use more active, certain, and optimistic language to increase quality ratings. thus, governments and industry associations can build country images that encourage experts to write reviews with active, optimistic, and central language to influence quality ratings.. ewom bias expert reviews intext-mining wine","special session: corporate social responsibility and ai: the case of fashion: an abstract. this paper critically examines the ethical issues of current and predicted adoption of ai within fashion. ai is a branch of computer science that combines machine learning, logic and natural language processing to emulate human intelligence. by processing vast amounts of data and recognising patterns in the data, ai produces insights and analytics at or above human capability (ieee 2017). it has been applied to fashion in trend forecasting, personalisation, customer interaction and the use of cgi models. for example, amazon developed “echo look” which combines computer vision with predictive ai and human analysis to assess a consumer’s current wardrobe choices and provide recommendations of what to wear. users can ask the machine to rate two different possible outfits in terms of current trends and actual appearance (gibbs 2017). online styling subscription stitch fix uses machine learning algorithms to determine its customers’ preferred styles and offer personalised recommendations. ai has also been developed to mimic the role of social media influencers and models. cgi character “miquela” has 1.6 million instagram followers, collaborates with luxury brands and launched her own clothing brand, but is not a real person. there is a dedicated digital modelling agency (davis 2018). such innovations are likely to further inflame the ongoing ethical debate about using digital technologies and virtual reality at the risk of opportunities for real people, for example diminishing already scarce opportunities for black models (graham 2018), or introducing norms of artificial appearance and personal aesthetics impossible for ‘real’ people to achieve. ai has the potential to revolutionise fashion through greater precision, accuracy and effectiveness in decision-making, but at what cost? to what extent can and should ai replicate and replace human creativity and presence in an industry sector founded on human input in creating new trends? yet, ai that predicts garment suitability and offers greater customisation for consumers may reduce waste and over-consumption (snow 2017). producing items which have greater relevance could reduce returns, markdowns and warehouse space, plus increase customer satisfaction and experience. automating repetitive tasks could save money and time for greater value-added activities, for example through the use of chatbots for online customer service. this paper applies the lens of csr to inform the debate on whether core fashion retailing functions can and should be replaced by ai. a csr approach argues for businesses assuming social responsibilities beyond any economic, technical and legal obligations which constitute its existence, with the objective of reconciling economic, social and environmental objectives (davis 1973).. ai csr ethics fashion","exploring the holistic customer experience gestalt through its elements: an abstract. customer experience (ce) is the essence of what constitutes the interaction of a service and a customer. it is multidimensional and comprised of cognitive, emotional, behavioral, sensorial, and social elements resulting from a direct or indirect interaction with a firm (lemon and verhoef 2016, p. 71). ce has many definitions and descriptions of its constituent elements, which adds a layer of complexity in constructing comparable studies across the board. this fragmentation poses limitations for new researchers who attempt to study ce, described as “widely used, and abused”, and at the risk of being “dismissed because of the ambiguous manner in which it has been applied” (palmer 2010, p. 196). to this extent, the aim of this work is to (i) explore the definitions of ce and its elements, (ii) analyze conceptual and empirical studies relating to these elements, and (iii) from the previous two objectives incorporate a bottom-up approach, producing a unified elemental mapping framework enabling a more integrated and holistic manner in which ce is studied and observed in both academia and practice. following the objectives for this work, we conduct a systematic literature review (slr) search pertaining to ce and its elements, analyzing the different conceptualizations of its meaning and empirical foundations. we then extract and semantically group the ce elements into a unified list. after reviewing and analyzing our results we note that the literature defines ce through socioeconomic, phenomenological, or a combination of these lenses. furthermore, we observe that these lenses are driven along service and customer centric perspectives. we build upon the inductive experiential mapping scheme used by lofman (1991) to build a sub-elemental framework from the unified ce element list we obtained. from a theoretical standpoint, we attempt to redefine ce from the extant slr. additionally, we propose a sub-elemental mapping framework which would alleviate some of the conceptual fragmentation we highlighted in studying ce. from a managerial standpoint, we aim at addressing ce managers and executives by providing them with a theoretically-driven framework enabling them to map their organizational ce. as a result, a standardized means to measure and better understand ce in practice could be achieved. as a way forward, our mapping framework could provide a starting point to potentially connect technology-driven methods using artificial intelligence and machine learning algorithms to for example, utilize text-mining, and heuristic internet of things to map experiential data. this would enable researchers and managers to conduct studies in different contexts and domains, providing a customer-centric approach to ce while subsequently contributing to our holistic understanding of this concept.. artificial intelligence customer centric customer experience experiential elements framework systematic review","making sense of online reviews: a machine learning approach: an abstract. it is estimated that 80% of companies’ data is unstructured. unstructured data, or data that is not predefined by numerical values, continues to grow at a rapid pace. images, text, videos and voice are all examples of unstructured data. companies can use this type of data to leverage novel insights unavailable through more easily manageable, structured data. unstructured data, however, creates a challenge since it often requires substantial coding prior to performing an analysis. the purpose of this study is to describe the steps and introduce computational methods that can be adopted to further explore unstructured, online reviews. the unstructured nature of online reviews requires extensive text analytics processing. this study introduces methods for text analytics including tokenization at the sentence level, lemmatization or stemming to reduce inflectional forms of the words appearing in the text, and ‘bag of n-grams’ approach. we will also introduce lexicon-based feature engineering and methods to develop new lexicons for capturing theoretically established constructs and relationships that are specific to the domain of study. the numeric features generated in the analysis will then be analyzed using machine learning algorithms. this process can be applied to the analysis of other unstructured data such as dyadic information exchange between customer service, salespeople, customers and channel members. although not a comprehensive set of examples, companies can apply results from unstructured data analysis to examine a variety of outcomes related to customer decisions, managing channels and mitigating potential crisis situations. understanding interdisciplinary methods of analyzing unstructured data is critical as the availability of this type of data continues to accelerate and enables researchers to develop theoretical contributions within the marketing discipline.. lemmatization machine learning stemming tokenization unstructured data","dealing with ambiguity in online customer reviews: the topic-sentiment method for automated content analysis. content analysis has become a widely used technique for the analysis of the large quantities of data that are generated online. especially relevant for marketing researchers are customer reviews on websites such as tripadvisor and amazon, because they express customers’ satisfaction and they represent an important source of word-of-mouth for other consumers. although the recent preference for sentence-constrained approaches has increased the accuracy of analytical methods, in many cases these methods still ignore some of the nuances contained within online reviews. in particular, current methods may not detect when a single topic is discussed both positively and negatively in a single review, or when a single sentence discusses two separate topics. the topic-sentiment method that is proposed in this paper addresses these two issues. it is a sentence-constrained approach that identifies ‘topic-sentiment pairs’; sentences that contain one word that describes the topic, and another that expresses the sentiment (positive or negative). to illustrate the analytical process, the method is applied to a dataset of 17,225 tripadvisor reviews for restaurants in london. results indicate that the topic-sentiment method offers a more nuanced approach for the analysis of customer reviews, while it retains the intuitiveness and simplicity of currently used methods.. content analysis customer reviews data analysis digital marketing online reviews text analysis","nation brands in expert electronic word-of-mouth: an abstract. experts’ reviews shape consumer’s preferences and purchase decisions because they are often perceived as unbiased and trustworthy. wine industry is a high value agricultural industry from the perspectives of both the main raw agricultural and the value added in the finished products. as a high value industry to both governments and consumers, countries spend billions on marketing their agricultural industries to not only their own citizens but also foreign citizens all over the world including these experts to establish a good product reputation. this paper examines the role of country of origin in expert ewom (ratings and reviews) through large dataset from the global wine industry. furthermore, we processed the review content by applying a structured automated text analysis program diction. the result of anova test demonstrates that there are significant differences between countries in terms of price, rating, and all the diction content dimensions at the p < 0.001. we also find significant effects on wine quality ratings of price, country of origin, and review of content by conducting regression analysis. indicated by the size of effect on ratings, expert reviewers use price as signal of quality when they provide ewom. country image significantly influences their review in most cases exception of australia and france. usa variable is non-significant when considered alone but is significant when considered with price. france variable is non-significant with price included in the regression but has a significant bonus in ratings when not considering the effect of price. the australia variable was non-significant in all regression analysis indicating that there was australian wine ratings were not statistically different than minor wine producing countries. this paper provides several contributions in the field of expert ewom and country of origin literatures. wine producer in prestigious wine producing countries such as germany and italy can extract a price premium related to the country that is perceived by expert as high quality. however, producers in less prestigious wine producing countries (e.g., south african or chilean) can signal quality through a higher price that can help them overcome the country image bias and aid the perception by experts of high quality, and they can also take advantage of the low price segment of consumers. producers can also manage expert ewom by priming these reviewers to use more active, certain, optimistic language in reviews to increase quality ratings. furthermore, governments and industry associations can encourage experts to writes reviews with active, optimistic and central language to have an influence on quality rating, and thus develop country images.. ewom bias expert reviews intext-mining wine","the effect of nwom firestorms on south african retail banking. purpose: this study analysed the effect of online negative word-of-mouth (nwom) firestorms in the retail banking sector. by understanding negative sentiment and sentiment recovery across an entire retail banking sector, the research exposed a unique view of banking in south africa. design/methodology/approach: the study made use of both a sentiment and topic analysis of over 1.7 million social media posts in south africa. the methodology made use of both nlp and human validation techniques to measure changes in social media sentiment during online firestorms. this measurement included each of south africa's major retail banks over a twelve month period. findings: from the analysis, key trigger characteristics for these firestorms (product failures, service failures, social failures and communication failures) were categorised. in addition, the average duration of a firestorm was calculated and factors that impact sentiment recovery were explored. originality/value: the study was located in south africa and, unlike firm level studies, researched nwom for the whole retail banking sector. a theoretical footprint depicting the typical anatomy of a firestorm was derived in order to aid stakeholders to be more vigilant and better equipped to provide correct intervention in such times of crisis.. negative word-of-mouth (nwom) online firestorms retail banks sentiment analysis social media","do they expect differently in hotel experiences? views of eastern vs. western: an abstract. this study sheds light on ways online guests’ rating in the hotel review website tripadvisor differ between chinese- and english-speaking groups. the study adopted mixed methodology and applied with both quantitative and qualitative analysis. the dual analysis with 800 online reviews of eight hotel brands that considered as a representative of its category located in bangkok city, thailand. results from quantitative analysis revealed that 2-star hotels were rated positively than 3- to 5-star hotels. english-speaking guests gave higher ratings than chinese-speaking guest in all the hotel service attributes (e.g. service, cleanliness, room, sleep quality, location, value, and overall). from qualitative aspect, ten service attributes that impacted on traveler satisfaction were identified. these attributes were then classified into three broad themed categories known as intangible service, tangible service and staying experience. the result further reveals the most frequently used words when cross-cultural travelers give online reviews on the sites. implications of the divergences in themes abstracted are discussed. the current research offers practical insights for hotel operators who would like to improve their service quality. more online reviews on the hotels’ webs is better for the hotel performance since those online reviews can be considered as good references for the travelers during their searching process. positive reviews mean satisfaction and positively influence on consumer purchase decision while negative reviews consider as a gift for hoteliers for further development their service standard. in this case, the hotelier could consider developing the customer incentive system (e.g. membership rewards) to guests who post reviews of hotels products and services on the social media platforms, which plays an important role in ewom for younger generation who prefers using social media platform rather than traditional wom (kim et al. 2016).. hotel attributes online reviews service quality text mining","analysing the interrelationship between online reviews and sales: the role of review length and sentiment index in electronic markets. with the advent of electronic commerce, online customer reviews (ocrs) have become a significant source of information related to a product/service. nowadays, lot of research is being carried out to understand the role played by ocrs on the buying intentions and product evaluation of the prospective customers. in this paper, we use log linear regression models to include the effects of product price, review length, review volume and sentiments (positive or negative words) on the sales of a product. using amazon.com review dataset, we show that in addition to the price, the customers are influenced by review volume, length, star rating and sentiment of reviews' text. we have also discussed the managerial as well as theoretical implication of the regression-based sales modelling integrated with information processing technique of text mining and sentiment analysis.. electronic commerce ocrs online customer reviews review length sentiment analysis sentiment index text mining","accommodation ewom in the sharing economy: automated text comparisons from a large sample. across many industries, individuals are increasingly relying on customer ratings and reviews on social media. while customer reviews often provide detailed diagnostic information about experiences, customer ratings often reduce the experience down to a simple number. moreover, there is evidence to support that customer rating inflation is occurring on social media sites over time, especially in the sharing economy, and especially with regard to travel and tourism experiences. this paper conceptualizes how customer experiences are reduced into customer reviews and further abridged into customer ratings in both the traditional and sharing economy contexts. we propose that customers observe how service providers present themselves as a professional (established chain hotel) or amateur (owner operated vacation rental apartment), and then form different service expectations and perceptions accordingly. we investigate 55,110 customer reviews and ratings of new york city’s accommodation providers and indeed find evidence of rating inflation over the eight years studied.. amateur service providers automated text analysis customer rating inflation customer reviews sharing economy","why human involvement is still required to move text analytics technologies leveraged with artificial intelligence from the trough of disillusionment to the plateau of productivity. the text analytics market, which has been predicted to be worth us$21.7bn by 2025, is falling headfirst into gartner’s well-known trough of disillusionment by failing to deliver real organisational value and meet user expectations. the might of marketing has duped clients with hyped promises of illusive actionable insights delivered through fast, sexy interfaces, yet the industry is not delivering the value it promises. this paper explores the reasons behind the failure to deliver this expected value. it will define the terms ‘value’, ‘insight’ and ‘actionable insight’. it will use these definitions to identify where and why industry practice fails to meet these fundamental expectations. a short case study is included to provide an example of how emotion analytics of consumer-generated unstructured text data can help deliver meaningful and genuinely actionable insights.. actionable insights ai big data consumer data emotion analytics hybrid text analytics nlp text analytics unstructured text","user response to e-wom in social networks: how to predict a content influence in twitter. the purpose of this research is to find influential factors on electronic word-of-mouth effectiveness for e-retailers in twitter social media, applying data mining and text mining techniques and through r programming language. the relationship between using hashtag, mention, media and link in the tweet content, length of the content, the time of being posted and the number of followers and followings with the influence of e-wom is analysed. 48,129 tweets about two of the most famous american e-retailers, amazon and ebay, are used as samples; results show a strong relationship between the number of followers, followings, the length of the content and the effectiveness of e-wom and weaker relevance between having media and mention with e-wom effectiveness on twitter. findings of this paper would help e-retailing marketers and managers to know their influential customers in social media channels for viral marketing purpose and advertising campaigns.. content influence data mining e-retailing e-wom electronic word-of-mouth social media text mining twitter","an empirical case study on indian consumers' sentiment towards electric vehicles: a big data analytics approach. today, climate change due to global warming is a significant concern to all of us. india's rate of greenhouse gas emissions is increasing day by day, placing india in the top ten emitters in the world. air pollution is one of the significant contributors to the greenhouse effect. transportation contributes about 10% of the air pollution in india. the indian government is taking steps to reduce air pollution by encouraging the use of electric vehicles. but, success depends on consumer's sentiment, perception and understanding towards electric vehicles (ev). this case study tried to capture the feeling, attitude, and emotions of indian consumers' towards electric vehicles. the main objective of this study was to extract opinions valuable to prospective buyers (to know what is best for them), marketers (for determining what features should be advertised) and manufacturers (for deciding what features should be improved) using deep learning techniques (e.g doc2vec algorithm, recurrent neural network (rnn), convolutional neural network (cnn)). due to the very nature of social media data, big data platform was chosen to analyze the sentiment towards ev. deep learning based techniques were preferred over traditional machine learning algorithms (support vector machine, logistic regression and decision tree, etc.) due to its superior text mining capabilities. two years data (2016 to 2018) were collected from different social media platform for this case study. the results showed the efficiency of deep learning algorithms and found cnn yield better results in-compare to others. the proposed optimal model will help consumers, designers and manufacturers in their decision-making capabilities to choose, design and manufacture ev.. big data deep learning electric vehicles india sentiment analysis","helpfulness and economic impact of multidimensional rating systems: perspective of functional and hedonic characteristics. nowadays, the multidimensional rating systems are increasingly popular in practice to help consumers evaluate multiattribute products or services. this study aims to examine the helpfulness and economic impact of multidimensional rating systems and the moderating effect of product type (ordinary, premium) from the perspective of product line. to identify the different effects of different product characteristics on consumers' behavior, multidimensional ratings and textual reviews are classified into two different sets according to the product attributes (functional, hedonic). besides, the effects of review valence are considered simultaneously. econometric analysis and text mining are conducted on the real data from autohome.com, a leading chinese automobile rating platform. results show that (a) in terms of perceived helpfulness, reviews with positive functional ratings or with negative hedonic ratings are perceived more helpful for ordinary product, whereas reviews with positive hedonic ratings are perceived more helpful for premium product. (b) regarding product sales, the impacts of functional and hedonic ratings on product sales are inverted u-shape. furthermore, for ordinary product, variance of functional ratings has a negative effect on product sales, whereas hedonic ratings variance has an opposite impact. surprisingly, ordinary product consumers tend to pay more attention to the negative hedonic reviews, whereas premium product consumers concentrate more on the negative functional reviews. in summary, various influences of multidimensional reviews and ratings across product type on review helpfulness and product sales are deeply explored in this study, suggesting that firms' online marketing strategies should be contingent on product type and characteristics.. na","exploring multisensory place experiences through cruise blog analysis. the multisensory approach to understanding consumer behavior has received increasing attention as a factor shaping individuals’ perception, attitude, and behavior. however, previous studies have scarcely investigated the role of senses on destination experiences as places of consumption. the aim of the research is to uncover tourists’ meaningful multisensory place perceptions by analyzing cruise travel blogs. the study conducted a thematic content analysis of 248 blog entries on mediterranean port of call experiences using the text analytics software leximancer. the results show a convergence between visual, gustatory, olfactory, and haptic impressions, while auditory perceptions are scarce and refer mainly to the presence/absence of noise. interestingly, unlike past multisensory studies that reported haptic perceptions as the least salient sensory dimension in tourist experiences, the research identified three haptic themes associated with cutaneous, hedonic-elicited, and somatic touch. the research yields a threefold contribution: (a) it provides empirical evidence for the relevance of multisensory perceptions on tourists’ evaluation of place experiences; (b) a novel methodological approach to assessing sensory impressions is used by analyzing tourists’ freely written online narratives; and (c) the research broadens the scope of existing multisensory literature by assessing urban/coastal travel destination experiences.. blogs ewom leximancer place branding sensory marketing","uniting the tribes: using text for marketing insight. words are part of almost every marketplace interaction. online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data. but how can marketers best use such data? this article provides an overview of automated textual analysis and details how it can be used to generate marketing insights. the authors discuss how text reflects qualities of the text producer (and the context in which the text was produced) and impacts the audience or text recipient. next, they discuss how text can be a powerful tool both for prediction and for understanding (i.e., insights). then, the authors overview methodologies and metrics used in text analysis, providing a set of guidelines and procedures. finally, they further highlight some common metrics and challenges and discuss how researchers can address issues of internal and external validity. they conclude with a discussion of potential areas for future work. along the way, the authors note how textual analysis can unite the tribes of marketing. while most marketing problems are interdisciplinary, the field is often fragmented. by involving skills and ideas from each of the subareas of marketing, text analysis has the potential to help unite the field with a common set of tools and approaches.. computational linguistics interdisciplinary machine learning marketing insight natural language processing text analysis text mining","consumer acceptance of camel milk in emerging economy. camel is considered as an indispensable part of desert eco-framework across the globe. apart from helping in transport, it is also used for its milk. camel's milk is used worldwide because of its salty taste. however, it has many beneficial effects such as cure for autism and helps to control diabetes, allergy, and also prevents liver cirrhosis. objective of this study is to analyze the ways to make people purchase and consume camel milk. in the last few years, awareness about nutritional and medicinal benefits of camel milk has rapidly increased. consequently, the demand of the product has also increased. objective of this article is to analyze the consumers’ willingness to buy camel milk before and after knowing its health benefits. it also explores the linkage between lifestyle diseases and consumer’s willingness to buy. we analyzed data in two stages. initially, we did qualitative analysis with twitter text mining to understand customer’s sentiments about camel milk. later, primary data was collected through structured questionnaire from a sample of 120 respondents by convenience sampling for quantitative analysis. there is a significant increase in the proportion of people willing to buy camel milk and once they were aware of the benefits, people are willing to buy more after knowing the benefits. people exposed to lifestyle diseases are more willing to buy camel milk. hence, support of market-based awareness campaigns to stimulate demand for quality camel milk among consumers can be realized through information dissemination and awareness campaign.. awareness camel milk health benefits willingness to buy","large-scale cross-category analysis of consumer review content on sales conversion leveraging deep learning. how consumers use review content has remained opaque due to the unstructured nature of text and the lack of review-reading behavior data. the authors overcome this challenge by applying deep learning–based natural language processing on data that tracks individual-level review reading, searching, and purchasing behaviors on an e-commerce site to investigate how consumers use review content. they extract quality and price content from 500,000 reviews of 600 product categories and achieve two objectives. first, the authors describe consumers’ review-content-reading behaviors. although consumers do not read review content all the time, they do rely on it for products that are expensive or of uncertain quality. second, the authors quantify the causal impact of read-review content on sales by using supervised deep learning to tag six theory-driven content dimensions and applying a regression discontinuity in time design. they find that aesthetics and price content significantly increase conversion across almost all product categories. review content has a higher impact on sales when the average rating is higher, ratings variance is lower, the market is more competitive or immature, or brand information is not accessible. a counterfactual simulation suggests that reordering reviews based on content can have the same effect as a 1.6% price cut for boosting conversion.. consumer purchase journey deep learning economic impact of text natural language processing product reviews regression discontinuity in time","personalisation the artificial intelligence way. the era of artificial intelligence (ai) has arrived. companies all over the world are championing their latest progress with ai, machine learning and deep learning, even though most of it is far short of anything that could be described as a breakthrough. unfortunately, this excitement does not always translate into quantifiable success — it can take up to six months to go from concept to production, and even then, only one in three ai projects turns out successful. these odds might be low, but the effort is well worth it as the potential payoff could be