Will We Be the Last Human Editors of JCR? | Journal of Consumer Research

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class="orchid-icon" alt="ORCID logo" aria-hidden="true" src="//oup.silverchair-cdn.com/Themes/Silver/app/img/mini-icon.png"/>  https://orcid.org/0000-0003-0770-8338 </a> </div> <div class="info-card-search-label"> Search for other works by this author on: </div> <div class="info-card-search info-card-search-internal"> <a href="/jcr/search-results?f_Authors=June+Cotte" rel="nofollow">Oxford Academic</a> </div> <div class="info-card-search info-card-search-pubmed"> <a href="http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=Cotte J">PubMed</a> </div> <div class="info-card-search info-card-search-google"> <a href="http://scholar.google.com/scholar?q=author:%22Cotte June%22">Google Scholar</a> </div> </div> </span> </span> <span class="al-author-name-more js-flyout-wrap"> <button type="button" class="linked-name js-linked-name-trigger btn-as-link">Markus Giesler</button><span class='delimiter'>, </span> <span class="al-author-info-wrap arrow-up"> <div class="info-card-author 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class="name-role-wrap"> <div class="info-card-name"> Andrew T Stephen </div> </div> <div class="info-card-search-label"> Search for other works by this author on: </div> <div class="info-card-search info-card-search-internal"> <a href="/jcr/search-results?f_Authors=Andrew+T+Stephen" rel="nofollow">Oxford Academic</a> </div> <div class="info-card-search info-card-search-pubmed"> <a href="http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=Stephen A">PubMed</a> </div> <div class="info-card-search info-card-search-google"> <a href="http://scholar.google.com/scholar?q=author:%22Stephen Andrew T%22">Google Scholar</a> </div> </div> </span> </span> <span class="al-author-name-more js-flyout-wrap"> <button type="button" class="linked-name js-linked-name-trigger btn-as-link">Stacy Wood</button><span class='delimiter'></span> <span class="al-author-info-wrap arrow-up"> <div class="info-card-author authorInfo_OUP_ArticleTop_Info_Widget"> <div class="name-role-wrap"> <div class="info-card-name"> Stacy Wood </div> </div> <div class="info-card-location"> <a id="contrib-orcid-0000-0001-5256-1772" href="https://orcid.org/0000-0001-5256-1772"> <img class="orchid-icon" alt="ORCID logo" aria-hidden="true" src="//oup.silverchair-cdn.com/Themes/Silver/app/img/mini-icon.png"/>  https://orcid.org/0000-0001-5256-1772 </a> </div> <div class="info-card-search-label"> Search for other works by this author on: </div> <div class="info-card-search info-card-search-internal"> <a href="/jcr/search-results?f_Authors=Stacy+Wood" rel="nofollow">Oxford Academic</a> </div> <div class="info-card-search info-card-search-pubmed"> <a href="http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=Wood S">PubMed</a> </div> <div class="info-card-search info-card-search-google"> <a href="http://scholar.google.com/scholar?q=author:%22Wood Stacy%22">Google Scholar</a> </div> </div> </span> </span> </div> </div> <div class="pub-history-wrap clearfix js-history-dropdown-wrap"> <div class="pub-history-row clearfix"> <div class="ww-citation-primary"><em>Journal of Consumer Research</em>, Volume 51, Issue 3, October 2024, Pages 451–454, <a href='https://doi.org/10.1093/jcr/ucae053'>https://doi.org/10.1093/jcr/ucae053</a></div> </div> <div class="pub-history-row clearfix"> <div class="ww-citation-date-wrap"> <div class="citation-label">Published:</div> <div class="citation-date">16 September 2024</div> </div> </div> </div> </div> </div> <script> $(document).ready(function () { $('.article-top-widget').on('click', '.ati-toggle-trigger', function () { $(this).find('.icon-general-add, .icon-minus').toggleClass('icon-minus icon-general-add'); $(this).siblings('.ati-toggle-content').toggleClass('hide'); }); // In Chrome, an anchor tag with target="_blank" and a "mailto:" href opens a new tab/window as well as the email client // I suspect this behavior will be corrected