huge. this paper describes five different types of ai — sound, time series, text, image and video — and illustrates various ways that ai can be used, including in customer relationship management, e-commerce, customer recommendations, security, voice assistance and natural language processing for customer understanding. this article argues that ai will become the basis for a level of customer personalisation that will not only be recognised but soon be demanded by fickle customers everywhere. as the paper will show, it is imperative for brands to utilise ai in their marketing because it allows them to have both a single view of the customer as well as a single view of their media.. artificial intelligence chatbots customer lifecycle deep learning emotional recognition image search lookalike marketing machine learning personalisation programmatic advertising psychometrics real-time marketing voice recognition voice-assisted search website morphing","when words sweat: identifying signals for loan default in the text of loan applications. the authors present empirical evidence that borrowers, consciously or not, leave traces of their intentions, circumstances, and personality traits in the text they write when applying for a loan. this textual information has a substantial and significant ability to predict whether borrowers will pay back the loan above and beyond the financial and demographic variables commonly used in models predicting default. the authors use text-mining and machine learning tools to automatically process and analyze the raw text in over 120,000 loan requests from prosper, an online crowdfunding platform. including in the predictive model the textual information in the loan significantly helps predict loan default and can have substantial financial implications. the authors find that loan requests written by defaulting borrowers are more likely to include words related to their family, mentions of god, the borrower’s financial and general hardship, pleading lenders for help, and short-term-focused words. the authors further observe that defaulting loan requests are written in a manner consistent with the writing styles of extroverts and liars.. consumer finance loan default machine learning text mining","strategic orientation and firm risk. entrepreneurial orientation (eo) and market orientation (mo) have received substantial conceptual and empirical attention in the marketing and management literature and both orientations have consistently been linked to stronger financial performance. yet the way in which market-oriented firms seek to achieve superior rents is substantively different from that of entrepreneurially oriented firms which could lead to differential impacts of eo and mo on firm risk. in this study, the authors employ a text mining technique to assess firms' eo and mo and examine the impact of these two strategic orientations on shareholder risk outcomes. the results show that while eo increases idiosyncratic risk, mo decreases it. however, only eo decreases systematic risk. overall, the results of this study demonstrate that a firm's decisions regarding strategic orientation should be examined in light of both likely risks and returns in order to make appropriate resource allocation decisions.. entrepreneurial orientation firm risk market orientation shareholder return text analysis","a continuum of transformative service exchange: insights for service and social marketers. purpose: transformative service research (tsr) and social marketing share a common goal, which is to institute social change that improves individual and societal well-being. however, the mechanism via which such improved well-being results or so-called “transformation” occurs, is not well understood. the purpose of this paper is to examine the claims made in the tsr literature to identity the themes and scholarly meaning of “transformative” service exchange; ascertain the mechanisms used in service contexts to realize transformation, including to motivate long-term, sustainable societal change; and develop a transformative service exchange continuum to guide research and managerial approaches that aim to create uplifting social change. the authors recommend their continuum as a framework to inform how social marketing and service scholars design service solutions to address wicked social problems. design/methodology/approach: this paper presents a qualitative study where leximancer, a text-mining tool, is used to visualize the structure of themes and concepts that define transformative service exchanges as explained and applied in the literature. additionally, a profiling analysis of transformation as it is discussed in the tsr literature is used to identify the mechanisms that service marketers have developed to establish current theorization of service thinking for social change. these qualitative phases of analysis then inform the development of the transformative service exchange continuum. findings: a scoping review identified 51 articles across 12 journals, based on this study’s selection criteria for identifying transformative service exchanges. the leximancer analysis systematically and efficiently guided the authors’ interpretation of the large data corpus and was used in the identification of service themes. the use of text-mining software afforded a detailed lens to enrich the authors’ interpretation and clarification of six high-level concepts for inclusion on a transformative service exchange continuum. originality/value: this paper aims to unpack the meaning of transformative service exchange by highlighting the mechanism(s) used by researchers when designing social change outcomes. it contributes to tsr via the development of the continuum across micro, meso and macro levels. the temporal nature of transformative service exchanges is also elucidated. this continuum integrates current tsr studies and can guide future service studies in the tsr and social marketing domains.. qualitative research service ecosystem social marketing transformative","enhancing the helpfulness of online consumer reviews: the role of latent (content) factors. past empirical studies have analysed the influence of manifest online review content factors and the reviewer-related factors on online review helpfulness. however, the influence of latent content factors, which are implied from the text and that result in the differential helpfulness perceptions of review receivers, have been ignored. hence, using the lens of the elaboration likelihood model (elm), we develop a comprehensive model to study the influence of content- and reviewer-related factors on review helpfulness. this study not only includes the manifest content-related and reviewer-related factors, but also the latent content factors consisting of argument quality (comprehensiveness, clarity, readability and relevance) and message valence. the study initially employs a manual content analysis to analyse the argument quality of ~500 tripadvisor reviews (study 1). subsequently, model testing techniques are used to study the holistic and relative influence of these different factors on review helpfulness. in the validation study (study 2), machine learning and natural language processing techniques are used to extract latent content information and test the above model with 50,000 reviews from yelp.com. the results show that latent review content variables like argument quality and valence influence the helpfulness of the reviews better and beyond the previously studied, manifest review content- and reviewer-related factors.. argument quality content analysis message valence natural language processing online consumer reviews review helpfulness text mining","tweeting with the stars: automated text analysis of the effect of celebrity social media communications on consumer word of mouth. prior research has focused on analyzing the content and intent of celebrity social media communications. by observing that the linguistic style of such celebrity communications drives consumer word of mouth, the main goal of the current research is to broaden this limited perspective. an automated text analysis of narrative/analytical, internally/externally focused, and negative/positive emotional styles in tweets by celebrity chefs, personal trainers, and fashion bloggers was conducted to this effect. the findings are threefold. first, across celebrity categories externally focused, narrative styles are more effective in terms of word of mouth. second, emotional styles are not effective. third, angry outbursts are an exception; they are effective drivers of word of mouth for personal trainers. as such, this research furthers scholarly and practitioner understanding of the state-the-art of celebrity social media communication: the effect of tweets' linguistic styles on consumer word of mouth.. analytical automated text analysis emotion focus function words linguistic style narrative speech act theory","artificial intelligence (ai) and its implications for market knowledge in b2b marketing. purpose: the purpose of this paper is to explain the technological phenomenon artificial intelligence (ai) and how it can contribute to knowledge-based marketing in b2b. specifically, this paper describes the foundational building blocks of any artificial intelligence system and their interrelationships. this paper also discusses the implications of the different building blocks with respect to market knowledge in b2b marketing and outlines avenues for future research. design/methodology/approach: the paper is conceptual and proposes a framework to explicate the phenomenon ai and its building blocks. it further provides a structured discussion of how ai can contribute to different types of market knowledge critical for b2b marketing: customer knowledge, user knowledge and external market knowledge. findings: the paper explains ai from an input–processes–output lens and explicates the six foundational building blocks of any ai system. it also discussed how the combination of the building blocks transforms data into information and knowledge. practical implications: aimed at general marketing executives, rather than ai specialists, this paper explains the phenomenon artificial intelligence, how it works and its relevance for the knowledge-based marketing in b2b firms. the paper highlights illustrative use cases to show how ai can impact b2b marketing functions. originality/value: the study conceptualizes the technological phenomenon artificial intelligence from a knowledge management perspective and contributes to the literature on knowledge management in the era of big data. it addresses calls for more scholarly research on ai and b2b marketing.. artificial intelligence b2b marketing customer knowledge knowledge-based marketing machine learning market knowledge natural language processing user knowledge","rules of (household) engagement: technology as manager, assistant and intern. purpose: practitioners need to understand how households will engage with connected-home technologies or risk the failure of these innovations. current theory does not offer sufficient explanation for how households will engage; hence, this paper aims to address an important gap by examining how households set “rules of engagement” for connected-home technologies in the context of electricity use and monitoring. design/methodology/approach: a review of the extant psychology, technology and engagement literature is conducted and yields two research questions for exploration. the research questions are addressed via 43 in-depth household interviews. analysis includes thematic analysis and computerized text analysis. findings: the results include a typology of technology engagement (the “pip typology”) and discuss three main roles for technology in assisting households: intern, assistant and manager. key contributions are as follows: consumers in household settings may experience “compromised engagement” where the perceived middle option is selected even if no-one selected that option originally; households open to using connected-home technologies are often taking advantage of their ability to “delegate” engagement to technology, and because consumers humanize technology, they also expect technology to follow social roles and boundaries. research limitations/implications: future research may examine the pip typology quantitatively and/or in different contexts and would benefit from a longitudinal study to examine how household technology engagement evolves. four research propositions are provided, which may form the basis for future research. practical implications: recommendations for practitioners are presented regarding the benefits of keeping consumers at the heart of connected-home technology goods and services. specific design principles are provided. originality/value: this paper fulfills the need to understand how households will engage with connected-home technologies and the roles this technology may fulfill in the complex household service system.. consumer engagement new technology","future service technologies: is service research on track with business reality?. purpose: service technologies are transforming the business landscape rapidly. this paper aims to explore the current scope of research in regard to emerging service technologies by comparing the content of articles in academic journals with practitioner-oriented publication outlets. design/methodology/approach: a total of 5,118 technology-related articles from service journals, service conferences, business journals and business magazines are analyzed. text-mining on abstracts is used for the thematic and semantic analysis. common research themes and their relationships are depicted in a two-dimensional structured network. further, the sample is analyzed regarding various technologies mentioned in the gartner hype cycle. findings: the paper reveals differences in academic and business perspectives in regard to service technologies. in comparison to business journals, scientific service research is more focused on customer-related aspects of technology. service research has a less concrete focus on technology than in business publications. still, service conference articles show a broader scope of emerging service technologies than academic journal articles. research limitations/implications: scientific research should focus on more concrete service technologies. business magazines serve as a good source for that and the paper identifies several promising new technology fields. practical implications: although business magazines cover significantly more concrete service technologies, they miss the integrated perspective that academic articles usually offer. academia can help business to better align concrete technologies with different internal and external perspectives. originality/value: this paper serves as an introduction to the special issue future service technologies. additionally, a quantitative study of recent service technology research serves as a reality check for academic researchers on business reality and provides research and practical recommendations.. digital digital economy future leximancer self-service technology technology adoption text-mining","the relative effects of business-to-business (vs. business-to-consumer) service innovations on firm value and firm risk: an empirical analysis. many firms introduce both business-to-business service innovations (b2b-sis) and business-to-consumer service innovations (b2c-sis) and need to better allocate their resources. however, they are unsure about b2b-sis’ effects on firm value or risk, especially relative to those of b2c-sis. the authors address this problem by developing hypotheses that relate the number of b2b-sis and b2c-sis to firm value and firm risk together with the moderators (the number of product innovations and customer-focus innovations). to test the hypotheses, the authors develop and estimate a model using unique panel data of 2,263 sis across 15 industries over eight years assembled from multiple data sources and controlling for firm- and market-specific factors, heterogeneity, and endogeneity. they analyze innovation announcements using natural language processing. the results show that b2b-sis have a positive effect on firm value and an insignificant influence on firm risk. importantly, the effect of a b2b-si on firm value is significantly greater than that of a b2c-si. unlike b2c-sis, the effect of b2b-sis on firm value is greater when the firm has more product innovations. surprisingly, unlike b2c-sis, the effect of b2b-sis on firm value is less positive when the sis emphasize customers. these findings offer important insights about the relative value of b2b-sis.. b2b marketing finance–marketing interface innovation machine learning natural language processing services shareholder value strategy","what’s yours is mine: exploring customer voice on airbnb using text-mining approaches. purpose: this paper aims to investigate customers’ experiences with airbnb by text-mining customer reviews posted on the platform and comparing the extracted topics from online reviews between airbnb and the traditional hotel industry using topic modeling. design/methodology/approach: this research uses text-mining approaches, including content analysis and topic modeling (latent dirichlet allocation method), to examine 1,026,988 airbnb guest reviews of 50,933 listings in seven cities in the usa. findings: the content analysis shows that negative reviews are more authentic and credible than positive reviews on airbnb and that the occurrence of social words is positively related to positive emotion in reviews, but negatively related to negative emotion in reviews. a comparison of reviews on airbnb and hotel reviews shows unique topics on airbnb, namely, “late check-in”, “patio and deck view”, “food in kitchen”, “help from host”, “door lock/key”, “sleep/bed condition” and “host response”. research limitations/implications: the topic modeling result suggests that airbnb guests want to get to know and connect with the local community; thus, help from hosts on ways they can authentically experience the local community would be beneficial. in addition, the results suggest that customers emphasize their interaction with hosts; thus, to improve customer satisfaction, airbnb hosts should interact with guests and respond to guests’ inquiries quickly. practical implications: hotel managers should design marketing programs that fulfill customers’ desire for authentic and local experiences. the results also suggest that peer-to-peer accommodation platforms should improve online review systems to facilitate authentic reviews and help guests have a smooth check-in process. originality/value: this study is one of the first to examine consumer reviews in detail in the sharing economy and compare topics from consumer reviews between airbnb and hotels.. airbnb consumer reviews sharing economy text-mining topic modelling","measuring matching among brand associations in the fashion online communities. in this manuscript a novel approach to measure brand association matching in virtual environments is applied. this innovative approach allows to compare company-defined with consumers-perceived brand associations in online brand studies also in terms of strength. it combines: i) existing text-mining techniques to collect the data from online communities, and ii) hybrid log-linear models for estimating the company-defined and the consumers-perceived brand association matching and strength. this approach provides an innovative marketing tool for companies and brand managers for defining the brand communication as well as the brand strategies. the proposed method is applied to real data extracted from online fashion communities and related to a famous fashion brand.. brand alignment brand association strength hybrid log-linear models text-mining virtual communities","product recall and brand association. a netnographic analysis. according to european legislation, withdrawal of a food product from the market is mandatory whenever the company that produced it or distributed it has doubts about its compliance with food safety requirements. if the food product has already been made available to consumers, the company is obliged to inform them on why it has to be withdrawn, and, if other measures for health protection are insufficient, the same company is bound to recall the non-compliant product. cases of withdrawal or recall of food products occur almost on a daily basis in italy. and this is extremely likely to create serious issues to the companies involved, damaging brand image, while losing trust on the side of consumers. also, the relationship between consumers and brands constantly happens on a double track, physical and virtual. the latter concerns, in particular, the exchange of information, perceptions and feelings that develop within the virtual community of consumers. through the analysis of these online communities, and focusing on key components of brand association, this paper aims to assess how recalling food products negatively impacts on brand image.. brand association brand image netnography product recall text-mining virtual community","what happens in vegas stays on tripadvisor? a theory and technique to understand narrativity in consumer reviews. many consumers base their purchase decisions on online consumer reviews. an overlooked feature of these texts is their narrativity: the extent to which they tell a story. the authors construct a new theory of narrativity to link the narrative content and discourse of consumer reviews to consumer behavior. they also develop from scratch a computerized technique that reliably determines the degree of narrativity of 190,461 verbatim, online consumer reviews and validate the automated text analysis with two controlled experiments. more transporting (i.e., engaging) and persuasive reviews have better-developed characters and events as well as more emotionally changing genres and dramatic event orders. this interdisciplinary, multimethod research should help future researchers (1) predict how narrativity affects consumers' narrative transportation and persuasion, (2) measure the narrativity of large digital corpora of textual data, and (3) understand how this important linguistic feature varies along a continuum.. automated text analysis computational linguistics consumer reviews narrative persuasion narrative transportation storytelling","p2v-map: mapping market structures for large retail assortments. the authors propose a new, exploratory approach for analyzing market structures that leverages two recent methodological advances in natural language processing and machine learning. they customize a neural network language model to derive latent product attributes by analyzing the co-occurrences of products in shopping baskets. applying dimensionality reduction to the latent attributes yields a two-dimensional product map. this method is well-suited to retailers because it relies on data that are readily available from their checkout systems and facilitates their analyses of cross-category product complementarity, in addition to within-category substitution. the approach has high usability because it is automated, is scalable and does not require a priori assumptions. its results are easy to interpret and update as new market basket data are collected. the authors validate their approach both by conducting an extensive simulation study and by comparing their results with those of state-of-the-art, econometric methods for modeling product relationships. the application of this approach using data collected at a leading german grocery retailer underlines its usefulness and provides novel findings that are relevant to assortment-related decisions.. big data machine learning market structure analysis product maps retailing","triggers of positive ewom: exploration with web analytics. purpose: the purpose of this paper is to determine the triggers of positive electronic word of mouth (ewom) using real-time big data obtained from online retail sites/dedicated review sites. design/methodology/approach: in this study, real-time big data has been used and analysed through support vector machine, to segregate positive and negative ewom. thereafter, using natural language processing algorithms, this study has classified the triggers of positive ewom based on their relative importance across six product categories. findings: the most important triggers of positive ewom (like product experience, product type, product characteristics) were similar across different product categories. the second-level antecedents of positive ewom included the person(s) for whom the product is purchased, the price and the source of the product, packaging and eagerness in patronising a brand. practical implications: the findings of this study indicate that the marketers who are active in the digital forum should encourage and incentivise their satisfied consumers to disseminate positive ewom. consumers with special interest for any product type (mothers or doctors for baby food) may be incentivised to write positive ewom about the product’s ingredients/characteristics. companies can launch the sequels of existing television or online advertisements addressing “for whom the product is purchased”. originality/value: this study identified the triggers of the positive ewom using real-time big data extracted from online purchase platforms. this study also contributes to the literature by identifying the levels of triggers that are most, more and moderately important to the customers for writing positive reviews online.. big data ewom product type web analytics","a data-driven approach to guest experiences and satisfaction in sharing. accommodation sharing provides unique experiences for tourists. this study collects 33,892 guest reviews of airbnb. a text analytic approach to deconstruct the nature of sharing experiences is explored. the data-driven approach extracts the following dimensions contributing to the core experiences of sharing, namely, housing, location, host-guest interaction, facility, overall sensation and others. a regression analysis further demonstrates a strong association between attributes of sharing experiences and guest satisfaction. factors such as room facilities and home experience convey different semantic space and significantly affect guest satisfaction. they serve as hygiene factor and motivator, respectively, confirming the two-factor theory.. airbnb experiences satisfaction sharing economy text analytics","making sense of customer service experiences: a text mining review. purpose: the purpose of this paper is to systematically review the concepts and theories underlying customer service experience (cse) and its underlying five dimensions (physical, social, cognitive, affective and sensorial). in this research, the contribution of the sensorial dimension to cse research is emphasized. senses are especially important in forming perceptions within servicescapes that are typically rich in sensory stimuli. design/methodology/approach: this study systematically identifies 258 articles published between 1994 and 2018 in services and marketing journals. the analysis uses a text mining approach with the leximancer software to extract research concepts and their relationships. findings: the results demonstrate a shift from cse research focused on brands and products toward value and interaction, around three focal areas: service system architecture, with its value creation processes; servicescape, with an increasingly digital interaction interface and outcome measures, with a stronger focus on emotional and relational metrics. in cse research, the physical, social and cognitive dimensions are mostly researched in the focal areas of servicescape and outcome measures. although important in practice, the sensorial dimension is the least investigated cse dimension in service marketing research. text mining insights demonstrate rich opportunities for sensorial research, particularly in studies on servicescape. practical implications: the synthesis will inform managers and service providers which elements of cse are most relevant to customers when forming perceptions. these insights help service providers to control, manage and design (multi)-sensory stimuli that influence how customers will make sense of the servicescape. originality/value: this research is one of the first studies to examine the conceptual structure of cse with a text mining approach that systematically analyzes a large set of articles, therein reducing the potential for researchers’ interpretative bias. the paper provides an assessment of the role of the largely neglected but crucial sensorial dimension, and offers future research suggestions into this emerging topic.. customer experience leximancer multisensory review sensory service research servicescape text mining","detecting, preventing, and mitigating online firestorms in brand communities. online firestorms pose severe threats to online brand communities. any negative electronic word of mouth (ewom) has the potential to become an online firestorm, yet not every post does, so finding ways to detect and respond to negative ewom constitutes a critical managerial priority. the authors develop a comprehensive framework that integrates different drivers of negative ewom and the response approaches that firms use to engage in and disengage from online conversations with complaining customers. a text-mining study of negative ewom demonstrates distinct impacts of high-and low-arousal emotions, structural tie strength, and linguistic style match (between sender and brand community) on firestorm potential. the firm’s response must be tailored to the intensity of arousal in the negative ewom to limit the virality of potential online firestorms. the impact of initiated firestorms can be mitigated by distinct firm responses over time, and the effectiveness of different disengagement approaches also varies with their timing. for managers, these insights provide guidance on how to detect and reduce the virality of online firestorms.. message dynamics online brand community online firestorms text mining word of mouth","consumer perceived value preferences for mobile marketing in china: a mixed method approach. this study aimed to identify a new framework for consumer perceived value (cpv) and evaluate the dynamics of relative importance of different dimensions of cpv in the context of mobile marketing. laddering interviews were conducted to capture the essence of cpvs, and then text-mining techniques were applied to extract key consumer values from the interviews. six dimensions of cpv, namely, design, emotional, functional, monetary, guarantee, and social, were identified. the construct validity of these six dimensions was demonstrated through a rigorous sorting process. a best–worst scaling (bws) survey was then implemented based on these six value dimensions to investigate consumers’ preference for each dimension in three critical decision-making phases of mobile marketing campaigns. statistical analysis of the bws data showed that significant dynamic differences exist among these six value dimensions in each phase. gender difference and consumer heterogeneity were also presented. theoretical and managerial implications were discussed.. consumer perceived value mixed method mobile marketing value dimensions wechat official account","text analysis in consumer research: an overview and tutorial. na. na","selectively emotional: how smartphone use changes user-generated content. user-generated content has become ubiquitous and very influential in the marketplace. increasingly, this content is generated on smartphones rather than personal computers (pcs). this article argues that because of its physically constrained nature, smartphone (vs. pc) use leads consumers to generate briefer content, which encourages them to focus on the overall gist of their experiences. this focus on gist, in turn, tends to manifest as reviews that emphasize the emotional aspects of an experience in lieu of more specific details. across five studies—two field studies and three controlled experiments—the authors use natural language processing tools and human assessments to analyze the linguistic characteristics of user-generated content. the findings support the thesis that smartphone use results in the creation of content that is less specific and privileges affect—especially positive affect—relative to pc-generated content. the findings also show that differences in emotional content are driven by the tendency to generate briefer content on smartphones rather than user self-selection, differences in topical content, or timing of writing. implications for research and practice are discussed.. affect emotion mobile marketing natural language processing social media word of mouth","unfolding the characteristics of incentivized online reviews. the rapid growth of social media in the last decades led e-commerce into a new era of value co-creation between the seller and the consumer. since there is no contact with the product, people have to rely on the description of the seller, knowing that sometimes it may be biased and not entirely true. therefore, review systems emerged to provide more trustworthy sources of information, since customer opinions may be less biased. however, the need to control the consumers’ opinion increased once sellers realized the importance of reviews and their direct impact on sales. one of the methods often used was to offer customers a specific product in exchange for an honest review. yet, these incentivized reviews bias results and skew the overall rating of the products. the current study uses a data mining approach to predict whether or not a new review published was incentivized based on several review features such as the overall rating, the helpfulness rate, and the review length, among others. additionally, the model was enriched with sentiment score features of the reviews computed through the vader algorithm. the results provide an in-depth understanding of the phenomenon by identifying the most relevant features which enable to differentiate an incentivized from a non-incentivized review, thus providing users and companies with a simple set of rules to identify reviews that are biased without any disclaimer. such rules include the length of a review, its helpfulness rate, and the overall sentiment polarity score.. incentivized online reviews sentiment analysis text mining","industrial-buying research 1965-2015: review and analysis. purpose: the purpose of this paper is to investigate the underlying knowledge structure and evolution of industrial-buying research published between 1965 and 2015. design/methodology/approach: bibliometric analysis is performed on 357 relevant papers (using principal component analysis and natural language processing, using vantagepoint® tools, used to generate bubble maps, auto-correlation maps and aduna cluster maps), demonstrating how various factors involved in industrial buying have evolved, their degree of correlation with each other and the interrelationships of multiple factors concerning their co-occurrences. findings: the systematic mapping of industrial-buying research would illustrate the development of the significant factors in industrial-buying research. this paper provides both a global perspective on the leading countries and journals in the field and a robust roadmap for further investigation in this field. research limitations/implications: this paper is limited to the data considered for analysis and may, therefore, overlook or underestimate some work that has not been captured while filtering databases related to industrial buying. practical implications: this paper facilitates near-future projection and trend analysis in industrial-buying research. originality/value: the methodology used is unique to the field of business-to-business marketing.. business-to-business marketing quantitative research","comparing automated text classification methods. online social media drive the growth of unstructured text data. many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. several methods have been proposed to automatically classify unstructured text. this paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. so far, marketing research relies predominantly on support vector machines (svm) and linguistic inquiry and word count (liwc). across all tasks we study, either random forest (rf) or naive bayes (nb) performs best in terms of correctly uncovering human intuition. in particular, rf exhibits consistently high performance for three-class sentiment, nb for small samples sizes. svm never outperform the remaining methods. all lexicon-based approaches, liwc in particular, perform poorly compared with machine learning. in some applications, accuracies only slightly exceed chance. since additional considerations of text classification choice are also in favor of nb and rf, our results suggest that marketing research can benefit from considering these alternatives.. machine learning natural language processing sentiment analysis social media text classification user-generated content","exploring online customer engagement with hospitality products and its relationship with involvement, emotional states, experience and brand advocacy. opinions published online about a given experience are known to influence consumers’ decisions. however, such opinions reflect different degrees of engagement which may affect the decisions in different forms. the aim of this study is to analyze the dimensions of online customer engagement and associated concepts (involvement, emotional states, experience and brand advocacy) in customers’ online reviews through text-mining and sentiment analysis trends. the current study focuses on yelp.com comments and includes a random sample of 15,000 unique reviews of restaurants, hotels and nightlife entertainment in eleven cities in the usa. a customer engagement dictionary is created, based on previously validated scales and extended using wordnet 2.1. the research findings reveal a high impact of the engagement cognitive processing dimension and hedonic experience on customers’ review endeavor. results further indicate that customers seem to be more engaged in positively advocating a company/brand than the contrary. companies that listen to such feedback may be more able to align with consumers’ expectations and, therefore, have a better competitive advantage in the market.. brand advocacy customer engagement emotional states involvement online experience user-generated content","extracting features of entertainment products: a guided latent dirichlet allocation approach informed by the psychology of media consumption. the authors propose a quantitative approach for describing entertainment products, in a way that allows for improving the predictive performance of consumer choice models for these products. their approach is based on the media psychology literature, which suggests that people’s consumption of entertainment products is influenced by the psychological themes featured in these products. they classify psychological themes on the basis of the “character strengths” taxonomy from the positive psychology literature (peterson and seligman 2004). they develop a natural language processing tool, guided latent dirichlet allocation (lda), that automatically extracts a set of features of entertainment products from their descriptions. guided lda is flexible enough to allow features to be informed by psychological themes while allowing other relevant dimensions to emerge. the authors apply this tool to movies and show that guided lda features help better predict movie-watching behavior at the individual level. they find this result with both award-winning movies and blockbuster movies. they illustrate the potential of the proposed approach in pure content-based predictive models of consumer behavior, as well as in hybrid predictive models that combine content-based models with collaborative filtering. they also show that guided lda can improve the performance of models that predict aggregate outcomes.. entertainment industry media psychology natural language processing positive psychology topic models","cutting through content clutter: how speech and image acts drive consumer sharing of social media brand messages. consumer-to-consumer brand message sharing is pivotal for effective social media marketing. even as companies join social media conversations and generate millions of brand messages, it remains unclear what, how, and when brand messages stand out and prompt sharing by consumers. with a conceptual extension of speech act theory, this study offers a granular assessment of brands’ message intentions (i.e., assertive, expressive, or directive) and the effects on consumer sharing. a text mining study of more than two years of facebook posts and twitter tweets by well-known consumer brands empirically demonstrates the impacts of distinct message intentions on consumers’ message sharing. specifically, the use of rhetorical styles (alliteration and repetitions) and cross-message compositions enhance consumer message sharing. as a further extension, an image-based study demonstrates that the presence of visuals, or so-called image acts, increases the ability to account for message sharing. the findings explicate brand message sharing by consumers and thus offer guidance to content managers for developing more effective conversational strategies in social media marketing.. brand communications consumer sharing image acts message dynamics rhetoric social media speech act theory text mining","how user-generated judgments of hotel attributes indicate guest satisfaction. user-generated content is a major source of information particularly in tourism industry where consumers seek unbiased and unregulated information. while making their hotel booking decisions, consumers refer to the previous guests’ experiences expressed in the hotel reviews across social media. studies in the literature have focused on enhancing the understanding of what makes customers satisfied or dissatisfied. they have analyzed the text reviews and the patterns in the overall rating and ranking of the hotels given by hotel guests. while most emerging destinations have been studied in the literature, studies related to india, a fast-growing leisure destination are scant. the present study tries to understand the hotel attributes that contribute towards customer satisfaction or dissatisfaction using online reviews for all hotels of andaman & nicobar islands in india. among the eight attributes identified from the most frequently used words in the text reviews, the study finds that while the location has a significant probability of increasing the ratings of both high- and low-rated reviews, rooms seem to have the most significant impact on lowering the probability of high scores irrespective of positive or negative sentiment review. the study also finds that guests of luxury hotels rate the hotels significantly higher than the guests of midrange and budget hotels.. customer satisfaction hotel attributes online reviews sentiment analysis text mining","rapid assessment of customer marketplace in disaster settings through machine learning, geospatial information, and social media text mining: an abstract. in disasters there is a need to rapidly assess and predict impact area boundaries (for staging emergency relief and product distribution dynamics), delineate and communicate with the affected public (for coordinated disaster assistance such as evacuation, search and rescue, prioritization of manpower and equipment usage, etc.), and provide cost estimation for potential use in government-to-government (g2g) marketing (golden and peterson 2018) soliciting relief and assistance funds from federal and/or state authorities. this rapid assessment needs to occur as the disaster unfolds and immediately after the disaster, even while data is scarce and emerging. marketing communication techniques and modern information technology methods (machine learning, geospatial information, and social media text mining) can help with these tasks. this paper shows how to use predictive modeling and geospatial interpolation along with machine learning for pre-disaster community planning, for risk management actions during the disaster, and for assisting in creating informed structure regulations to assist in disaster resilience. as disaster response is an ongoing process, the technique provides a framework of spatial level loss estimation using limited evolving data and iteratively improves estimation as more data becomes available. predictive and probabilistic modeling starts with historical data (compiled before the event) including data such as damage to a specific building type as a function of water depth or wind speed. topography of the land involved is assessed using satellite and lidar data and publically available flood maps created by fema for the national flood insurance program and topographic maps produced in the us topo project of the national geospatial program of the us geological survey (usgs). these baseline gis datasets are overlaid with gis-located data on building type and construction materials and usage scraped from county tax assessor websites. after buildings are gis mapped, the fema’s hazus algorithm is used to estimate damages to each building as a function of projected area water depth. the water depth projection is determined by regression using wind speed and recurrence interval (i.e., hurricane harvey in texas in 2017 was a 1 in 1,000 year event). as data becomes gradually available, we iteratively update estimates for areas using spatial interpolation. this informs communication modes of contact. to better assess impact area and population at risk with limited data, machine learning is applied. in disasters people often are active on mobile phones. gis located text mining messages reveals who needs what and where, directs rescue, and improve damage estimates when data is sparse, and allow target messaging to vulnerable populations (lazreg et al. 2016). references available upon request. na","special session: an abstract on “marketing techniques to assist public sectors in engaging customers to meet societal and individual disasters crisis need and beyond”. this session brings together academics, consultants, and government professionals to discuss ways marketing can help address needs in a natural disaster. extreme weather events have increased the visibility of marketing’s importance in relief and recovery solutions well beyond disaster fundraising. effective rapid response marketing (rrm) is critical. the dynamic and rapidly increasing developments in technology from apps to social media to machine learning and text mining can help marketers develop better rapid response marketing in any crisis situation. the first presentation, “g2g marketing from crisis to resolution: marketing for when the government must target the government” (linda golden, robert peterson), introduces the concept of government-to-government (g2g) marketing and public policy marketing (ppm). government agencies can be clients and suppliers to each other at different times, and states may compete for “their share” of federal funds. marketing approaches and contributions are discussed. the second presentation (alissa walsh, christopher emrich, marco bravo) introduces the social vulnerability index (sovi) to marketing and discusses its use in disaster management by facilitating directed marketing toward needs of the most vulnerable groups in disaster situations. the third presentation (rajiv garg, patrick brockett, linda golden, yuxin zhang) shows how techniques like machine learning, geospatial information, and social media text mining can assist in rapid assessment of damage and affected areas and populations before or as the disaster unfolds. the fourth presentation (peter nance, pete phillips) shows how integrated marketing communication (imc) is critical in disaster response and recovery. messaging must be strategically developed and delivered before, during, and after a crisis. the final presentation (colleen jones, alexandra gamble) gives real examples of how the texas general land office, charged with disaster resilience and recovery, implements marketing techniques to accomplish strategic goals and action plans. recent hurricanes provide examples. references available upon request. na","big consumer behavior data and their analytics: some challenges and solutions. this chapter contributes to the still very reduced marketing literature that deals with big consumer behavior data using cloud analytics by summarizing some of the main extant academic researches and by introducing new applications, datasets, and technologies in order to complete the picture. both internal “purchase history” and external web-based customer reviews and social media data are discussed, organized, and analyzed. they cover volume and variety aspects that define big data and uncover analytic complexities that need to be dealt with.. big data mapreduce sentiment analysis social mining text mining","a framework for big data analytics in commercial social networks: a case study on sentiment analysis and fake review detection for marketing decision-making. user-generated content about brands is an important source of big data that can be transformed into valuable information. a huge number of items are reviewed and rated by consumers on a daily basis, and managers have a keen interest in real-time monitoring of this information to improve decision-making. the main challenge is to mine reliable textual consumer opinions, and automatically use them to rate the best products or brands. we propose a framework to automatically analyse these reviews, transforming negative and positive user opinions in a quantitative score. sentiment analysis was employed to analyse online reviews on amazon. the fake review detection framework—frdf— detects and removes fake reviews using natural language processing technology. the frdf was tested on reviews of products from high-tech industries. brands were rated according to consumer sentiment. the findings demonstrate that brand managers and consumers would find this tool useful, in combination with the 5-star score, for more comprehensive decision-making. for instance, the frdf ranks the best products by price alongside their respective sentiment value and the 5-star score.. big data analytics fake reviews high-tech industries marketing decisions sentiment analysis","the coming democratisation of emotions analytics. the ability to understand emotion from human communication is essential for marketers and data analysts, particularly in a world of growing non-verbal communication, such as reviews, complaints, surveys social media and chatbots. thanks to advancements in artificial intelligence (ai) text analytics, a lot of information typically missed in sentiment analysis can now be analysed and early warning of issues flagged much earlier. this paper looks at how ai text analytics will succeed sentiment analysis and how it can identify additional customer emotion and intent by using concepts rather than simple keywords in its analysis. the paper also looks at how the most successful ai text analytics feature human-in-the-loop technology that optimises the combination of automation and human involvement. this makes the need for data scientists to run day-to-day models obsolete as the human input can come from the business user instead. the paper presents examples of how ai text analytics and emotions analytics can be applied, and argues that ai text analytics can reduce reliance on data scientists, provide early warnings of issues and analyse a much deeper level of sentiment and customer intent with higher levels of accuracy.. ai customer data customer satisfaction data science machine learning sentiment analysis text analytics","identifying customer needs from user-generated content. firms traditionally rely on interviews and focus groups to identify customer needs for marketing strategy and product development. user-generated content (ugc) is a promising alternative source for identifying customer needs. however, established methods are neither efficient nor effective for large ugc corpora because much content is noninformative or repetitive. we propose a machine-learning approach to facilitate qualitative analysis by selecting content for efficient review. we use a convolutional neural network to filter out noninformative content and cluster dense sentence embeddings to avoid sampling repetitive content. we further address two key questions: are ugc-based customer needs comparable to interview-based customer needs? do the machine-learning methods improve customer-need identification? these comparisons are enabled by a custom data set of customer needs for oral care products identified by professional analysts using industry-standard experiential interviews. the analysts also coded 12,000 ugc sentences to identify which previously identified customer needs and/or new customer needs were articulated in each sentence. we show that (1) ugc is at least as valuable as a source of customer needs for product development, likely more valuable, compared with conventional methods, and (2) machine-learning methods improve efficiency of identifying customer needs from ugc (unique customer needs per unit of professional services cost).. customer needs deep learning machine learning market research natural language processing online reviews text mining user-generated content voice of the customer","feelings generated by threat appeals in social marketing: text and emoji analysis of user reactions to anorexia nervosa campaigns in social media. threat appeals in social marketing have been widely researched regarding their effects in behaviour change. however, little is known about their emotional effects in individuals. feelings generated by threat appeals have proved to be ambiguous. considering that understanding the emotional effects of message frames has implications in long-term behaviour change, this paper aims at understanding the feelings generated by threat appeals, considering the inconsistent findings in the literature. the research analyses the feelings produced by threat appeals in two social networks - facebook and youtube. a sentiment analysis of forty non-governmental campaigns regarding anorexia nervosa awareness was conducted through two methodological forms. first, we have analysed the content of the comments made by users by text analysis; second, we have coded the emoji expressing feelings from the users in the same campaigns and have quantified their interactions. results indicate that feelings generated by threat appeals regarding anorexia nervosa campaigns in social media may be both positive and negative, with a great expression of fear, sadness and empathy, corroborating the ambiguous findings. positive feelings are most prominent in emoji and reveal support, compassion and admiration both for campaign messages and for people suffering from anorexia. negative feelings, such as fear and sadness, arise especially as a consequence of awareness and concerns. the paper contributes to the discussion of this ambivalent topic of research and also experiments two different sentiment analysis techniques – text and emoji analysis -, with different result outcomes.. anorexia campaigns emoji analysis sentiment analysis social media text analysis threat appeals","a decision support system framework to track consumer sentiments in social media. with the evolution of web 2.0 and social networks, customers and companies’ online interaction is growing at a fast pace, containing valuable insights about consumers’ expectations that should be monitored and explored in a day-to-day basis. however, such information is highly unstructured and difficult to analyze. there is an urgent need to set up transparent methods and processes to integrate such information in the tourism industry technological infrastructure, especially for small firms that are unable to pay for expensive services to monitor their online reputation. the current paper uses a text mining and sentimental analysis technique to structure online reviews and present them on a decision support system with two different dashboards to assist in decision-making. such system may help managers develop new insights and strategies aligned with consumers’ expectations in a much more flexible and sustainable pace.. decision support system sentiment analysis social media text mining tourist destination","unstructured data in marketing. the rise of unstructured data (ud), propelled by novel technologies, is reshaping markets and the management of marketing activities. yet these increased data remain mostly untapped by many firms, suggesting the potential for further research developments. the integrative framework proposed in this study addresses the nature of ud and pursues theoretical richness and computational advancements by integrating insights from other disciplines. this article makes three main contributions to the literature by (1) offering a unifying definition and conceptualization of ud in marketing; (2) bridging disjoint literature with an organizing framework that synthesizes various subsets of ud relevant for marketing management through an integrative review; and (3) identifying substantive, computational, and theoretical gaps in extant literature and ways to leverage interdisciplinary knowledge to advance marketing research by applying ud analyses to underdeveloped areas.. acoustic artificial intelligence big data deep learning image linguistics machine learning nonverbal text text mining unstructured data video voice","assessment of service quality using text mining – evidence from private sector banks in india. purpose: as the retail banking institutions are becoming more customer centric, their focus on service quality is increasing. established service quality frameworks such as servqual and servperf have been applied in the banking sector. while these models are widely accepted, they are expensive because of the need for replication across bank branches. the purpose of this paper is to propose a novel, user friendly and cost effective approach by amalgamating the traditional concept of service quality in banks (marketing base) and sentiment analysis literature (information systems base). design/methodology/approach: in this study, the main objective is to analyze user reviews to better understand the correlation between rater dimension sentiment scores as independent variables and user overall rating (customer satisfaction) grouping in “good” and “bad” as dependent variable through development of authors’ own logistic regression model using lexicon-based sentiment analysis. the model has been developed for three largest private banks in india pertaining to three banking product categories of loans, savings and current accounts and credit cards. findings: the results show that the responsiveness and tangibles dimensions significantly impact the user evaluation rating. even though the three largest private banks in india are concentrating on the tangibles dimension, not all of them are sufficiently focused on the responsiveness dimension. additionally, customers looking for loan products are more susceptible to negative perceptions on service quality. originality/value: this study has highlighted two types of scores whereby user provided overall evaluation scores help provide validation to the sentiment scores. the developed model can be used to assess performance of a bank in comparison to its peers and to generate in depth insights on point of parity (pop) and point of difference (pod) fronts.. customer satisfaction service marketing service quality","online sentiment analysis in marketing research: a review. purpose: the explosion of internet-generated content, coupled with methodologies such as sentiment analysis, present exciting opportunities for marketers to generate market intelligence on consumer attitudes and brand opinions. the purpose of this paper is to review the marketing literature on online sentiment analysis and examines the application of sentiment analysis from three main perspectives: the unit of analysis, sampling design and methods used in sentiment detection and statistical analysis. design/methodology/approach: the paper reviews the prior literature on the application of online sentiment analysis published in marketing journals over the period 2008-2016. findings: the findings highlight the uniqueness of online sentiment analysis in action-oriented marketing research and examine the technical, practical and ethical challenges faced by researchers. practical implications: the paper discusses the application of sentiment analysis in marketing research and offers recommendations to address the challenges researchers confront in using this technique. originality/value: this study provides academics and practitioners with a comprehensive review of the application of online sentiment analysis within the marketing discipline. the paper focuses attention on the limitations surrounding the utilization of this technique and provides suggestions for mitigating these challenges.. high technology marketing methodology online marketing qualitative research quantitative research text mining","a synthesis of the consumer-brand relationship domain: using text mining to track research streams, describe their emotional associations, and identify future research priorities. we conduct a text-mining analysis of 287 articles representing the consumer-brand relationship (cbr) literature from 1999 to 2015. we propose that the cbr domain is reflected by 71 constructs, of which less than half can be considered important. we structure the domain by identifying its seven major research streams as well as demarcating their respective evolution and emotional features. using the original data and incorporating a second corpus based on the articles included in the current journal of the association for consumer research issue on “brand relationships, emotions, and the self”, we outline a collection of insights that define opportunities for future research.. na","automated text analysis for consumer research. the amount of digital text available for analysis by consumer researchers has risen dramatically. consumer discussions on the internet, product reviews, and digital archives of news articles and press releases are just a few potential sources for insights about consumer attitudes, interaction, and culture. drawing from linguistic theory and methods, this article presents an overview of automated text analysis, providing integration of linguistic theory with constructs commonly used in consumer research, guidance for choosing amongst methods, and advice for resolving sampling and statistical issues unique to text analysis. we argue that although automated text analysis cannot be used to study all phenomena, it is a useful tool for examining patterns in text that neither researchers nor consumers can detect unaided. text analysis can be used to examine psychological and sociological constructs in consumerproduced digital text by enabling discovery or by providing ecological validity.. automated content analysis automated text analysis computational linguistics computer-assisted text analysis","topic modelling for open-ended survey responses. due to the availability of massive amounts of text data, both from online (twitter, facebook, online forums, etc) and offline open-ended survey questions, text analytics is growing in marketing research and analytics. most companies are now using open-ended survey questions to solicit customer opinions on any number of topics (eg ‘how can we improve our service?’). with large sample sizes, however, the task of collating this information manually is practically impossible. this paper describes an end-to-end process to extract insight from text survey data via topic modelling. a case study from a fortune 500 firm is used to illustrate the process.. latent dirichlet allocation natural language processing open-ended questions text analysis topic modelling","conversational marketing: creating compelling customer connections. conversational interfaces allow people to direct devices and programs through natural dialogue. chatbots (which integrate with messaging apps like facebook messenger) and virtual assistants (including apple’s siri and amazon’s alexa) are examples of conversational interfaces that have emerged in recent years. as adoption of conversational interfaces increases, so will opportunities for marketers to utilise them to better meet consumer needs. this paper highlights the trends driving the rise of conversational interfaces, identifies the types of conversational ‘jobs to be done’ for customers in the context of the marketing funnel, and discusses the implications of conversational data for marketers.. artificial intelligence chat chatbots conversational interfaces conversational marketing natural language processing smart speakers virtual assistants voice","designing a marketing analytics course for the digital age. marketing analytics is receiving great attention because of evolving technology and the radical changes in the marketing environment. this study aims to assist the design and implementation of a marketing analytics course. we assembled a rich data set from four sources: business executives, 400 employers’ job postings, one million tweets about marketing analytics from twitter, and 13 course syllabi downloaded from the internet. we apply text-mining techniques to generate insights from this big data set of electronic texts. by integrating insights from marketing practitioners and professors, we have designed a marketing analytics course and offer solutions for better structuring such courses. this study provides suggestions for marketing educators.. na","quantitative insights from qualitative data: using the doubling technique in correspondence analysis: an abstract. we describe a study that began as a qualitative research piece, involving a series of depth interviews with a wide spectrum of art collectors. text data from these interviews were analyzed using watson, a natural language processing content analysis software that enables an identification of the main personality traits of each respondent. this software produced output percentile scores on the big five personality traits. the big five is the most widely used personality model and describes how a person engages with the world based on five dimensions: intro-/extraversion, agreeableness, conscientiousness, emotional stability, and openness. this data in turn became the input for a statistical analysis tool, correspondence analysis, which enabled us to group the respondents according to their personality traits and distinguish among different subgroups of art collectors to form more homogenous groups of art collectors based on the personality profiles. due to the percentile scores of the output, we employed the use of a doubling technique to yield more valid results from the correspondence analysis. the doubling technique involved creating two points for each trait – the positive pole and the negative pole. this technique aided as a visual assessment for the grouping of art collectors into more homogenous groups based on personalities. the interpretation of the “doubled” points is effectively a visual assessment, for the total sample, of the distribution of the participant percentile scores for each of the five personality trait variables, which aided in grouping the respondents into distinct groups. results showed four distinct groups of art collectors based on the relative personality profiles. the significance and implications of these results are discussed in regards to the four “types” of art collectors. methodologically, this research addresses a means for marketing managers to uncover more insight into the psychographic traits of consumers.. correspondence analysis doubling technique main personality traits psychographic traits quantitative insights","“i can’t wait to see this”: an exploratory research on consumer online word-of-mouth on movies: an abstract. the success of a movie is often determined by its opening weekend performance (earnest, 1985; epstein, 2005; gong et al., 2011). using the most effective movie advertising tool, studios release trailers early in advance aiming to build heavy pre-release buzz which will in turn drive audiences to the cinema on the opening weekend. while pre-release movie buzz has proved to be instrumental in influencing box office performance, most electronic word-of-mouth (ewom) research on movies is limited to the quantitative measurement of wom metrics (e.g. volume, valence) (hennig-thurau et al., 2015; liu, 2006), overlooking other significant information that could offer insight on early audience perceptions. very recent research on the antecedents of movie wom has identified that the combination of liking the trailer along with understanding what the movie is about increases the likelihood of viewers engaging in favourable pre-release wom and in paying to see the movie at the cinema (archer-brown et al,. 2017). focusing on the concept of understanding, this work-in-progress aims to investigate the mechanisms through which consumers draw inferences and eventually form perceptions on upcoming movies when viewing trailers. approximately seven million data points on wide-release movies have been collected from twitter and youtube since november 2015. content analysis will be performed on the data through automated natural language processing techniques, where networks of words will be drawn as an innovative way to visualise user-generated content. early results on the analysis of three movies suggest that viewers utilise different techniques in inference-making depending on prior knowledge about the movie. ewom on sequel movies focuses on the appearance and on the prominence of characters, while wom on true story adaptations is richer with debates on how events truly happened and with the provision of secondary sources of information. finally, wom on movies with original storylines suggests that viewers rely on heuristic cues – such as the stars’ and directors’ previous work or other same-genre movies – when trying to fill in missing information about the movie’s plot. this is the first part of an ongoing research project which eventually aims to construct a framework for classifying comprehensive trailers and identifying their relationship to favourable wom and to box office performance. theoretical contributions will be made towards advertising and wom theory, by delving deeper into a newly added wom antecedent. managerial contributions include the design of effectively comprehensive promotional material as well as methodologies which can be adopted to gain insight on early audience perceptions.. na","an exploratory analysis of consumer opinions, ethics, and sentiment of neuromarketing: an abstract. neuromarketing can be defined as both an innovative research methodology that makes use of neuroscience techniques and as field of study that merges marketing principles and neuroscience theory. as a research methodology, neuromarketing has the potential to detect unconscious processes and activities that are difficult to access through traditional advertising research methods. as a field of study, neuromarketing refers to a link between neuroscience theory (and methods) and marketing techniques. the emergence of neuromarketing as a new field of study has created ethical concerns about the use and misuse of neuroscience technologies, under the premise of advancing the understanding of consumer behavior. the ethical issues concerning neuromarketing research suggest that consumers need more information about neuromarketing in order to understand its implications and to protect themselves from the negligent research behaviors of companies. thus, taking into consideration the relevant role of consumers in neuromarketing as both participants of the experiments and final recipients of the “benefits” derived from firms’ marketing decisions, an understanding of what consumers think about neuromarketing becomes critical. to examine consumer opinions, a content analysis of neuromarketing blogs and social media sites was selected as the exploratory method to uncover the “voice” of consumers about neuromarketing. three text-analytics techniques were used to examine the texts, namely, word frequency analysis, story network analysis, and sentiment analysis. the implications of this content analysis of neuromarketing texts from blogs and social media are twofold. first, the three-step methodology used for the analysis of texts represents a helpful tool for marketers and demonstrates the importance of user-generated content (ugc) as a valuable source of consumer information. conventional wisdom suggests that listening to what customers say and where they are saying it, can help a firm make decisions regarding their brands, products, and services. marketers must recognize that more effort has to be allocated to social media observation and the analysis of the data provided through these sites. second, the findings of this research represent the “voice” of consumers regarding the emerging field of neuromarketing. this exploratory study can help neuromarketing research firms, scientists, academics, and marketers understand consumer perceptions, feelings, and attitudes toward neuromarketing. specifically, this research makes evident that consumer sentiment of neuromarketing is mostly positive; however, some concerns regarding the ethical use of the method still need to be addressed. therefore, it is critical that government, companies, neuromarketing firms, researchers, and consumer advocacy organizations work together in the creation of regulations, standards, and a code of ethics for neuromarketing.. na","the transformation of global brands: an abstract. currently, using the technique of data analysis to assist brand management is very popular. in the rich digital data market, there is a great deal of general literature on exploring customers’ responses. however, specific guidance on what kind of global brands could survive in the high competitions is lacking. while the management of global brands has been extensively investigated, the transformation of global best brands is relatively unexplored. this paper introduces the possibility of text analytics to provide a framework for the profile and change of the successful global brands, by using interbrand’s best global brands ranking report as a reference source (interbrand 2017). na","an abstract: healthy food promotion with an app?. connected devices can be used in a social marketing perspective to mix virtual entertainment and real commitment to a behavioral change, such as a healthier diet for a better life. whereas the literature on healthy diet programs based on the use of connected device(s) include numerous surveys, our review showed a low use of theoretical frameworks in previous studies. furthermore nearly all studies reported a decrease in program usage throughout the intervention period. as a conclusion, behavioral changes have been observed in a certain amount of studies but small and on short periods, conclusions remaining rather unclear. moreover most studies concentrated on extrinsic factors and hardly took into account intrinsic factors such as participants’ motivations and barriers to engaging themselves in a long-term healthy diet program. following a transformative agenda (mick et al. 2012), we intend to consider our target as experts on their needs and wants toward a healthier diet and the potential help of connected devices. we conducted a qualitative research in parisian region (france) with four focus groups (27 participants). we targeted both men and women having tried to lose weight in the last 5 years; the sample diversity ensures the representation of other important criteria, such as educational level and the familiarity with connected devices. during the focus groups, we used a probing approach (mattelmäki 2008) for co-exploring which device and which design could better help the participants to lose weight in a long term, considering them as collaborative stakeholders. data analysis used softwares (nvivo 11 and alceste) for a text analysis and a content analysis. results highlight motivation as the major factor compared to the type of devices used (website, app, or connected devices such as fitbit or hapifork) and the type of messages. they suggest that self-determination (ryan and deci 2000) is a major factor to participants’ motivations and compliance to digital tailored prompt messages. clearly, a friendly app is their favorite tool. they consider it as their best ally to follow a diet program. our results indicate this importance of maintaining motivation in a long term by satisfying the needs of autonomy, competence, and relatedness. all the participants point out the need for being nudged, not judged. nevertheless, less educated and less familiar to it participants are the most reluctant to digital devices.. na","towards an analytical framework to understand consumer disengagement with digital advertising: an abstract. this study is dedicated to the understanding of digital disengagement. this phenomenon is seen in the context of digital advertising. digital advertisements feature varied content related to products and services. this content is supposed to be in line with the requirements and life worlds of the individuals consuming the content of digital advertisements. if the content and presentation are in sync with the former, such digital advertisements are favoured with the preference of the individuals consuming them. thus, digital advertisements face two specific outcomes in terms of the individuals consuming them: approach and avoidance. the basis of digital disengagement is the interaction with the digital advertisements based on the enhancement of the individual’s well-being. hence, engagement and disengagement vary depending on the context of the consumers and their engagement. however, there are no studies that have attempted to discover disengagement, especially in the context of digital advertising. therefore, this study has extensively studied the literature pertaining to consumer engagement. this was supplemented by an equally exhaustive review and study of fields related to disengagement. it was imperative to study these fields to effect vertical transfer. vertical transfer is necessary when the area of study does not have literature that has investigated it. based on this exhaustive review, we were able to discover the key dimensions necessary to understand digital disengagement; personal values, functionality, connectedness with daily activities and relations with the brands and products. in order to develop these dimensions into the integral parts of the framework to understand the nature of digital disengagement, we used the exploratory approach. we performed two studies, text mining and expert interviews. the first study involved the analysis of consumer facebook comments based on 170 advertisements. this analysis extracted the relevant written information of the consumers with regard to digital advertisements. the second study involved eliciting specific information with regard to an unexplored area. in line with the same, we interviewed 15 experts. as a result of this in depth study, we were able to develop our framework at three levels. the first level consisted of the dimensions of functionality and well-being of consumers. the second level consisted of the dimensions of comprehension of the consumers’ desired end state. the final level had the dimensions of procedural flow, integrative capacity and praxeological extension. this framework can be used by academicians and practitioners to minimise the digital disengagement of the consumers.. consumer engagement depth study digital advertising disengagement integrative capacity","mapping country wine brand personalities, examples from five nations: an abstract. this paper presents a study of wine estate websites in five different countries and regions designed to explore which dimensions of brand personality wine estates exhibit online, to determine whether wine estates in different countries portray different dimensions of brand personality. the study uses text content from wine estate websites and analyzes it using the text analysis software diction. typical applications of the bps include comparisons of brand personalities within an offering category utilizing questionnaires in which respondents indicate the extent to which the brands being compared possess dimensions of brand personality, namely, sincerity, excitement, competence, sophistication, and ruggedness. we chose five wine tourist destination countries/regions for the study to, first, get a mix of old and new world wine producers (france [old], south africa [new/old], australia, new zealand, usa [new]) and in the case of three nations looked at the entire country (australia, new zealand, and south africa) and at the best-known regions in two countries (bordeaux for france, napa for usa). the text analysis software diction (www.dictionsoftware.com ) was used to analyze the data. diction is especially useful for determining the tone of a verbal message and allows the user to incorporate their own dictionaries into the analysis to determine scores based on frequency of key words and tone. we extracted complete text from the websites of the different wine producers and used the five brand personality dictionaries (competence, excitement, ruggedness, sincerity, and sophistication) from the pitt et al. (2007) dictionary source as the basis for computation for the analysis. our findings show that there is little distinction across estates and regions on dimensions of brand personality in the self-portrayals of the brands on estate websites. although there is some distinction, all speak most of excitement, followed by sincerity, then confidence and ruggedness, and least of sophistication in that order. these findings indicate that although wine estates and regions are unique in terms of the wine they produce and the geography from which the wine is produced, there is potentially room for estates to differentiate their brand on brand personality dimensions in their marketing. as well, the approach used in this research provides methodological insight into a way for those who manage wine tourism at the national, regional, and estate levels to gauge whether the personality of their brand is being communicated online as it is intended to be. the information gleaned from this type of research can be utilized both in brand personality decisions at a strategic level and in website design decisions at a tactical level.. na","online reviews and its impact on brand equity. the significant rise of online social media increases consumer's power over brands. therefore, this study attempts to assess the impact of online reviews on brand equity dimensions, namely, brand awareness, brand associations (in terms of perceived value, brand personality and organisational associations) and perceived quality. the present study integrates source credibility theory and attribution theory to understand the impact of online reviews on brands. the study follows mixed method approach by using quantitative analysis (structural equation modeling) and qualitative text analysis (netnography). for quantitative study, data has been collected from select facebook brand pages with the help of google form application. for qualitative study, \"apple users india\" brand community is considered to conduct netnography. quantitative analysis reveals that online reviews have more significant impact on brand awareness and perceived value. qualitative analysis also finds evidences of brand equity dimensions in the online brand community.. brand equity netnography online reviews structural equation modelling","popular research topics in marketing journals, 1995–2014. during the past two decades, the focus of marketing has moved from the tactics of persuasion to the strategies of value cocreation. after moving toward cognitive science and corporate strategies in the early 2000s, marketing research returned to its traditional domains of consumer psychologies and customer management. while conscientious consumers are gradually restraining themselves from selfish indulgence, marketers have refocused on a new set of values that encompass mental, experiential, and societal well-being. in this regard, we adopt an unprecedented approach by incorporating topic modeling with social network analysis. the results show that, in terms of topic heterogeneity, the most impactful journals are the most diverse, whereas each runner-up has a unique focus. among the journals, we detect two major co-authorship communities, and among the topics, we detect three. further, we find that the communities of the most cited papers are composed of heterogeneous clusters of similar topics. the pivots within, and the bridges between, these communities are also reported. in the spirit of collaborative research, our topic model and network analysis are shared via online collaboration and visualization platforms that readers can use to explore our models interactively and to download the dataset for further studies.. data visualization reproducible and collaborative research text mining topic model","sentiment classification of consumer-generated online reviews using topic modeling. the development of the internet and mobile devices enabled the emergence of travel and hospitality review sites, leading to a large number of customer opinion posts. while such comments may influence future demand of the targeted hotels, they can also be used by hotel managers to improve customer experience. in this article, sentiment classification of an eco-hotel is assessed through a text mining approach using several different sources of customer reviews. the latent dirichlet allocation modeling algorithm is applied to gather relevant topics that characterize a given hospitality issue by a sentiment. several findings were unveiled including that hotel food generates ordinary positive sentiments, while hospitality generates both ordinary and strong positive feelings. such results are valuable for hospitality management, validating the proposed approach.. customer reviews hospitality sentiment classification text mining topic modeling","harvesting brand information from social tags. social tags are user-defined keywords associated with online content that reflect consumers' perceptions of various objects, including products and brands. this research presents a new approach for harvesting rich, qualitative information on brands from user-generated social tags. the authors first compare their proposed approach with conventional techniques such as brand concept maps and text mining. they highlight the added value of their approach that results from the unconstrained, open-ended, and synoptic nature of consumer-generated content contained within social tags. the authors then apply existing text-mining and data-reduction methods to analyze disaggregate-level social tagging data for marketing research and demonstrate how marketers can utilize the information in social tags by extracting key representative topics, monitoring common dynamic trends, and understanding heterogeneous perceptions of a brand.. brand associative networks social tags text mining topic modeling user-generated content","identifying salient attributes of peer-to-peer accommodation experience. this study explores key content and themes from online reviews to explain major service attributes of peer-to-peer (p2p) accommodation sought by guests. the results from lexical analyses indicate that attributes frequently mentioned in guest reviews are associated with location (proximity to point of interest and characteristics of neighborhood), host (service and hospitality), and property (facilities and atmosphere). reviews focusing on location and feeling welcome are consistently linked with higher rating scores, including accuracy, cleanliness, check-in, communication, value, and overall ratings. this confirms that p2p accommodation appeals to consumers who are driven by experiential and social motivations. marketing implications are provided.. accommodation business analytics collaborative consumption consumer review sharing economy text mining","where does pride lead? corporate managerial hubris and strategic emphasis. a firm’s strategic emphasis on value creation versus appropriation, which is typically reflected in its resource allocation between r&d and advertising, is a central corporate decision that significantly influences financial performance. however, the drivers of such decisions remain underexplored. this study identifies a significant predictor of strategic emphasis, namely, corporate managerial hubris, and reveals some of its boundary conditions. leveraging a unique dataset based on text mining of press releases issued by over 400 firms across 13 years, the authors demonstrate that high corporate managerial hubris predicts low strategic emphasis on advertising relative to r&d. however, this effect is mitigated significantly by firm maturity, corporate governance, and industry-level strategic emphasis. the results provide novel insights into the effects of hubris on firm spending, the situations wherein marketing decisions tend to be subject to managers’ psychological bias, the means of preventing over- or under-investment in marketing strategy, and the recruitment and training of managers.. advertising corporate managerial hubris r&d strategic emphasis","unveiling what is written in the stars: analyzing explicit, implicit, and discourse patterns of sentiment in social media. deciphering consumers' sentiment expressions from big data (e.g., online reviews) has become a managerial priority to monitor product and service evaluations. however, sentiment analysis, the process of automatically distilling sentiment from text, provides little insight regarding the language granularities beyond the use of positive and negative words. drawing on speech act theory, this study provides a fine-grained analysis of the implicit and explicit language used by consumers to express sentiment in text. an empirical text-mining study using more than 45,000 consumer reviews demonstrates the differential impacts of activation levels (e.g., tentative language), implicit sentiment expressions (e.g., commissive language), and discourse patterns (e.g., incoherence) on overall consumer sentiment (i.e., star ratings). in two follow-up studies, we demonstrate that these speech act features also influence the readers' behavior and are generalizable to other social media contexts, such as twitter and facebook. we contribute to research on consumer sentiment analysis by offering a more nuanced understanding of consumer sentiments and their implications.. consumer sentiment online reviews sales ranks social media speech act theory text mining","a picture is worth a thousand words: translating product reviews into a product positioning map. product reviews are becoming ubiquitous on the web, representing a rich source of consumer information on a wide range of product categories (e.g., wines and hotels). importantly, a product review reflects not only the perception and preference for a product, but also the acuity, bias, and writing style of the reviewer. this reviewer aspect has been overlooked in past studies that have drawn inferences about brands from online product reviews. our framework combines ontology learning-based text mining and psychometric techniques to translate online product reviews into a product positioning map, while accounting for the idiosyncratic responses and writing styles of individual reviewers or a manageable number of reviewer groups (i.e., meta-reviewers). our empirical illustrations using wine and hotel reviews demonstrate that a product review reveals information about the reviewer (for the wine example with a small number of expert reviewers) or the meta-reviewer (for the hotel example with an enormous number of reviewers reduced to a manageable number of meta-reviewers), as well as about the product under review. from a managerial perspective, product managers can use our framework focusing on meta-reviewers (e.g., traveler types and hotel reservation websites in our hotel example) as a way to obtain insights into their consumer segmentation strategy.. consumer segmentation experience products online product reviews product positioning map psychometrics text mining","modeling the role of message content and influencers in social media rebroadcasting. we develop a model that examines the role of content, content-user fit, and influence on social media rebroadcasting behavior. while previous research has studied the role of content or the role of influence in the spread of social media content separately, none has simultaneously examined both in an effort to assess the relative effects of each. our modeling approach also accounts for a message's “fit” with users, based on the content of the message and the content of messages typically shared by users. as an empirical application, we examine how twitter posts originating from top business schools are subsequently rebroadcasted (or retweeted) by other users. we employ an individual-level split hazard model that accounts for variation in rebroadcasting decisions related to (1) content, (2) the content-user fit and (3) the influence of other users. we find that the rebroadcasting a message depends not only on message content but also on the message's fit with a user. our analysis also yields measures of influence and susceptibility to influence for each user, which can be used to identify influential social media users. we demonstrate how our approach can be used to evaluate different types of seeding strategies designed to increase the reach of social media messages.. bayesian estimation social influence social media text mining twitter","@dmos promote hotel occupancy in tourist destinations: an abstract. destination marketing organizations (dmos) are aware of twitter’s relevance, and they have now integrated twitter into their own websites (luna-nevarez and hyman 2012). because of their growing practical importance, social media have become strong allies for tourism destinations. social media are used by both tourists and providers (leung et al. 2013), so research is needed to show social media’s direct contribution to the tourism sector. to improve our knowledge of the effect of social media in tourism, this study examines how dmos’ use of twitter affects hotel occupancy in tourist destinations. our conceptual framework presents a model of the links between predictors of tourist occupancy (i.e., activity, content, followers, and actions of twitter users with respect to accounts managed by dmos) and the outcome (i.e., occupancy rates in tourist destinations). five spanish dmos with high levels of twitter activity before and during the 2015 holy week were selected for study. using twitter application programming interfaces, the numbers of followers, tweets, retweets, and replies tweeted by dmos and users were obtained. text mining was used to analyze the tweets by dmos, differentiating between tweets related to events, tourism resources/attractions, socialization, and commercial. data were analyzed using artificial neural networks (anns). the best fit was achieved through a multilayer perceptron where the content of dmo tweets, level of dmo activity, followers of dmos, and user actions acted as predictors of occupancy in different destinations. results show that the number of retweets and replies achieved by dmos had a greater effect than any other predictor of the hotel occupancy rate. furthermore, the type of content had a different influence on hotel occupancy in tourism destinations. tweets about events strongly influenced occupancy (importance of 74 %). these tweets were concise and informative—two characteristics highly valued by users (andré et al. 2012)—and hence conducive to generating interest in destinations. although tweets about tourist resources/attractions and socialization had some influence on occupancy, these tweets were not fundamental. finally, marketing tweets scarcely influenced occupancy in the individual models and the clustered model. results thereby provide evidence of the impact of social media, an area that requires greater attention from scholars (anderson 2012; leung et al. 2013; zeng and gerritsen 2014). managerial implications regarding optimizing dmos’ twitter strategies are discussed, and avenues for further research are highlighted. references available upon request. artificial neural network conceptual framework practical importance social medium user action","applying text analysis to determine factors that increase the assessed usefulness of online product reviews: an abstract. online consumer reviews (ocrs) are important in determining sales and price of products because they build trust in the seller (pavlou and dimoka 2006). trust is the driving force in e-commerce (gefen et al. 2003). one of the key ways in which trust is applied in ecommerce is through e-wom (electronic word of mouth), such as that created through ocr (dellarocas 2003). accordingly, interest in ocr has increased from both academics and practitioners as a valuable source of information for consumers making online buying decisions (e.g., archak et al. 2011). these decisions are influenced by the negative and positive information about a product or service posted online by other consumers (forman et al. 2008).. na","competitive analysis of social media data in the banking industry. recently, most companies interact more with their customers through the social media, particularly facebook and twitter. this has made large amount of textual data freely available on the internet for competitive intelligence analysis, which is helping reposition more and more companies for better profit. in order to carry out competitive intelligence, financial institutions need to take note of and analyse their competitor's social media sites. this paper, therefore, aims to help the banking industry in nigeria understand how to perform a social media competitive analysis and transform social media data into knowledge, which will form the foundation for decision-making and internet marketing of such institutions. the study describes an in-depth case study which applies text mining to analyse unstructured text content on facebook and twitter sites of the five largest and leading financial institutions (banks) in nigeria: zenith bank, first bank, united bank for africa, access bank and gtbank. analysing the social media content of these institutions will increase their competitive advantage and also lead to more profit for the banking institutions in question. the results obtained from this research showed that text mining is able to reveal uncommon and non-trivial trend for competitive advantage from social media data, and also provide specific recommendations to help banks maximise their competitive edge.. banking clustering competitive intelligence facebook sentiment analysis social media text mining twitter","idea generation, creativity, and prototypicality. we explore the use of big data tools to shed new light on the idea generation process, automatically “read” ideas to identify promising ones, and help people be more creative. the literature suggests that creativity results from the optimal balance between novelty and familiarity, which can be measured based on the combinations of words in an idea. we build semantic networks where nodes represent word stems in a particular idea generation topic, and edge weights capture the degree of novelty versus familiarity of word stem combinations (i.e., the weight of an edge that connects two word stems measures their scaled co-occurrence in the relevant language). each idea contains a set of word stems, which form a semantic subnetwork. the edge weight distribution in that subnetwork reflects how the idea balances novelty with familiarity. based on the “beauty in averageness” effect, we hypothesize that ideas with semantic subnetworks that have a more prototypical edge weight distribution are judged as more creative. we show this effect in eight studies involving over 4,000 ideas across multiple domains. practically, we demonstrate how our research can be used to automatically identify promising ideas and recommend words to users on the fly to help them improve their ideas.. creativity data mining idea generation innovation text mining","cross-cultural perceptions of onshore guided tours: a qualitative approach based on ewom. recognizing the importance of the study of guided tour experiences and being aware of the cross-cultural variations of services’ perceptions, the purpose of this article is to examine if cruise tourists from the two main generating markets (europe and north america) perceive differently a port of call guided tour based on the content of the ewom generated. the data set was comprised of 334 reviews on guided tours undertaken in spanish ports of call and published during the period 2009–2015 on the major travel web site tripadvisor. the thematic content analysis of the data was performed using the text mining software leximancer. the results yielded different pictures for the experiences described by both cultures, with europeans valuing the tour in terms of efficiency and north americans praising guide's performance and tailor-made tour services. based on these findings, practical implications are discussed.. na","self or simulacra of online reviews: an empirical perspective. online user-generated content includes various forms of computer-mediated online reviews, ratings, and feedback. the impact of online consumer reviews has been widely studied particularly in e-commerce, online marketing, and consumer behavior domains using text-mining and sentiment analysis. such studies often assume that consumer-submitted online reviews truly represent consumer experiences, but multiple studies on online social networks suggest otherwise. drawing from the social network literature, this paper investigates the impact of peers on consumer willingness to write and submit online reviews. an empirical study using data from “yelp,” a globally used online restaurant review web site, shows that the number of friends and fans positively impacts the number of online consumer reviews written. implications for research and practice are discussed.. na","sentence-based text analysis for customer reviews. firms collect an increasing amount of consumer feedback in the form of unstructured consumer reviews. these reviews contain text about consumer experiences with products and services that are different from surveys that query consumers for specific information. a challenge in analyzing unstructured consumer reviews is in making sense of the topics that are expressed in the words used to describe these experiences. we propose a new model for text analysis that makes use of the sentence structure contained in the reviews and show that it leads to improved inference and prediction of consumer ratings relative to existing models using data from www.expedia.com and www.we8there.com. sentence-based topics are found to be more distinguished and coherent than those identified from a word-based analysis.. bayesian analysis big data extended lda model text data unstructured data user-generated content","the role of emotions for the perceived usefulness in online customer reviews. online customer reviews often express emotions. this can enable marketers to analyze the textual content of online reviews with the aim to understand the role of emotions and how they can affect other customers. in this paper, we present an approach to extracting emotion content from online reviews in order to measure the importance of various emotion dimensions within different product categories. the approach uses an emotion lexicon to extract emotion terms, while it also builds a classification model to measure the importance of emotion dimensions based on the quality of reviews. review quality is measured based on the usefulness of online customer reviews, which are perceived and evaluated by other customers through their helpfulness ratings. this approach allows the identification of emotion dimensions that characterize qualitative reviews. the empirical evaluation in our study suggests that trust, joy, and anticipation are the most decisive emotion dimensions, although substantial variance across product categories can also be detected. additionally, we compared two contrasting emotion dictionaries. one lexicon was crowd-funded and contained a large vocabulary, whereas the other was more focused and smaller, since it was created word-wise by an expert. our empirical findings indicate that the crowd-funded solution outperforms its smaller counterpart in terms of classification precision. the main implication of this study is that it adds an emotional perspective to the broad set of existing tools that marketers employ to analyzing online reviews. our contributions are: i) we are the first to analyze emotions' role in online customer reviews; ii) we demonstrate how to develop a big data model such as this, without external assistance; iii) we show how to interpret the results of the created model; and iv) we show which dictionary to prefer when creating the model.. electronic word-of-mouth emotions helpfulness online customer reviews text mining","a structured analysis of unstructured big data by leveraging cloud computing. accurate forecasting of sales/consumption is particularly important for marketing because this information can be used to adjust marketing budget allocations and overall marketing strategies. recently, online social platforms have produced an unparalleled amount of data on consumer behavior. however, two challenges have limited the use of these data in obtaining meaningful business marketing insights. first, the data are typically in an unstructured format, such as texts, images, audio, and video. second, the sheer volume of the data makes standard analysis procedures computationally unworkable. in this study, we combine methods from cloud computing, machine learning, and text mining to illustrate how online platform content, such as twitter, can be effectively used for forecasting. we conduct our analysis on a significant volume of nearly two billion tweets and 400 billion wikipedia pages. our main findings emphasize that, by contrast to basic surface-level measures such as the volume of or sentiments in tweets, the information content of tweets and their timeliness significantly improve forecasting accuracy. our method endogenously summarizes the information in tweets. the advantage of our method is that the classification of the tweets is based on what is in the tweets rather than preconceived topics that may not be relevant. we also find that, by contrast to twitter, other online data (e.g., google trends, wikipedia views, imdb reviews, and huffington post news) are very weak predictors of tv show demand because users tweet about tv shows before, during, and after a tv show, whereas google searches, wikipedia views, imdb reviews, and news posts typically lag behind the show.. big data cloud computing google trends text mining twitter user generated content","advanced database marketing: innovative methodologies and applications for managing customer relationships. while the definition of database marketing hasn't changed, its meaning has become more vivid, versatile and exciting than ever before. advanced database marketing provides a state-of-the-art guide to the methods and applications that define this new era in database marketing, including advances in areas such as text mining, recommendation systems, internet marketing, and dynamic customer management. an impressive list of contributors including many of the thought-leaders in database marketing from across the world bring together chapters that combine the best academic research and business applications. the result is a definitive guide and reference for marketing and brand analysts, masters students, teachers and researchers in marketing analytics. the proliferation of marketing platforms and channels and the complexity of customer interactions create an urgent need for a multidisciplinary and analytical toolkit. advanced database marketing is a resource to enable marketers to achieve insights and increased financial performance; to provide them with the capability to implement and evaluate approaches to marketing that will meet, in equal measure, the changing needs of customers and the businesses that serve them.. na","decoding social media speak: developing a speech act theory research agenda. purpose – drawing on the theoretical domain of speech act theory (sat) and a discussion of its suitability for setting the agenda for social media research, this study aims to explore a range of research directions that are both relevant and conceptually robust, to stimulate the advancement of knowledge and understanding of online verbatim data. design/methodology/approach – examining previously published cross-disciplinary research, the study identifies how recent conceptual and empirical advances in sat may further guide the development of text analytics in a social media context. findings – decoding content and function word use in customers’ social media communication can enhance the efficiency of determining potential impacts of customer reviews, sentiment strength, the quality of contributions in social media, customers’ socialization perceptions in online communities and deceptive messages. originality/value – considering the variety of managerial demand, increasing and diverging social media formats, expanding archives, rapid development of software tools and fast-paced market changes, this study provides an urgently needed, theory-driven, coherent research agenda to guide the conceptual development of text analytics in a social media context.. social media communication speech act theory text mining","understanding satisfied and dissatisfied hotel customers: text mining of online hotel reviews. this article aims to examine the underpinnings of satisfied and unsatisfied hotel customers. a text-mining approach was followed and online reviews by satisfied and dissatisfied customers were compared. online reviews of 2,510 hotel guests were collected from tripadvisor.com for sarasota, florida. the research findings revealed some common categories that are used in both positive and negative reviews, including place of business (e.g., hotel, restaurant, and club), room, furnishing, members, and sports. study results further indicate that satisfied customers who are willing to recommend a hotel to others refer to intangible aspects of their hotel stay, such as staff members, more often than unsatisfied customers. on the other hand, dissatisfied customers mention more frequently the tangible aspects of the hotel stay, such as furnishing and finances. the study offers clear theoretical and managerial implications pertaining to understanding of satisfied and dissatisfied customers through the use of text mining and hotel ratings via review websites, social media, blogs, and other online platforms.. customer satisfaction dissatisfaction hotel reviews text mining user generated content","sponsorship research: drawing on the past to shape the future of sponsorship. this study examines how scholarly research on sponsorship has evolved between 1980 and july 2012. while various scholars (e.g., cornwell and maignan 1998; pope 1998; walliser 2003) have documented the progress of sponsorship research previously, this review departs from existing perspectives and redirects the conversation in the sponsorship literature by focusing attention on the semantic relationships among sponsorship concepts and the ways in which they have changed over time. using leximancer text-analysis software, we explore the emergence and growth of ideas and topics in sponsorship research by undertaking a systematic analysis of ideological trends. the research objectives of this study are: (1) to identify the scholarly trends through an empirical assessment of research across the history of sponsorship; and (2) based on the trends identified, to shape the future of sponsorship research. we collected titles and abstracts of articles in sponsorship presented in scholarly journals to july 2012. guided by previous authors, we focused specifically on sourcing abstracts since abstracts encapsulate a concise summary of an article’s core issues and are therefore lexically dense (cretchley et al. 2010). we sourced abstracts for our analyses from multiple databases (e.g., isi web of science, scopus, econpapers (repec), psycinfo and google scholar) and from over 150 marketing journals. we included abstracts from journals in languages other than english where english translations of the abstract were available electronically (walliser 2003). in total, we collected 804 articles published across 144 scholarly journals. next, we created subsets of the data in 10-year periods (1980-1989, 1990-1999, and 2000-2009), and one for 2010-2012. using leximancer software (version 4.0), we produced a set of concept maps and reports showing semantic structures across the history of sponsorship for each of the four periods (see smith and humphreys 2006). the concept map of the entire corpus of abstracts revealed a core set of concepts that are recurrent and quite evenly distributed throughout the history of sponsorship. sponsorship, marketing, objectives, value, strategy, companies, important, and key are concepts central to sponsorship’s intellectual identity. we identified two primal opposing but complementary forces around sponsorship measurement (e.g., consumers, awareness, brand, effects, image, impact, media, advertising) and sponsorship management (e.g., industry, benefits, rights, business, organizations, management, development, financial). based on examination of the prominent concepts within each period, we defined the 1980s as the intellectual era (television, important, business, benefits, and advertising); the 1990s as the ambushing era (objectives, ambush, support, major, and corporate); the 2000s as the consumer era (products, financial, media, role, and market); and as the 2010s as the relationships era (professional, team, relationship, social and value). in the decade 2010-2019, we expect to observe the development and empirical exploration of more managerially focused models of sponsorship that center on better understanding the development of sponsorship strategy and the extraction of value from sponsorship investment. going forward, the integration of social media as a sponsorship-marketing tool could also define a new era of research. in brief, adding a new data set to earlier reflections on sponsorship deepens our understanding of the role that key concepts and their inter-relationships have played in shaping sponsorship scholarship to date. we strongly believe the current study lends a high level of credibility to our insights about sponsorship’s past that can richly inform discussions about the future directions of sponsorship research.. multiple database scholarly journal scholarly research semantic relationship semantic structure","cultural and economic impacts on global cultural products: evidence from u.s. movies. existing international product diffusion studies have identified economic and cultural factors that influence consumers’ acceptance of new products, but they have not fully examined these factors’ roles in the international diffusion of global cultural products. the authors examine country-level economic and cultural factors that influence consumers’ acceptance of new global cultural products across countries. using 846 recent u.s. movies’ box office performances in 48 national markets as the empirical context, the authors obtain the following key novel findings on product sales: (1) an inverse u-shaped impact of economic development status, (2) a positive impact of the cultural compatibility of the product and the market, and (3) a u-shaped impact of intercountry cultural distance in the presence of cultural compatibility and a decreasing linear impact of cultural distance in the absence of cultural compatibility.. consumer culture global product movie text mining","semantic aspect of methodical training of faculty members (recursive model). components of methodical training of faculty members at higher education institutions, determining efficiency of using pedagogical technologies, are investigated in this paper. the author studies semantic aspect of methodical training of faculty members at higher educational institutions - the competency to organize student work on making sense of studied conceptions and texts. we substantiate from psychological and pedagogical perspective that semantic analysis is basic for faculty members to carry out different kinds of professional activity. based on methodological analysis, learning actions composing the essence of semantic analysis are revealed: establishing relations in studied material like juxtaposing scope and content of conceptions, reflecting the sense (conception) and the meaning (denotation) of names of conceptions. combination of those actions represents the essence of understanding process while working with educational text. examples from mathematics course, demonstrating the distinguishing features of semantic analysis, are given. the author offers the technology of training in shaping skills in making semantic text analysis in recursive model of methodical training, built in accordance with cyclic principle. every cycle of the technology includes methodologic, information and training stage of shaping skills in making semantic text analysis. recursive technology use in shaping methodical competency of faculty member will make possible to boost solving topical problems of russian higher education system.. educational text hermeneutic trend in education making sense in educational cognition semantics of educational text","traveller-generated contents for destination image formation: mainland china travellers to taiwan as a case study. this study examines destination image (di) formation through user-generated web contents. recently, web contents developed by peers have become one of the most popular information sources for travel planning purposes. after conducting a content analysis and text mining of a sample of 1033 blog articles (2009–2013), this study found that attraction to images of activities and positive/negative impressions with five categories (attraction, shopping, food and beverage, accommodation and transportation) form travellers’ di. the study results suggest that traveller-generated web contents can be especially useful for di analyses in marketing and management. this study also highlights the importance of understanding di formation from traveller-generated contents and the challenges for those in tourism marketing to narrow the gap.. content analysis destination image traveller-generated contents web 2.0","the journal of consumer research at 40: a historical analysis. this article reviews 40 years of the journal of consumer research (jcr). using text mining, we uncover the key phrases associated with consumer research. we use a topic modeling procedure to uncover 16 topics that have been featured in the journal since its inception and to show the trends in topics over time. for example, we highlight the decline in family decision-making research and the flourishing of social identity and influence research since the journal’s inception. a citation analysis shows which jcr articles have had the most impact and compares the topics in top-cited articles with all jcr journal articles. we show that methodological and consumer culture articles tend to be heavily cited. we conclude by investigating the scholars who have been the top contributors to the journal across the four decades of its existence. and to better understand which schools have contributed most to the knowledge of consumer research over this history, we provide an analysis of where these top-performing scholars were trained. our approach shows that the jcr archives can be an excellent source of data for scholars trying to understand the complicated, challenging, and dynamic field of consumer research.. citation analysis historical analysis, journal of consumer research topic modeling","a scientometric analysis of publications in the journal of business-to-business marketing 1993–2014. abstract: purpose: to conduct a scientometric analysis of the contents of the journal of business-to-business marketing from 1993 to 2014. methodology/approach: the authors use the leximancer computer-aided text analysis program, which reliably and reproducibly identifies the main concepts embedded in the text—their frequency and patterns of co-occurrence—based on the ways words move together in the text. they also identify key concepts that differentiate among the networks of concepts occurring in each of the first four five-year periods of the journal’s history. findings: there are four common underlying conceptual themes: relationships, market, study, and business. but the focal mix of concepts has changed over time, from a narrower initial focus on distribution and power and conflict, to a greater focus on firm business marketing strategy and pedagogy, to a focus on networks, the internet and more collaborative relations, to a focus, in the most recent period, on psycho-social network concepts, such as trust and commitment. research implications: the results complement and provide a baseline for evaluating and comparing researcher-conducted literature reviews of business marketing and jbbm’s contributions. the methodology provides possibilities for further analysis as it can be extended to analyze the business marketing and wider marketing literature more generally and how it has evolved. practical implications: leximancer provides a comprehensive, accessible summary of the content of the jbbm and demonstrates its value as a text analysis program. originality/value/contribution: the authors are among the first to use this novel computer text analysis program to conduct a scientometric analyses of journal content. it provides a template for conducting studies of other journals and text.. business marketing evolution of business concepts journal of business-to-business marketing leximancer relationships scientometric text analysis","from consumer panels to big data: an overview on marketing data development. this work explores marketing data development beginning with consumer panels and extending into today’s big data. its intent is to overview how big data builds on the former and how each possesses uniqueness. for example, a key strength of consumer panel data is its ability to link consumer characteristics with purchase behavior, while big data and its lack of structure has concerns with consumer characteristic revelation, yet can provide strong consumer insights. overall, the study overviews the potential, issues and future of big data in the context of marketing.. big data consumer panels predictive analytic purchase behavior text analysis","digging for gold with a simple tool: validating text mining in studying electronic word-of-mouth (ewom) communication. text-based electronic word-of-mouth (ewom) communication has increasingly become an important channel for consumers to exchange information about products and services. how to effectively utilize the enormous amount of text information poses a great challenge to marketing researchers and practitioners. this study takes an initial step to investigate the validities and usefulness of text mining, a promising approach in generating valuable information from ewom communication. bilateral data were collected from both ewom senders and readers via two web-based surveys. results provide initial evidence for the validity and utility of text mining and demonstrate that the linguistic indicators generated by text analysis are predictive of ewom communicators’ attitudes toward a product or service. text analysis indicators (e.g., negations and money) can explain additional variance in ewom communicators’ attitudes above and beyond the star ratings and may become a promising supplement to the widely used star ratings as indicators of ewom valence.. attitude electronic word-of-mouth linguistic indicator text mining","ambush marketing of the london olympics: a content analysis. global sporting events such as the olympic games attract significant amounts of sponsorship spend by companies that want to position their brands with such a high profile event. there are very few events that can achieve this on a truly global scale, but the olympic games are one of these events. however, it is also the case that these types of events attract ambush marketing as rival companies seek to have their brands associated with the olympics, but at only a fraction of the cost. ambush marketing has been termed associative marketing as the ambush marketer attempts to associate the brand with and event and trade off the goodwill associated with the event. thus ambush marketing attempts to confuse the consumer into believing that the ambusher is a sponsor. ambush marketing strategies include sponsoring media coverage of an event, sponsoring a sub-category of the event, consumer promotions, congratulatory messages, pourage agreements and corporate hospitality. however, burton and chadwick (2009) say ambush marketing has evolved, from mainly broadcast sponsorship campaigns and advertising at the events, to more creative and associative marketing efforts away from the event, to the use of spectators or fans to promote the brand. although ambush marketing of such an event is not necessarily new, the 2012 london olympics presented an intriguing research scenario because firstly it had the strictest anti-ambush legislation and enforcement policies of any other previous games. in an attempt to protect the extensive sponsorship spend (in excess of £3.7 billion) required to host the london olympic games, a total of 24 statutory instruments and five acts of parliament were brought into being including bans on the use of numerous words associated with the olympics, including ‘games’, ‘2012’ and combinations of ‘gold’, ‘summer’, ‘medals’, ‘safe zones’ where sponsors have sole rights and aggressive onsite policing. ambush marketing is no longer just a civil matter but a criminal one. such strict control measures had the potential to both alienate the public as well as lull the sponsors into a false sense of security. the second aspect making the london olympics an interesting research event was the strong social media presence. these games were even described as the ‘digital games’. this paper looks at how effective this legislation was in practice given the strong social media presence, by undertaking a content analysis on text extracted from google using the key word search term ‘london olympics ambush marketing’. a search was done every day for a month before, and the time during the olympics. for each period all the articles were then combined into a single word document. a total of 406 354 words (1108 pages) were included in the data set for the ‘before’ period and 413 433 words (1008 pages) in the ‘during’ set. leximancer, text mining software used to analyse (thematically and semantically) the content of textual documents and produce conceptual maps, was used for the data analysis. the leximancer maps revealed that in the ‘month before’ analysis the main themes exhibit a strong focus on the highly regulated nature of the london olympics and the extreme lengths that the ioc and locog’s have gone to protect the official sponsors. the main themes during the olympics while still emphasising the measures in place to protect the official sponsors, revealed an emergence of more discussion on the ambushers, with nike and dr dre featuring prominently. both these ambushers appear to have tiptoed around the ioc and locog legislation and rules. the role of social media is highlighted. comparing the data from the two periods, the research findings suggest that the legislation did curtail ambush marketing to some extent, although did not eliminate it. despite concerns about a backlash from spectators and the possibility of negative publicity due to the strict controls, this did not appear to materialise. furthermore, the nature of ambush marketing seems to have changed from a direct assault on the event, to more subtle ambushing techniques, also more difficult to predict. the hype surrounding the london games as the ‘first truly’ digital games, with the anticipated mass use of social media as an ambush medium of the games did not materialise. this may have been due to the cooperation of the leading social media sites with facebook banning advertising associated with the games during the olympics. similarly, twitter announced a ban any potential ambush tweets. the concept maps did pick up the theme of media for both before and during the games with the central concept in both periods relating to social media. it is difficult to tell how effective the games were for the official sponsors in terms of raising the profile of their brands. only four brands were picked up by the concept maps, two official sponsors (adidas and coca cola) and two unofficial sponsors (nike and beats). it would seem that sponsoring the games does not get everyone talking about your brand. limitations in this research are acknowledged.. digital game enforcement policy extreme length olympic game social medium","gender and publication activity in top marketing journals. this article studies the relationship between the gender of marketing scholars and their publication activity in marketing science, journal of consumer research, journal of marketing research, and journal of marketing within a 5 year time-frame (2009-2013). percentages of female authors publishing either a single or multiple articles or as first-authors on multiple-authored articles or as authors in any position in multiple-authored articles were mostly less than 35%. these numbers were generally the highest, but mostly under 50%, in the journal of consumer research and were the lowest in marketing science. text mining of abstracts suggested that female authors were more likely to work on topics that fit better with the scope of the journal of consumer research.. na","conceptual framework of mass media manipulative models forming. nowadays mass media operate, transform and limit information thus becoming the main mechanism of influence in the modern society. state-ofthe- art information technologies are used in order to increase the efficiency of government strategies. they help to turn the audience into an object of manipulation. many theories studying mass media are focused on the influence the media has on its viewer or reader. development of satellite television and some other media types that have almost unlimited coverage has increased the opportunities of cultural and information expansion. mass media is extremely necessary during social development, because without its activity it is impossible to evaluate political consciousness, value system and purposes of all levels of the population and to gain public support of social changes. nowadays there are two basic theoretical models of studying mass media political influence. the first one studies the influence of mass communications on individual behavior and social life in general. the second one has been developing for the last 30 years under the influence of cultural anthropology, structural theories, semiotics and other crossdisciplinary scientific fields. it was focused on perceiving the connections between “texts,” individuals and big social groups. in this article we study “influence studying” model and “text analysis” model.. manipulation mass media media theories mediacracy","community crosstalk: an exploratory analysis of destination and festival ewom on twitter. research suggests that festivals can promote a destination via online word-of-mouth (ewom) on socialmedia, even though the nature of this effect is not yet fully understood. using a combination of social network analysis and text analysis (qualitative and quantitative), this article examines ewom at a tourism destination (bournemouth) when a festival (bournemouth air show 2013) is staged. the communities of interest of ewom interactions on twitter were captured and analysed to understand the structure and content of ewom. findings indicate that key users are usually already prominent individuals and that festivals act as both a direct generator as well as an online animator of ewom. finally, network size, span and scope may be useful indicators when comparing ewom networks.. community of interest ewom festivals social network analysis twitter","message reframing in advertising. various communication techniques such as humor or two-sided argumentation are frequently used in advertising. one technique that is common but has not been conceptualized is the deliberate change of perspective of specific product or brand attributes. based on the reframing method used in neuro-linguistic programming (nlp) this technique is conceptualized as message reframing, defined as a communication technique that changes the conceptual and/or emotional setting or viewpoint in relation to how a brand is experienced by placing it in a different frame that fits the same brand equally well or better. the effects of message reframing in contrast to conventional advertisements as well as in contrast to humor and two-sided argumentation are hypothesized and tested in an experimental study. the results reveal that message reframing outplays traditional advertising techniques regarding attention toward the ad, perceived novelty, attitude toward the ad, and attitude toward the brand. moreover, message reframing does not show the deterioration of source credibility commonly observed in humorous messages, and leads to higher attention, perceived novelty, and attitude toward the ad than two-sided messages.. na","fake or real? the computational detection of online deceptive text. online repositories are providing business opportunities to gain feedback and opinions on products and services in the form of digital deposits. such deposits are, in turn, capable of influencing the readers’ views and behaviours from the posting of misinformation intended to deceive or manipulate. establishing the veracity of these digital deposits could thus bring key benefits to both online businesses and internet users. although machine learning techniques are well established for classifying text in terms of their content, techniques to categorise them in terms of their veracity remain a challenge for the domain of feature set extraction and analysis. to date, text categorisation techniques for veracity have reported a wide and inconsistent range of accuracies between 57 and 90 per cent. this article evaluates the accuracy of detecting online deceptive text using a logistic regression classifier based on part of speech tags extracted from a corpus of known truthful and deceptive statements. an accuracy of 72 per cent is achieved by reducing 42 extracted part of speech tags to a feature vector of six using principle component analysis. the results compare favourably to other studies. improvements are anticipated by training machine learning algorithms on more complex feature vectors by combining the key features identified in this study with others from disparate feature domains.. applied artificial intelligence business analytics computational linguistics online fake reviews open source data text mining","a topical history of jmr. using subject indexes and text mining of author abstracts, the authors track the evolution of content in journal of marketing research since its inception 50 years ago. these data reveal that the journal has expanded beyond its initial emphasis on marketing research methods and advertising to increase its coverage of other substantive topics and consumer behavior. moreover, a joint space of topics and editors reveals that editorial orientations appear largely evolutionary rather than revolutionary and that a major shift in journal coverage occurs at the time marketing science began publication. the authors conclude their analysis with several policy recommendations.. na","identifying customer satisfaction estimators using review mining. in recent years, various methods have been developed which enable organisations to collect information on customer sentiments, perceptions, and demands. however, these methods do not provide practical guidance for utilising this information to offer superior products and services to their customers. given this oversight, the current study proposes a new method for identifying the strengths and weaknesses of products or services by using language-processing software on product reviews. we use an online review site, skytrax, to collect user reviews related to economy class flights for four airlines. we then analyse the language inherent in these reviews to identify the strengths and weaknesses of each airline. the results of the analyses may assist in reconciling discrepancies between customer expectations and their perceptions of products or services.. airline industry customer assessment customer needs customer satisfaction marketing multiple regression analysis natural language processing quantitative analysis strategic planning word-of-mouth communication","exploring brand associations: an innovative methodological approach. purpose: the purpose of this exploratory study is to propose a new methodological approach to investigate brand associations. more specifically, the study aims to show how brand associations can be identified and analysed in an online community of international consumers of fashion to determine the degree of matching with company-defined brand associations. design/methodology/approach: the methodology is two-pronged, integrating qualitative market research techniques with quantitative text mining. it was applied to determine types and perceptions of brand associations among fashion bloggers with reference to three leading italian fashion houses. these were then compared to brand associations found in company-generated texts to measure the degree of matching. findings: the results showed consistent brand associations across the three brands, as well as substantial matching with company-defined brand associations. in addition, the analysis revealed the presence of distinctive brand association themes that shed further light on how brand attributes were perceived by blog participants. practical implications: the methods described can be used by managers to identify and reinforce favourable brand associations among consumers. this knowledge can then be applied towards developing and implementing effective brand strategies. originality/value: the authors propose an interdisciplinary approach to investigate brand associations in online communities. it incorporates text mining and computer-assisted textual analysis as techniques borrowed from the field of linguistics which have thus far seen little application in marketing studies, but can nonetheless provide important insights for strategic brand management. © emerald group publishing limited.. brand associations fashion industry online communities qualitative-quantitative approach text mining textual analysis","the impact of text product reviews on sales. purpose - the aim of this research is to theorize and demonstrate that analyzing consumers’ text product reviews using text mining can enhance the explanatory power of a product sales model, particularly for hedonic products, which tend to generate emotional and subjective product evaluations. previous research in this area has been more focused on utilitarian products. design/methodology/approach - our text clustering-based procedure segments text reviews into multiple clusters in association with consumers’ numeric ratings to address consumer heterogeneity in taste preferences and quality valuations and the j-distribution of numeric product ratings. this approach is novel in terms of combining text clustering with numeric product ratings to address consumers’ subjective product evaluations. findings - using the movie industry as our empirical application, we find that our approach of making use of product text reviews can improve the explanatory power and predictive validity of the box-office sales model. research limitations/implications - marketing scholars have actively investigated the impact of consumers’ online product reviews on product sales, primarily focusing on consumers’ numeric product ratings. recently, studies have also examined user-generated content. similarly, this study looks into users’ textual product reviews to explain product sales. it remains to be seen how generalizable our empirical results are beyond our movie application. practical implications - whereas numeric ratings can indicate how much viewers liked products, consumers’ reviews can convey why viewers liked or disliked them. therefore, our review analysis can help marketers understand what factors make new products succeed or fail. originality/value - primarily our approach is suitable to products subjectively evaluated, mostly, hedonic products. in doing so, we consider consumer heterogeneity contained in reviews through our review clusters based on their divergent impacts on sales.. hedonic products j-distribution online product reviews product sales text clustering","technology acceptance modeling of augmented reality at the point of sale: can surveys be replaced by an analysis of online reviews?. online reviews by users have become an increasingly important source of information. this is true not only for new users of goods or services, but also for their producers. they extend the insight into the acceptance of new goods and services, e.g. at the point of sale, from a mere sales and usage quantity oriented point of view to a cause and effect oriented one. since online reviews by consumers of many goods and services are nowadays widespread and easily available on the internet, the question arises whether their analysis can replace the more traditional approaches to measure technology acceptance, e.g., using questionnaires with tam (technology acceptance model) items. this paper tries to answer this question using ikea[u+05f3]s mobile catalogue app as an example. for comparisons reasons, data on the acceptance of the current version of this catalogue is collected in four different ways, (1) as answers to batteries of tam items, (2) as assignments to pre-defined adjective pairs, (3) as textual likes and dislikes of users (simulating online reviews), and (4) as publicly available (real) reviews by users. the source for (1)-(3) is a survey with a sample of respondents, the source for (4) an online forum. the data is analyzed using partial least squares (pls) for tam modeling and text mining for pre-processing the textual data. the results are promising: it seems that data collection via surveys can be replaced - with some reservations - by the analysis of publicly available (real) online reviews. © 2014 elsevier ltd.. augmented reality online reviews partial least squares (pls) technology acceptance technology acceptance model (tam) text mining","pass it on: a framework for classifying the content of word of mouth. while word of mouth (wom) is regarded as a key ingredient in product success, little is known about the content of wom given. using an online text-mining approach, we investigated the key themes within electronic wom (ewom) in two entertainment categories, television programs and movies. we identify seven key themes within ewom conversations. these are (in order of prevalence): product/. service characteristic (26% of comments); personal opinion (17%); distribution characteristic (15%); categorisation comparison (14%); description of purchase/. consumption activity (10%); advertising/. promotion (9%) and in-market performance (4%). there are differences across categories, largely explicable by differences in the purchase/consumption process of movies versus television programs. however, there are only minor differences for new versus returning products. our findings highlight the value of advertising during the initial stages of a product's launch to enable consumers to form opinions early on. furthermore, marketing activities should provide information about how people can purchase/consume the product/service, as such details are often sought and passed on via ewom. given that ewom often entails a comparison of a given product against another, providing signals on appropriate comparative offerings may help marketers control the context for consumers' understanding of the new product. © 2014 australian and new zealand marketing academy.. entertainment ewom films movies television programs word of mouth","analyzing hotel customers' e-complaints from an internet complaint forum. recent web technology advancement has enabled online customers to express their concerns and negative experiences about the hotel services over the internet. this exploratory study attempts to provide some insights into this recent phenomenon by analyzing self-selected hotel customers' online complaints recorded on a specialized complaint forum, ecomplaints.com. a content analysis of those e-complaints in 18 problem categories revealed that online customers were mostly unhappy with fundamental service failures (i.e., declined service quality and rude employees) in the hotel service encounter. surprisingly, only one in every five e-complaints received a response from the hotel company in the identified top-five complaint categories. the authors further used text-mining software to identify most frequently complained keywords and examine their relational associations. business implications of the research findings were also discussed.. company response complaint handling e-complaints (online complaints) hotel service quality service recovery","relationships, roles, and consumer identity in services marketing. consumers form complex relationships with service entities. while relationship marketing theory is one means of exploring these relationships, other theoretical areas can also yield useful insights. an examination of customers' perceptions of one retail service provider across a 15-year span reveals the multifaceted yet consistent roles the provider plays as these consumers make use of the site to instantiate and express their identities. © 2013 copyright taylor and francis group, llc.. consumer identity relationship marketing role theory text analysis","how changes in word frequencies reveal changes in the focus of the jdddmp. accounts of changes over time in the focus of direct marketing need not necessarily be subjective. analysis of the frequency with which words appear in the journal of direct, data and digital marketing practice makes it possible to identify marketing concepts that have gained or lost traction during the past 12 years. many indicative keywords can be consolidated into keyword groups, sets of associated terms, and thereby used to identify changes over time in the thematic focus of contributions to the journal. numerical changes in the frequency of use of words within these keyword groups make it possible to quantify changes over time in the 'footprint' of different thematic areas of interest to marketers. from such analysis, there is strong evidence not just of the growth of coverage of topics relating to social media but also the decline in the coverage of crm and, perhaps more surprisingly, of the business and organizational context within which the practice of direct marketing has historically been undertaken.. data and digital marketing practice direct marketing footprint journal of direct keywords text mining word frequency","mcdonald's apology over a pig toy: a cultural territorial clash. mcdonald's introduced a modified chinese zodiac promotion to celebrate chinese new year in singapore in 2010. instead of the traditional 12 zodiac animals - rat, ox, tiger, rabbit, dragon, snake, horse, sheep, monkey, rooster, dog, and pig - mcdonald's replaced the pig symbol with a cupid toy because valentine's day fell on the same day as chinese new year in 2010 and because the restaurant's muslim customers do not consume pork. this paper aimed to find out how and why customers of mcdonald's rejected the zodiac promotion in singapore through heated discussions in the media. following a review of literature on multiculturalism, cultural sensitivity and hybrid cultural theories, a foucault-based framework of discipline-ethics-performativity guided this qualitative text analysis of 97 documents from forum pages of the straits times newspaper and online postings on asia one and channel news asia. mass media asserted that it was a racial issue related to extreme political correctness that fuelled the controversy but the emerging theory was that there was a cultural territorial clash of discipline structures, ethical moderation, and identity performance. this paper contributes to literature on business in asia, as there seems to be little research on pig symbolism in marketing or on the failure of culturally oriented marketing activities. the implication for practice is that the marketing team needs to consider more carefully the fundamental cultural disciplinary structure, ethics responsibility, and identity performativity in a multi-ethnic country. while mcdonald's zodiac promotion appeared not to have upset the muslims in multi-racial singapore, it would be interesting to research whether there was any online backlash in malaysia and indonesia, which have a higher muslim population. © 2013 australian and new zealand marketing academy.. chinese and muslim culture cultural sensitivity pig symbolism theory of cultural territorial clash zodiac promotion","stock market reactions to customer and competitor orientations: the case of initial public offerings. recognizing that initial public offerings (ipos) represent the debut of private firms on the public stage, this study investigates how pre-ipo customer and competitor orientations (ccos) affect ipo outcomes. building on information economics, we propose that ccos influence investors' sentiments toward an ipo and that both ipo-specific variables (which influence the credibility of cco information) and facets of the organizational institutional and task environments (which influence the appropriateness of cco information) moderate this influence. we test the framework using data collected from computer-aided text analysis, expert coders, and secondary sources for 543 ipos across 43 industries between 2000 and 2004. a bayesian shrinkage model, which accounts for industry-specific effects and uses latent instrumental variables to address cco endogeneity, shows that ccos positively influence ipo outcomes. furthermore, (1) underwriter reputation and venture funding positively moderate the effects of ccos; (2) technological and market turbulence positively and institutional complexity negatively moderate the effect of customer orientation; and (3) technological turbulence, competitive intensity, and institutional complexity positively moderate the effect of competitor orientation. also, accounting for endogeneity using latent instrumental variables substantially improves the predictive validity of the model, relative to alternative model specifications. © 2013 informs.. cheap talk competitor orientation content analysis costly state falsification customer orientation hierarchical bayesian analysis information economics initial public offering institutional environment latent instrumental variables","more than words: the influence of affective content and linguistic style matches in online reviews on conversion rates. customers increasingly rely on other consumers' reviews to make purchase decisions online. new insights into the customer review phenomenon can be derived from studying the semantic content and style properties of verbatim customer reviews to examine their influence on online retail sites' conversion rates. the authors employ text mining to extract changes in affective content and linguistic style properties of customer book reviews on amazon.com. a dynamic panel data model reveals that the influence of positive affective content on conversion rates is asymmetrical, such that greater increases in positive affective content in customer reviews have a smaller effect on subsequent increases in conversion rate. no such tapering-off effect occurs for changes in negative affective content in reviews. furthermore, positive changes in affective cues and increasing congruence with the product interest group's typical linguistic style directly and conjointly increase conversion rates. these findings suggest that managers should identify and promote the most influential reviews in a given product category, provide instructions to stimulate reviewers to write powerful reviews, and adapt the style of their own editorial reviews to the relevant product category. © 2013, american marketing association.. affective content conversion rate internet marketing linguistic style match online customer reviews","creating value with sales promotion strategies that avoid price discounting. this paper introduces the development of a new type of sales promotion strategy tocreate more value for goods and to avoid price discounting. i use a psychological approach designed by creating consumer insight hypotheses based on in-depth interviews, which are then verified by webmotivation research and text-mining. this innovative sales promotion approach is a very hot topic as a new type of promotion development among large companies in japan and is useful in avoiding pricediscounting sales. this paper explains the concrete process used in this type of promotion and reveals the successful case of a large spice company in japan. the process uses price sensitivity measurement (psm) as a pricing technique. in the experiment, conducted in nine retail stores, the most successful sales promotion condition saw an increase of 900% in monetary sales without price discounting during the two weeks of the experiment, and 500% in the two weeks after that. © 2012 by emerald group publishing limited.. na","marketing activity, blogging and sales. the recent growth of consumer-generated media (cgm), also known as \"new\" media, has changed the interaction between consumers and firms from being unidirectional to being bidirectional. however, cgm are almost always accompanied by traditional media (such as tv advertising). this research addresses the critical question of whether new and traditional media reinforce or damage one another's effectiveness. this question is important because traditional media, in which a manufacturer creates and delivers content to consumers, consume a firm's resources. in contrast to these paid media, new media (in which consumers create content and this content is exchanged between other consumers and potentially between manufacturers) are primarily available for free. this question becomes even more salient when new product launches are involved, as firms typically allocate approximately half of their marketing budgets to support new products.one of the most prevalent forms of new media is blogging. therefore, we assemble a unique data set from japan that contains market outcomes (sales) for new products, new media (blogs) and traditional media (tv advertising) in the movie category. we specify a simultaneous equation log-linear system for market outcomes and the volume of blogs. our results suggest that new and traditional media act synergistically, that pre-launch tv advertising spurs blogging activity but becomes less effective during the post-launch period and that market outcomes have an effect on blogging quantity. we find detailed support for some of these results via a unique and novel text-mining analysis and replicate our findings for a second product category, cellular phone service. we also discuss the managerial implications of our findings. © 2012 elsevier b.v.. blogs cellular phone service consumer generated media japan movies response models social media text mining tv advertising","mine your own business: market-structure surveillance through text mining. web 2.0 provides gathering places for internet users in blogs, forums, and chat rooms. these gathering places leave footprints in the form of colossal amounts of data regarding consumers' thoughts, beliefs, experiences, and even interactions. in this paper, we propose an approach for firms to explore online user-generated content and \"listen\" to what customers write about their and their competitors' products. our objective is to convert the user-generated content to market structures and competitive landscape insights. the difficulty in obtaining such market-structure insights from online user-generated content is that consumers' postings are often not easy to syndicate. to address these issues, we employ a text-mining approach and combine it with semantic network analysis tools. we demonstrate this approach using two cases-sedan cars and diabetes drugs-generating market-structure perceptual maps and meaningful insights without interviewing a single consumer. we compare a market structure based on user-generated content data with a market structure derived from more traditional sales and survey-based data to establish validity and highlight meaningful differences. © 2012 informs.. market structure marketing research text mining user-generated content","designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. user-generated content on social media platforms and product search engines is changing the way consumers shop for goods online. however, current product search engines fail to effectively leverage information created across diverse social media platforms. moreover, current ranking algorithms in these product search engines tend to induce consumers to focus on one single product characteristic dimension (e.g., price, star rating). this approach largely ignores consumers' multidimensional preferences for products. in this paper, we propose to generate a ranking system that recommends products that provide, on average, the best value for the consumer's money. the key idea is that products that provide a higher surplus should be ranked higher on the screen in response to consumer queries. we use a unique data set of u.s. hotel reservations made over a three-month period through travelocity, which we supplement with data from various social media sources using techniques from text mining, image classification, social geotagging, human annotations, and geomapping. we propose a random coefficient hybrid structural model, taking into consideration the two sources of consumer heterogeneity the different travel occasions and different hotel characteristics introduce. based on the estimates from the model, we infer the economic impact of various location and service characteristics of hotels. we then propose a new hotel ranking system based on the average utility gain a consumer receives from staying in a particular hotel. by doing so, we can provide customers with the \"best-value\" hotels early on. our user studies, using ranking comparisons from several thousand users, validate the superiority of our ranking system relative to existing systems on several travel search engines. on a broader note, this paper illustrates how social media can be mined and incorporated into a demand estimation model in order to generate a new ranking system in product search engines. we thus highlight the tight linkages between user behavior on social media and search engines. our interdisciplinary approach provides several insights for using machine learning techniques in economics and marketing research. © 2012 informs.. crowdsourcing hotels ranking system search engines social media structural models text mining user-generated content","mining messages: exploring consumer response to consumer- vs. firm-generated ads. social media provide consumers with a platform for interactivity, and interactivity leads to consumer empowerment by providing the consumer with a platform to make their voice heard. this paper contributes to the marketing literature exploring the voice of the consumer in consumer-generated advertisements (cgas). the objective of this research is to find ways to measure consumer response to cgas. we measure whether they differ from firm-generated ads in the responses they elicit and also observe whether they generate different responses by ad type, or if some categories have similar responses. we review cgas for apple's macbook air lightweight laptop and run a text mining application to understand the common themes and conduct text analysis on the responses to both cgas and firm-generated ads to answer the question \"is the source of the advertisement important?\" text analytics also enable us to measure consumers' attitude toward products, companies, and ads. we then work toward understanding why and under what circumstances cgas are effective and how companies may handle or foster different types of cgas. © 2011 john wiley & sons, ltd.. na","data analysis across various media: data fusion, direct marketing, clickstream data and social media. as the volume and diversity of digitally stored data keeps growing, the importance for the business to create a holistic picture of the customer continues to grow. in this paper, we discuss the possibilities available to researchers with four different streams of data. first, we discuss data fusion, merging of proprietary databases with market research data, which enables appending attitudinal data to customer behaviour. secondly, best practices in direct marketing leverage analysis of customer response patterns to provide the right offer to the right customer at the right moment. thirdly, analysing internet usage data allows many opportunities for small-scale, agile marketing pilots. and finally, social media are discussed, one of the newer opportunities to collect data about customer sentiments that relies heavily on text mining analytics. © 2011 macmillan publishers ltd.. data analytics data fusion direct marketing internet usage social media text mining","text analytics: where are we headed and where are we now?. several text analytics tools are being used in marketing research, but few have been developed for marketing researchers by marketing researchers. this creates a tremendous growth opportunity for survey software firms that deal with open-ended comments. text analytics seems better suited to larger amounts of data, however, and still is not a very useful tool for qualitative researchers. going forward, we expect to see fewer text analytics \"pure play\" firms and more firms specializing in use case or industry case.. na","service failures and recovery actions in the hotel industry: a text-mining approach. the purpose of this article is to cluster service failures and recovery actions in the hotel industry. keywords were extracted from the descriptive responses of hotel guests and systematically clustered to identify major areas of service failures and recovery actions in the hotel industry. two sets of textual data (service failure and service recovery) were collected from 75 hotel guests using the critical incident technique and content-analyzed with a text-mining program. text-mining analysis identified 50 keywords in eight clusters from the service failure data and 50 keywords in seven clusters from the service recovery data. the identified keywords were conceptually graphed to map meaningful findings that are logically precise and computationally tractable. the major theoretical and practical implications are also discussed in this study. © the author(s) 2011.. critical incident technique service failure service recovery text-mining approach","efficient methods for sampling responses from large-scale qualitative data. the world wide web contains a vast corpus of consumer-generated content that holds invaluable insights for improving the product and service offerings of firms. yet the typical method for extracting diagnosticinfor mation from online content-text mining-has limitations. as a starting point, we propose analyzing a sample of comments before initiating text mining. using a combination of real data and simulations, we demonstrate that a sampling procedure that selects respondents whose comments contain a large amount of information is superior to the two most popular sampling methods-simple random sampling and stratified random sampling--in gaining insights from the data. in addition, we derive a method that determines the probability of observing diagnosticinfor mation repeated a specificnumber of times in the population, which will enable managers to base sample size decisions on the trade-off between obtaining additional diagnostic information and the added expense of a larger sample. we provide an illustration of one of the methods using a real data set from a website containing qualitative comments about staying at a hotel and demonstrate how sampling qualitative comments can be a useful first step in text mining. ©2011 informs.. consumer-generated content consumer-generated media customer feedback on the web large-scale qualitative data sets qualitative comments sampling open-ended questions text mining","automated marketing research using online customer reviews. market structure analysis is a basic pillar of marketing research. classic challenges in marketing such as pricing, campaign management, brand positioning, and new product development are rooted in an analysis of product substitutes and complements inferred from market structure. in this article, the authors present a method to support the analysis and visualization of market structure by automatically eliciting product attributes and brand's relative positions from online customer reviews. first, the method uncovers attributes and attribute dimensions using the \"voice of the consumer,\" as reflected in customer reviews, rather than that of manufacturers. second, the approach runs automatically. third, the process supports rather than supplants managerial judgment by reinforcing or augmenting attributes and dimensions found through traditional surveys and focus groups. the authors test the approach on six years of customer reviews for digital cameras during a period of rapid market evolution. they analyze and visualize results in several ways, including comparisons with expert buying guides, a laboratory survey, and correspondence analysis of automatically discovered product attributes. the authors evaluate managerial insights drawn from the analysis with respect to proprietary market research reports from the same period analyzing digital imaging products. © 2011, american marketing association.. market structure analysis online customer reviews text mining","current themes in social marketing research: text-mining the past five years. social marketing has advanced rapidly from its beginnings almost 40 years ago. this commentary takes a look at the current themes in published social marketing research by using text-mining to analyze articles published in the past 5 years. it also discusses the areas for future research, especially the need for published social marketing research to expand from mostly health-related application to other areas.. na","estimating aggregate consumer preferences from online product reviews. today, consumer reviews are available on the internet for a large number of product categories. the pros and cons expressed in this way uncover individually perceived strengths and weaknesses of the respective products, whereas the usually assigned product ratings represent their overall valuation. the key question at this point is how to turn the available plentitude of individual consumer opinions into aggregate consumer preferences, which can be used, for example, in product development or improvement processes. to solve this problem, an econometric framework is presented that can be applied to the mentioned type of data after having prepared it using natural language processing techniques. the suggested methodology enables the estimation of parameters, which allow inferences on the relative effect of product attributes and brand names on the overall evaluation of the products. specifically, we discuss options for taking opinion heterogeneity into account. both the practicability and the benefits of the suggested approach are demonstrated using product review data from the mobile phone market. this paper demonstrates that the review-based results compare very favorably with consumer preferences obtained through conjoint analysis techniques. © 2010 elsevier b.v.. consumer behavior electronic commerce and internet marketing market analysis and response preference analysis product management user generated content","an introduction to data mining and other techniques for advanced analytics. this paper reviews the use of data mining (dm) for extracting patterns from large databases, held by companies such as banks, retailers and telco operators. the dm process is discussed, together with the ideal architecture, for applying this approach in a data warehouse environment. some related techniques are identified advanced data visualization tools for converting large volumes of data into pictorial form together with text mining and social network analysis for extracting structured data from unstructured text and relationships. the role of contact optimization is highlighted, as a method for optimizing the business value that a company can achieve from its dm activities. finally, the paper suggests some initial steps in selecting a dm software product and offers the author's personal guidelines for the types of product that are likely to be most useful in different situations. © 2010 macmillan publishers ltd.. advanced analytics contact optimization data mining data visualization social network analysis text mining","representations of the emotions associated with a wine purchasing or consumption experience. wine products possess pronounced cultural and symbolic characteristics. given how hard it is to differentiate the product’s more objective dimensions, it is important to understand the emotions and feelings that accompany wine purchasing, consuming or sharing experiences. the present study uses a writing workshop and literature review to uncover the relationships between the dimensions of the emotions that people feel during an experience of this kind, along with the mode of expression of different consumer groups. it also offers specific analysis of the role played by brands among the different characteristics of emotions. towards this end, a series of wine-related experiential narratives has been compiled and treated via a structural text analysis approach followed by lexical contents analysis. one of the findings breaks consumers’ involvement down into four categories of emotions. another comes from a content analysis of the narratives that will differentiate among three separate consumer groups. lastly, suggestions will be made in terms of wider consequences for the field of wine marketing. © the authors. journal compilation.. emotions experiential marketing feelings narrative wine marketing","exploring inner landscapes through psychophenomenology: the contribution of neuro-linguistic programming to innovations in researching first person experience. purpose – the purpose of this paper is to explore a contemporary european development in research into first person accounts of experience, called psychophenomenology, that offers enhancements to phenomenological interviewing. it is a form of guided introspection that seeks to develop finely grained first-person accounts by using distinctions in language, internal sensory representations and imagery that have been incorporated from neuro-linguistic programming (nlp). it is also a participative, relational and developmental form of interviewing, in the sense that the interviewee can gain significant insight into their experience; the process is not concerned purely with data gathering. design/methodology/approach – the authors review the theoretical assumptions on which psychophenomenology is based, then describe the principal method used in psychophenomenology, the “explicitation interview”. the interview protocol is illustrated with transcript data, through which they identify specific aspects of nlp that have been incorporated into psychophenomenology. findings – psychophenomenology offers refinements to the precision of phenomenological methods found in organizational research, such as interpretative phenomenological analysis. research limitations/implications – the epistemological claims and implications of psychophenomenology are reviewed. practical implications – these developments may provide a basis for reconsidering the research value of introspection, which has often been dismissed as non-rigorous. originality/value – the paper introduces psychophenomenology to the field of organizational research. it also describes how psychophenomenology has innovated by drawing from nlp, an approach to personal development that is found in organizational practices such as executive coaching, in order to enhance the precision and rigour of both interviews and transcript analysis.. interviews neurolinguistic programming phenomenology qualitative research","children playing and learning in an online environment: a review of previous research and an examination of six current web sites. purpose this paper aims to examine what elements in online environments promote engagement, learning and repeated visits for children aged 6-12 years. design/methodology/approach an in-depth textual analysis, exploring components such as navigation, construction of site, character choice and development, style of text, types of questioning, animation, color and other factors, of six english-language web sites, describing themselves as “educational and fun”, was carried out against a background of literature available on web site design for children, relying particularly on media text analysis and an evaluation method produced in relation to children's motivation and web site use. findings the analysis of the six web sites resulted in a number of usability requirements for children's web sites, including the following: web sites should have an understanding of the community of users they serve; web sites should offer dynamic forms of learning; web sites should encourage interaction between users and site designers; web sites should offer open activities rather than closed ones; web sites should view young people as persons with rights. research limitations/implications insights gained from the analysis of six web sites are hard to generalize. user behavior was not studied. practical implications web designers should bear the usability requirements in mind when designing web sites for children. originality/value although educational content for children on the internet is growing exponentially, the area is relatively under-researched. this is one of the first detailed analyses of entertaining educational web sites targeting children. © 2009, emerald group publishing limited. children (age groups) education internet worldwide web","the changing digital dynamics of multichannel marketing the feasibility of the weblog: text mining approach for fast fashion trending. purpose - the purpose of this paper is to examine the theoretical/ conceptual development and application of weblog-textmining to fashion forecasting in general and street fashion trending in particular. design/methodology/approach - the current methods of forecasting cannot keep pace with the changing dynamics of the marketplace - mostly due to the rampant diffusion of data/information. the company that can tap the continual flow of data/information in the present, contrast it with a stored set of information from the past, and adjust based on repeated cycles, will have the best insight into the lingering trend, changing trend, or dynamic trend. the paper uses a simple example to explain blog trend analysis using nielsen buzzmetrics' blogpulse. findings - the study finds that to make fashion weblog forecasting a reality, there needs to be a rich accumulation of fashion communication in structured blogs. in addition, there needs to be a classification of the various forms of industry web text, web venue. furthermore, rich research traditions must be in place to chronicle the cultural, behavioral, linguistic, socioeconomic, and communication behaviors over time for the weblog and the fashion weblogger in particular. practical implications - the changing dynamics of the fashion business makes it a good example for understanding the weblog-text mining approach developed in this paper. originality/value - the understanding and implementation of trend forecasting using blogs as data mining sources will add another dimension of forecasting techniques to survive the multi-channel revolution in fashion marketing.. distribution channels and marketing fashion forecasting text retrieval worldwide web","advancing hermeneutic research for interpreting interfirm new product development. purpose - the principal objective here is to describe conceptual and research tools for achieving deeper sense-making of what happened and why it happened -including how participants interpret outcomes of what happened and the dynamics of emic (executive) and etic (researcher) sense-making. design/methodology/approach - this article uses a mixed research design including decision systems analysis, cognitive mapping, computer software-based text analysis, and the long interview method for mapping the mental models of the participants in specific decision-making processes as well as mapping the immediate, feedback, and downstream influences of decisions-actions-outcomes. findings - the findings in the empirical study support the view that decision processes are prospective, introspective, and retrospective, sporadically rational, ultimately affective, and altogether imaginatively unbounded. research limitations/implications - not using outside auditors to evaluate post-etic interpretations is recognized as a method limitation to the extended case study; such outside auditor reports represent an etic-4 level of interpretation. incorporating such etic-4 interpretation is one suggestion for further research. practical implications - asking executives for in-depth stories about what happened and why helps them reflect and uncover very subtle nuances of what went right and what went wrong. originality/value - a series advanced hermeneutic b2b research reports of a specific issue (e.g., new product innovation processes) provides an advance for developing a grounded theory of what happened and why it happened. such a large-scale research effort enables more rigorous, accurate and useful generalizations of decision making on a specific issue than is found in literature reviews of models of complex systems. © emerald group publishing limited.. decision making product development","analyzing hotel customers’ e-complaints from an internet complaint forum. recent web technology advancement has enabled online customers to express their concerns and negative experiences about the hotel services over the internet. this exploratory study attempts to provide some insights into this recent phenomenon by analyzing self-selected hotel customers' online complaints recorded on a specialized complaint forum. a content analysis ofthose e-complaints in 18 problem categories revealed that online customers were mostly unhappy with fundamental service failures (i.e., declined service quality and rude employees) in the hotel service encounter. surprisingly, only one in every five e-complaints received a response from the hotel company in the identified top-five complaint categories. the authors further used text-mining software to identify most frequently complained keywords and examine their relational associations. business implications of the research findings were also discussed. © 2005, taylor & francis group, llc. all rights reserved.. company response complaint handling e-complaints (online complaints) hotel service quality service recovery","internet banking: modelling the e-competence of customers with a text-analytic cit approach. purpose - the paper is aimed at modelling the e-competence of internet bank customers with a new text-analytic method. design/methodology/approach - a web survey using a critical incident technique approach (cit) and follow-up interviews was used to sequentially capture verbal accounts of the very first experience of users of a particular internet bank. text analyses were carried out from the complete set of responses to the trigger questions to synthesise user accounts of these experiences. findings - these verbal syntheses (as modelled by a pertex text-analytic approach) are purported to illustrate e-competence. a diverging pattern of e-competency was found. one group of customers was able to handle the web site and the other group felt abandoned and was not able to adopt technology. research limitations/implications - the study comprised a limited sample of danish first-time internet bank customers. therefore, generalisations should not be drawn as to the competencies of bank customers. further research could encompass much larger and broader samples and be focused on a particular internet function or e-service. practical implications - findings suggest that some customer groups may have difficulties in applying new technology and that these groups may have special needs of support in the start-up period. originality/value - the value of the paper is that it illustrates a new kind of text analysis that can summarise divergent texts and thereby distinguish between different kinds of user competence in a meaningful way. © emerald group publishing limited.. banking competences worldwide web","analysing competitors' online persuasive themes with text mining. information is power. cliché, but true. the internet has simultaneously empowered individuals. prospects can click, choose and control their online interactions. for organisations, the pendulum has swung in the opposite direction. they face more competition and are overloaded with more information than ever before. manual tracking and processing of competitor activity are tedious, inaccurate, and rapidly outdated. technology has created the “problem”, and technology can offer potential solutions. this study explores the use of text mining technology to analyse competitors' online promotional text messages. to examine its potential applications, a text mining analysis is applied to top educational sites in the usa. how their web content is positioned relative to their competitors is analysed and discussed. © 2004, emerald group publishing limited. competitive analysis data handling internet product positioning text retrieval","national differences in materialism using alternative research strategies to explore the construct. one of the core objectives in cross-national marketing research is to establish research results which are comparable across national entities. nevertheless, certain national idiosyncrasies (unique meaning of constructs, distinctive expressions) may hamper these cross-national research endeavors. two different approaches have been introduced in the social sciences, in order to cope with this comparability-dilemma. the “emic” and the “etic” school of thought. these can be seen as two extremes on the continuum of cross-national research methodology. the paper tries to illustrate advantages and potential shortcomings of the etic (mostly quantitative) vs. the emic (mostly qualitative) research approach. a combination of alternative, qualitative and quantitative research strategies was used to explore national differences in materialism. a questionnaire was developed comprising both qualitative and quantitative sections on materialism. the materialism scale, as operationalized by richins and dawson (1992), was used for the quantitative section. the research findings call for the use of alternative research strategies” to overcome the emic/etic duality in cross-national research. comparative text analysis and graphical representations of consumers’ statements can help to explore the reasons for conceptual differences. © 2001, taylor & francis group, llc. all rights reserved.. comparative text-analysis consumer values emic/etic research formalized qualitative research materialism","rapport in telemarketing - mirror, mirror on the call?. examines the hypothesis that the uk population has not yet fully developed a telemarketing culture and that there is, therefore, a particular need for telemarketers to understand how rapport might be developed on the telephone. relevant literature from the fields of social psychology, applied psychology and marketing are reviewed and a programme of research was carried out, comprising an omnibus to measure the extent of telemarketing experience in the uk population and a study among organisations with in-house telemarketing facilities to explore the types of practices that might foster rapport. it concludes that a telemarketing culture still has some way to develop and that, while many organisations used a number of seemingly relevant techniques, in particular nlp mirroring and matching, there are a number of issues still to be resolved regarding measurement of rapport as well as the theory and “measuring instruments” associated with nlp. other factors affecting the development of rapport in a telemarketing environment are also considered. © 1998, mcb up limited. direct marketing market segmentation marketing communications psychology telecommunications industry telemarketing","bridging the great divide - the transfer of techniques. discusses ways in which qualitative techniques might be incorporated in quantitative research and quantitative techniques in qualitative research - a transfer of techniques. explores the use of neuro-linguistic programming (nlp) and projective techniques in quantitative research. reports the results of customizing a self-completion questionnaire to a respondent’s preferred representational system (prs). this application of nlp produced encouraging findings. provides suggestions for further research. describes an example of how nlp and projective techniques can benefit a quantitative study with a case study in which trbi’s brandworks was used. suggests that, although the adoption by qualitative researchers of techniques used in quantitative research focuses on computer applications, the recent academic interest in the use of text analysers has not been matched by practitioners. discusses issues related to quality, validity, transparency and value, and reports the findings of a survey of the largest qualitative marketing research suppliers. finally, examines the use of correspondence analysis and describes ways in which correspondence analysis might benefit the qualitative researcher. © 1996, mcb up limited. computer management neuro-linguistic programming qualitative techniques quantitative techniques","computer-aided content analysis: what do 240 advertising slogans have in common?. this paper uses the technique of computer-aided text analysis to evaluate the meanings of 240 advertising slogans. content analysis performed by a computer is more reliable and less tedious than that performed by human coders. we analyze the content of the slogans and assume this reflects what the advertiser thinks that he or she said. one large group of equivocal slogans and four other more distinct groups are found.. advertising slogans computer-aided content analysis"],"liwc":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,3,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"liwc_alt":["0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","3","0","4","3","0","0","5","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","6","0","0","0","0","0","3","0","0","0","0","0","2","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","3","0","3","0","0","2","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","4","0","0","0","0","0","0","0","1","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","2","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","1","0","3","0","0","0","1","0","0","0","0","0","0","0","0","4","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","4","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0"]},"columns":[{"id":"combined_text","name":"combined_text","type":"character","width":850},{"id":"liwc","name":"liwc","type":"numeric","width":50},{"id":"liwc_alt","name":"liwc_alt","type":"character","width":50}],"searchable":true,"defaultPageSize":3,"dataKey":"d5c22ba467391a3d70d6dc987550a4bf"},"children":[]},"class":"reactR_markup"},"evals":[],"jsHooks":[]}</script>
</div>
</div>
<p>It seems that when researchers talk about LIWC, they always include “liwc” but not always “linguistic inquiry and word count” or other variations. We can therefore concentrate on the unique detection of “liwc”, “bert” and other acronyms in the text.</p>
<p>We use the <code>wesanderson</code> package to get a nice palette of colors. We pick the <code>Royal2</code> palette.</p>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>Royal2 <span class="ot"><-</span> wesanderson<span class="sc">::</span><span class="fu">wes_palette</span>(<span class="st">"Royal2"</span>)</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>fig <span class="ot"><-</span> <span class="fu">ggplot</span>(cumulative_data, <span class="fu">aes</span>(<span class="at">x =</span> year)) <span class="sc">+</span></span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y =</span> cum_liwc, <span class="at">linetype =</span> <span class="st">"LIWC"</span>, <span class="at">color =</span> <span class="st">"LIWC"</span>)) <span class="sc">+</span></span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y =</span> cum_leximancer, <span class="at">linetype =</span> <span class="st">"Leximancer"</span>, <span class="at">color =</span> <span class="st">"Leximancer"</span>)) <span class="sc">+</span></span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y =</span> cum_bert, <span class="at">linetype =</span> <span class="st">"BERT"</span>, <span class="at">color =</span> <span class="st">"BERT"</span>)) <span class="sc">+</span></span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y =</span> cum_chatgpt, <span class="at">linetype =</span> <span class="st">"ChatGPT"</span>, <span class="at">color =</span> <span class="st">"ChatGPT"</span>)) <span class="sc">+</span></span>
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">y =</span> cum_passivepy, <span class="at">linetype =</span> <span class="st">"PassivePy"</span>, <span class="at">color =</span> <span class="st">"PassivePy"</span>)) <span class="sc">+</span></span>
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_color_manual</span>(</span>
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a> <span class="at">name =</span> <span class="st">"Techniques"</span>,</span>
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a> <span class="at">values =</span> Royal2</span>
<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span></span>
<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_linetype_manual</span>(</span>
<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a> <span class="at">name =</span> <span class="st">"Techniques"</span>,</span>
<span id="cb8-15"><a href="#cb8-15" aria-hidden="true" tabindex="-1"></a> <span class="at">values =</span> <span class="fu">c</span>(</span>
<span id="cb8-16"><a href="#cb8-16" aria-hidden="true" tabindex="-1"></a> <span class="st">"LIWC"</span> <span class="ot">=</span> <span class="st">"solid"</span>,</span>
<span id="cb8-17"><a href="#cb8-17" aria-hidden="true" tabindex="-1"></a> <span class="st">"Leximancer"</span> <span class="ot">=</span> <span class="st">"dashed"</span>,</span>
<span id="cb8-18"><a href="#cb8-18" aria-hidden="true" tabindex="-1"></a> <span class="st">"BERT"</span> <span class="ot">=</span> <span class="st">"dotted"</span>,</span>
<span id="cb8-19"><a href="#cb8-19" aria-hidden="true" tabindex="-1"></a> <span class="st">"ChatGPT"</span> <span class="ot">=</span> <span class="st">"dotdash"</span>,</span>
<span id="cb8-20"><a href="#cb8-20" aria-hidden="true" tabindex="-1"></a> <span class="st">"PassivePy"</span> <span class="ot">=</span> <span class="st">"longdash"</span></span>
<span id="cb8-21"><a href="#cb8-21" aria-hidden="true" tabindex="-1"></a> ),</span>
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<span id="cb8-23"><a href="#cb8-23" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span></span>
<span id="cb8-24"><a href="#cb8-24" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb8-25"><a href="#cb8-25" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Evolution of NLP techniques in marketing"</span>,</span>
<span id="cb8-26"><a href="#cb8-26" aria-hidden="true" tabindex="-1"></a> <span class="at">subtitle =</span> <span class="st">"Cumulative sum of the number of articles mentioning each technique"</span>,</span>
<span id="cb8-27"><a href="#cb8-27" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="st">""</span>,</span>
<span id="cb8-28"><a href="#cb8-28" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="st">"Cumulative number of occurrences"</span></span>
<span id="cb8-29"><a href="#cb8-29" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span></span>
<span id="cb8-30"><a href="#cb8-30" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_x_continuous</span>(<span class="at">breaks =</span> <span class="fu">seq</span>(<span class="dv">2010</span>, <span class="dv">2023</span>, <span class="at">by =</span> <span class="dv">1</span>), <span class="at">labels =</span> <span class="fu">seq</span>(<span class="dv">2010</span>, <span class="dv">2023</span>, <span class="at">by =</span> <span class="dv">1</span>)) <span class="sc">+</span> <span class="co"># Set breaks and labels</span></span>
<span id="cb8-31"><a href="#cb8-31" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_minimal</span>() <span class="sc">+</span></span>
<span id="cb8-32"><a href="#cb8-32" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">"bottom"</span>) <span class="co"># Move the legend to the bottom for better visibility</span></span>
<span id="cb8-33"><a href="#cb8-33" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-34"><a href="#cb8-34" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplotly</span>(fig)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<p>It seems like Leximancer is coming to an end, while BERT and ChatGPT are on the rise. PassivePy is also gaining traction but we can’t really conclude anything about it yet since the article has been published in 2022 @(sepehri2022passivepy). One interesthing to notice is that BERT</p>
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</script><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class="">
<div class="sourceCode" id="cb9" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> "Systematic literature review"</span></span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a><span class="an">bibliography:</span><span class="co"> references.bib</span></span>
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a><span class="an">title-block-banner:</span><span class="co"> true</span></span>
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a><span class="an">subtitle:</span><span class="co"> "NLP techniques used in marketing"</span></span>
<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a><span class="an">author:</span></span>
<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a><span class="co"> - name: Olivier Caron</span></span>
<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a><span class="co"> email: [email protected]</span></span>
<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a><span class="co"> affiliations: </span></span>
<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a><span class="co"> name: "Paris Dauphine - PSL"</span></span>
<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a><span class="co"> city: Paris</span></span>
<span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a><span class="co"> state: France</span></span>
<span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a><span class="co"> - name: Christophe Benavent</span></span>
<span id="cb9-14"><a href="#cb9-14" aria-hidden="true" tabindex="-1"></a><span class="co"> email: [email protected]</span></span>
<span id="cb9-15"><a href="#cb9-15" aria-hidden="true" tabindex="-1"></a><span class="co"> affiliations: </span></span>
<span id="cb9-16"><a href="#cb9-16" aria-hidden="true" tabindex="-1"></a><span class="co"> name: "Paris Dauphine - PSL"</span></span>
<span id="cb9-17"><a href="#cb9-17" aria-hidden="true" tabindex="-1"></a><span class="co"> city: Paris</span></span>
<span id="cb9-18"><a href="#cb9-18" aria-hidden="true" tabindex="-1"></a><span class="co"> state: France</span></span>
<span id="cb9-19"><a href="#cb9-19" aria-hidden="true" tabindex="-1"></a><span class="an">date :</span><span class="co"> "last-modified"</span></span>
<span id="cb9-20"><a href="#cb9-20" aria-hidden="true" tabindex="-1"></a><span class="an">toc:</span><span class="co"> true</span></span>
<span id="cb9-21"><a href="#cb9-21" aria-hidden="true" tabindex="-1"></a><span class="an">number-sections:</span><span class="co"> true</span></span>
<span id="cb9-22"><a href="#cb9-22" aria-hidden="true" tabindex="-1"></a><span class="an">number-depth:</span><span class="co"> 5</span></span>
<span id="cb9-23"><a href="#cb9-23" aria-hidden="true" tabindex="-1"></a><span class="an">format:</span></span>
<span id="cb9-24"><a href="#cb9-24" aria-hidden="true" tabindex="-1"></a><span class="co"> html:</span></span>
<span id="cb9-25"><a href="#cb9-25" aria-hidden="true" tabindex="-1"></a><span class="co"> theme:</span></span>
<span id="cb9-26"><a href="#cb9-26" aria-hidden="true" tabindex="-1"></a><span class="co"> light: yeti</span></span>
<span id="cb9-27"><a href="#cb9-27" aria-hidden="true" tabindex="-1"></a><span class="co"> dark: darkly</span></span>
<span id="cb9-28"><a href="#cb9-28" aria-hidden="true" tabindex="-1"></a><span class="co"> code-fold: true</span></span>
<span id="cb9-29"><a href="#cb9-29" aria-hidden="true" tabindex="-1"></a><span class="co"> code-summary: "Display code"</span></span>
<span id="cb9-30"><a href="#cb9-30" aria-hidden="true" tabindex="-1"></a><span class="co"> code-tools: true #enables to display/hide all blocks of code</span></span>
<span id="cb9-31"><a href="#cb9-31" aria-hidden="true" tabindex="-1"></a><span class="co"> code-copy: true #enables to copy code</span></span>
<span id="cb9-32"><a href="#cb9-32" aria-hidden="true" tabindex="-1"></a><span class="co"> grid:</span></span>
<span id="cb9-33"><a href="#cb9-33" aria-hidden="true" tabindex="-1"></a><span class="co"> body-width: 1000px</span></span>
<span id="cb9-34"><a href="#cb9-34" aria-hidden="true" tabindex="-1"></a><span class="co"> margin-width: 100px</span></span>
<span id="cb9-35"><a href="#cb9-35" aria-hidden="true" tabindex="-1"></a><span class="co"> toc: true</span></span>
<span id="cb9-36"><a href="#cb9-36" aria-hidden="true" tabindex="-1"></a><span class="co"> toc-location: left</span></span>
<span id="cb9-37"><a href="#cb9-37" aria-hidden="true" tabindex="-1"></a><span class="an">execute:</span></span>
<span id="cb9-38"><a href="#cb9-38" aria-hidden="true" tabindex="-1"></a><span class="co"> echo: true</span></span>
<span id="cb9-39"><a href="#cb9-39" aria-hidden="true" tabindex="-1"></a><span class="co"> warning: false</span></span>
<span id="cb9-40"><a href="#cb9-40" aria-hidden="true" tabindex="-1"></a><span class="co"> message: false</span></span>
<span id="cb9-41"><a href="#cb9-41" aria-hidden="true" tabindex="-1"></a><span class="an">editor:</span><span class="co"> visual</span></span>
<span id="cb9-42"><a href="#cb9-42" aria-hidden="true" tabindex="-1"></a><span class="an">fig-align:</span><span class="co"> "center"</span></span>
<span id="cb9-43"><a href="#cb9-43" aria-hidden="true" tabindex="-1"></a><span class="an">highlight-style:</span><span class="co"> ayu</span></span>
<span id="cb9-44"><a href="#cb9-44" aria-hidden="true" tabindex="-1"></a><span class="an">css:</span><span class="co"> styles.css</span></span>
<span id="cb9-45"><a href="#cb9-45" aria-hidden="true" tabindex="-1"></a><span class="an">reference-location:</span><span class="co"> margin</span></span>
<span id="cb9-46"><a href="#cb9-46" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
<span id="cb9-47"><a href="#cb9-47" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-48"><a href="#cb9-48" aria-hidden="true" tabindex="-1"></a><span class="fu">## Libraries R and loading data</span></span>
<span id="cb9-49"><a href="#cb9-49" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-52"><a href="#cb9-52" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb9-53"><a href="#cb9-53" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: load-packages</span></span>
<span id="cb9-54"><a href="#cb9-54" aria-hidden="true" tabindex="-1"></a><span class="co">#| message: false</span></span>
<span id="cb9-55"><a href="#cb9-55" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-56"><a href="#cb9-56" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
<span id="cb9-57"><a href="#cb9-57" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(reactable)</span>
<span id="cb9-58"><a href="#cb9-58" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(plotly)</span>
<span id="cb9-59"><a href="#cb9-59" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(wesanderson)</span>
<span id="cb9-60"><a href="#cb9-60" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb9-61"><a href="#cb9-61" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-64"><a href="#cb9-64" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb9-65"><a href="#cb9-65" aria-hidden="true" tabindex="-1"></a><span class="co">#load data</span></span>
<span id="cb9-66"><a href="#cb9-66" aria-hidden="true" tabindex="-1"></a>data_embeddings <span class="ot"><-</span> <span class="fu">read.csv</span>(<span class="st">"data_for_embeddings.csv"</span>)</span>
<span id="cb9-67"><a href="#cb9-67" aria-hidden="true" tabindex="-1"></a>annotations_stanza <span class="ot"><-</span> <span class="fu">read.csv</span>(<span class="st">"annotated_stanza.csv"</span>)</span>
<span id="cb9-68"><a href="#cb9-68" aria-hidden="true" tabindex="-1"></a>annotations_udpipe <span class="ot"><-</span> <span class="fu">read.csv</span>(<span class="st">"annotated_udpipe.csv"</span>)</span>
<span id="cb9-69"><a href="#cb9-69" aria-hidden="true" tabindex="-1"></a>annotations_trankit <span class="ot"><-</span> <span class="fu">read.csv</span>(<span class="st">"annotated_trankit.csv"</span>)</span>
<span id="cb9-70"><a href="#cb9-70" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-71"><a href="#cb9-71" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb9-72"><a href="#cb9-72" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-73"><a href="#cb9-73" aria-hidden="true" tabindex="-1"></a><span class="fu">## Loading of all annotations (Stanza, UDPipe, Trankit)</span></span>
<span id="cb9-74"><a href="#cb9-74" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-77"><a href="#cb9-77" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb9-78"><a href="#cb9-78" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: list-annotations</span></span>
<span id="cb9-79"><a href="#cb9-79" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-80"><a href="#cb9-80" aria-hidden="true" tabindex="-1"></a><span class="co">#annotation filter on PROPN</span></span>
<span id="cb9-81"><a href="#cb9-81" aria-hidden="true" tabindex="-1"></a>annotations_stanza_propn <span class="ot"><-</span> annotations_stanza <span class="sc">%>%</span> <span class="fu">filter</span>(upos <span class="sc">==</span> <span class="st">"PROPN"</span>)</span>
<span id="cb9-82"><a href="#cb9-82" aria-hidden="true" tabindex="-1"></a>annotations_udpipe_propn <span class="ot"><-</span> annotations_udpipe <span class="sc">%>%</span> <span class="fu">filter</span>(upos <span class="sc">==</span> <span class="st">"PROPN"</span>)</span>
<span id="cb9-83"><a href="#cb9-83" aria-hidden="true" tabindex="-1"></a>annotations_trankit_propn <span class="ot"><-</span> annotations_trankit <span class="sc">%>%</span> <span class="fu">filter</span>(upos <span class="sc">==</span> <span class="st">"PROPN"</span>)</span>
<span id="cb9-84"><a href="#cb9-84" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-85"><a href="#cb9-85" aria-hidden="true" tabindex="-1"></a><span class="co">#list of most frequent words in annotatinos</span></span>
<span id="cb9-86"><a href="#cb9-86" aria-hidden="true" tabindex="-1"></a>toppropn_stanza <span class="ot"><-</span> annotations_stanza_propn <span class="sc">%>%</span> <span class="fu">count</span>(lemma, <span class="at">sort =</span> <span class="cn">TRUE</span>) <span class="sc">%>%</span> <span class="fu">drop_na</span>() <span class="sc">%>%</span> <span class="fu">filter</span>(n <span class="sc">></span> <span class="dv">1</span>)</span>
<span id="cb9-87"><a href="#cb9-87" aria-hidden="true" tabindex="-1"></a>toppropn_udpipe <span class="ot"><-</span> annotations_udpipe_propn <span class="sc">%>%</span> <span class="fu">count</span>(lemma, <span class="at">sort =</span> <span class="cn">TRUE</span>) <span class="sc">%>%</span> <span class="fu">drop_na</span>() <span class="sc">%>%</span> <span class="fu">filter</span>(n <span class="sc">></span> <span class="dv">1</span>)</span>
<span id="cb9-88"><a href="#cb9-88" aria-hidden="true" tabindex="-1"></a>toppropn_trankit <span class="ot"><-</span> annotations_trankit_propn <span class="sc">%>%</span> <span class="fu">count</span>(lemma, <span class="at">sort =</span> <span class="cn">TRUE</span>) <span class="sc">%>%</span> <span class="fu">drop_na</span>() <span class="sc">%>%</span> <span class="fu">filter</span>(n <span class="sc">></span> <span class="dv">1</span>)</span>
<span id="cb9-89"><a href="#cb9-89" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-90"><a href="#cb9-90" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb9-91"><a href="#cb9-91" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-92"><a href="#cb9-92" aria-hidden="true" tabindex="-1"></a><span class="fu">### Table of most frequent PROPN words in the Stanza annotations</span></span>
<span id="cb9-93"><a href="#cb9-93" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-96"><a href="#cb9-96" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb9-97"><a href="#cb9-97" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: reactable-stanza</span></span>
<span id="cb9-98"><a href="#cb9-98" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-99"><a href="#cb9-99" aria-hidden="true" tabindex="-1"></a><span class="co">#reactable of most frequent words in annotations_propn</span></span>
<span id="cb9-100"><a href="#cb9-100" aria-hidden="true" tabindex="-1"></a>toppropn_stanza <span class="sc">%>%</span> </span>
<span id="cb9-101"><a href="#cb9-101" aria-hidden="true" tabindex="-1"></a> <span class="fu">reactable</span>(</span>
<span id="cb9-102"><a href="#cb9-102" aria-hidden="true" tabindex="-1"></a> <span class="at">searchable =</span> <span class="cn">TRUE</span>,</span>
<span id="cb9-103"><a href="#cb9-103" aria-hidden="true" tabindex="-1"></a> <span class="at">defaultColDef =</span> <span class="fu">colDef</span>(</span>
<span id="cb9-104"><a href="#cb9-104" aria-hidden="true" tabindex="-1"></a> <span class="at">minWidth =</span> <span class="dv">100</span>,</span>
<span id="cb9-105"><a href="#cb9-105" aria-hidden="true" tabindex="-1"></a> <span class="at">sortable =</span> <span class="cn">TRUE</span></span>
<span id="cb9-106"><a href="#cb9-106" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb9-107"><a href="#cb9-107" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb9-108"><a href="#cb9-108" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb9-109"><a href="#cb9-109" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-110"><a href="#cb9-110" aria-hidden="true" tabindex="-1"></a><span class="fu">### Table of most frequent PROPN words in the UDPipe annotations</span></span>
<span id="cb9-111"><a href="#cb9-111" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-114"><a href="#cb9-114" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb9-115"><a href="#cb9-115" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: reactable-udpipe</span></span>
<span id="cb9-116"><a href="#cb9-116" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-117"><a href="#cb9-117" aria-hidden="true" tabindex="-1"></a><span class="co">#reactable of most frequent words in annotations_propn</span></span>
<span id="cb9-118"><a href="#cb9-118" aria-hidden="true" tabindex="-1"></a>toppropn_udpipe <span class="sc">%>%</span> </span>
<span id="cb9-119"><a href="#cb9-119" aria-hidden="true" tabindex="-1"></a> <span class="fu">reactable</span>(</span>
<span id="cb9-120"><a href="#cb9-120" aria-hidden="true" tabindex="-1"></a> <span class="at">searchable =</span> <span class="cn">TRUE</span>,</span>
<span id="cb9-121"><a href="#cb9-121" aria-hidden="true" tabindex="-1"></a> <span class="at">defaultColDef =</span> <span class="fu">colDef</span>(</span>
<span id="cb9-122"><a href="#cb9-122" aria-hidden="true" tabindex="-1"></a> <span class="at">minWidth =</span> <span class="dv">100</span>,</span>
<span id="cb9-123"><a href="#cb9-123" aria-hidden="true" tabindex="-1"></a> <span class="at">sortable =</span> <span class="cn">TRUE</span></span>
<span id="cb9-124"><a href="#cb9-124" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb9-125"><a href="#cb9-125" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb9-126"><a href="#cb9-126" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb9-127"><a href="#cb9-127" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-128"><a href="#cb9-128" aria-hidden="true" tabindex="-1"></a><span class="fu">### Table of most frequent PROPN words in the Trankit annotations</span></span>
<span id="cb9-129"><a href="#cb9-129" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-132"><a href="#cb9-132" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb9-133"><a href="#cb9-133" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: reactable-trankit</span></span>
<span id="cb9-134"><a href="#cb9-134" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-135"><a href="#cb9-135" aria-hidden="true" tabindex="-1"></a><span class="co">#reactable of most frequent words in annotations_propn</span></span>
<span id="cb9-136"><a href="#cb9-136" aria-hidden="true" tabindex="-1"></a>toppropn_trankit <span class="sc">%>%</span> </span>
<span id="cb9-137"><a href="#cb9-137" aria-hidden="true" tabindex="-1"></a> <span class="fu">reactable</span>(</span>
<span id="cb9-138"><a href="#cb9-138" aria-hidden="true" tabindex="-1"></a> <span class="at">searchable =</span> <span class="cn">TRUE</span>,</span>
<span id="cb9-139"><a href="#cb9-139" aria-hidden="true" tabindex="-1"></a> <span class="at">defaultColDef =</span> <span class="fu">colDef</span>(</span>
<span id="cb9-140"><a href="#cb9-140" aria-hidden="true" tabindex="-1"></a> <span class="at">minWidth =</span> <span class="dv">100</span>,</span>
<span id="cb9-141"><a href="#cb9-141" aria-hidden="true" tabindex="-1"></a> <span class="at">sortable =</span> <span class="cn">TRUE</span></span>
<span id="cb9-142"><a href="#cb9-142" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb9-143"><a href="#cb9-143" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb9-144"><a href="#cb9-144" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb9-145"><a href="#cb9-145" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-146"><a href="#cb9-146" aria-hidden="true" tabindex="-1"></a><span class="fu">## NLP techniques in Marketing</span></span>
<span id="cb9-147"><a href="#cb9-147" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-148"><a href="#cb9-148" aria-hidden="true" tabindex="-1"></a><span class="fu">### Generic techniques</span></span>
<span id="cb9-149"><a href="#cb9-149" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-150"><a href="#cb9-150" aria-hidden="true" tabindex="-1"></a>We want to provide a list of recurrent techniques in marketing. The tables above clearly show that some of them are often used. Let's focus first on them in no particular order.</span>
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<span id="cb9-152"><a href="#cb9-152" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>LIWC</span>
<span id="cb9-153"><a href="#cb9-153" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-154"><a href="#cb9-154" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>Leximancer</span>
<span id="cb9-155"><a href="#cb9-155" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-156"><a href="#cb9-156" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>BERT</span>
<span id="cb9-157"><a href="#cb9-157" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-158"><a href="#cb9-158" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>ChatGPT</span>
<span id="cb9-159"><a href="#cb9-159" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-160"><a href="#cb9-160" aria-hidden="true" tabindex="-1"></a><span class="ss">- </span>PassivePy</span>
<span id="cb9-161"><a href="#cb9-161" aria-hidden="true" tabindex="-1"></a></span>
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<span id="cb9-165"><a href="#cb9-165" aria-hidden="true" tabindex="-1"></a><span class="in">```{r}</span></span>
<span id="cb9-166"><a href="#cb9-166" aria-hidden="true" tabindex="-1"></a><span class="co">#i want to get just one number if multiple words of the vector are found in the string</span></span>
<span id="cb9-167"><a href="#cb9-167" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-168"><a href="#cb9-168" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-169"><a href="#cb9-169" aria-hidden="true" tabindex="-1"></a><span class="co">#detect and count the words above in the "combined_text" column of data_embeddings</span></span>
<span id="cb9-170"><a href="#cb9-170" aria-hidden="true" tabindex="-1"></a>data_embeddings<span class="sc">$</span>combined_text <span class="ot"><-</span> <span class="fu">tolower</span>(data_embeddings<span class="sc">$</span>combined_text)</span>
<span id="cb9-171"><a href="#cb9-171" aria-hidden="true" tabindex="-1"></a>data_embeddings<span class="sc">$</span>liwc <span class="ot"><-</span> <span class="fu">str_count</span>(data_embeddings<span class="sc">$</span>combined_text, <span class="st">"liwc"</span>)</span>
<span id="cb9-172"><a href="#cb9-172" aria-hidden="true" tabindex="-1"></a>data_embeddings<span class="sc">$</span>leximancer <span class="ot"><-</span> <span class="fu">str_count</span>(data_embeddings<span class="sc">$</span>combined_text, <span class="st">"leximancer"</span>)</span>
<span id="cb9-173"><a href="#cb9-173" aria-hidden="true" tabindex="-1"></a>data_embeddings<span class="sc">$</span>bert <span class="ot"><-</span> <span class="fu">str_count</span>(data_embeddings<span class="sc">$</span>combined_text, <span class="st">"bert"</span>)</span>
<span id="cb9-174"><a href="#cb9-174" aria-hidden="true" tabindex="-1"></a>data_embeddings<span class="sc">$</span>chatgpt <span class="ot"><-</span> <span class="fu">str_count</span>(data_embeddings<span class="sc">$</span>combined_text, <span class="st">"chatgpt"</span>)</span>
<span id="cb9-175"><a href="#cb9-175" aria-hidden="true" tabindex="-1"></a>data_embeddings<span class="sc">$</span>passivepy <span class="ot"><-</span> <span class="fu">str_count</span>(data_embeddings<span class="sc">$</span>combined_text, <span class="st">"passivepy"</span>)</span>
<span id="cb9-176"><a href="#cb9-176" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-177"><a href="#cb9-177" aria-hidden="true" tabindex="-1"></a><span class="co"># Group by year and calculate the cumulative sum for each technique</span></span>
<span id="cb9-178"><a href="#cb9-178" aria-hidden="true" tabindex="-1"></a>sum_data <span class="ot"><-</span> data_embeddings <span class="sc">%>%</span></span>
<span id="cb9-179"><a href="#cb9-179" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(year) <span class="sc">%>%</span></span>
<span id="cb9-180"><a href="#cb9-180" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarize</span>(</span>
<span id="cb9-181"><a href="#cb9-181" aria-hidden="true" tabindex="-1"></a> <span class="at">sum_liwc =</span> <span class="fu">sum</span>(liwc),</span>
<span id="cb9-182"><a href="#cb9-182" aria-hidden="true" tabindex="-1"></a> <span class="at">sum_leximancer =</span> <span class="fu">sum</span>(leximancer),</span>
<span id="cb9-183"><a href="#cb9-183" aria-hidden="true" tabindex="-1"></a> <span class="at">sum_bert =</span> <span class="fu">sum</span>(bert),</span>
<span id="cb9-184"><a href="#cb9-184" aria-hidden="true" tabindex="-1"></a> <span class="at">sum_chatgpt =</span> <span class="fu">sum</span>(chatgpt),</span>
<span id="cb9-185"><a href="#cb9-185" aria-hidden="true" tabindex="-1"></a> <span class="at">sum_passivepy =</span> <span class="fu">sum</span>(passivepy)</span>
<span id="cb9-186"><a href="#cb9-186" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span>
<span id="cb9-187"><a href="#cb9-187" aria-hidden="true" tabindex="-1"></a> <span class="fu">ungroup</span>()</span>
<span id="cb9-188"><a href="#cb9-188" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-189"><a href="#cb9-189" aria-hidden="true" tabindex="-1"></a>cumulative_data <span class="ot"><-</span> sum_data <span class="sc">%>%</span></span>
<span id="cb9-190"><a href="#cb9-190" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span>
<span id="cb9-191"><a href="#cb9-191" aria-hidden="true" tabindex="-1"></a> <span class="at">cum_liwc =</span> <span class="fu">cumsum</span>(sum_liwc),</span>
<span id="cb9-192"><a href="#cb9-192" aria-hidden="true" tabindex="-1"></a> <span class="at">cum_leximancer =</span> <span class="fu">cumsum</span>(sum_leximancer),</span>
<span id="cb9-193"><a href="#cb9-193" aria-hidden="true" tabindex="-1"></a> <span class="at">cum_bert =</span> <span class="fu">cumsum</span>(sum_bert),</span>
<span id="cb9-194"><a href="#cb9-194" aria-hidden="true" tabindex="-1"></a> <span class="at">cum_chatgpt =</span> <span class="fu">cumsum</span>(sum_chatgpt),</span>
<span id="cb9-195"><a href="#cb9-195" aria-hidden="true" tabindex="-1"></a> <span class="at">cum_passivepy =</span> <span class="fu">cumsum</span>(sum_passivepy)</span>
<span id="cb9-196"><a href="#cb9-196" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span>
<span id="cb9-197"><a href="#cb9-197" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(year <span class="sc">></span> <span class="dv">2012</span>)</span>
<span id="cb9-198"><a href="#cb9-198" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-199"><a href="#cb9-199" aria-hidden="true" tabindex="-1"></a>patterns <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"linguistic inquiry and word count"</span>, <span class="st">"linguistic inquiry"</span>, <span class="st">"linguistic inquiry word count"</span>, <span class="st">"liwc"</span>)</span>
<span id="cb9-200"><a href="#cb9-200" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-201"><a href="#cb9-201" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a new column "liwc_alt" with initial empty strings</span></span>
<span id="cb9-202"><a href="#cb9-202" aria-hidden="true" tabindex="-1"></a>data_embeddings<span class="sc">$</span>liwc_alt <span class="ot"><-</span> <span class="st">""</span></span>
<span id="cb9-203"><a href="#cb9-203" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-204"><a href="#cb9-204" aria-hidden="true" tabindex="-1"></a><span class="co"># Find the column index for "liwc_alt"</span></span>
<span id="cb9-205"><a href="#cb9-205" aria-hidden="true" tabindex="-1"></a>ncol <span class="ot"><-</span> <span class="fu">which</span>(<span class="fu">colnames</span>(data_embeddings) <span class="sc">==</span> <span class="st">"liwc_alt"</span>)</span>
<span id="cb9-206"><a href="#cb9-206" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-207"><a href="#cb9-207" aria-hidden="true" tabindex="-1"></a><span class="co"># Loop through each row and pattern to count occurrences and populate "liwc_alt"</span></span>
<span id="cb9-208"><a href="#cb9-208" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="fu">nrow</span>(data_embeddings)) {</span>
<span id="cb9-209"><a href="#cb9-209" aria-hidden="true" tabindex="-1"></a> counts <span class="ot"><-</span> <span class="fu">sapply</span>(patterns, <span class="cf">function</span>(pattern) {</span>
<span id="cb9-210"><a href="#cb9-210" aria-hidden="true" tabindex="-1"></a> <span class="fu">str_count</span>(data_embeddings<span class="sc">$</span>combined_text[i], pattern)</span>
<span id="cb9-211"><a href="#cb9-211" aria-hidden="true" tabindex="-1"></a> })</span>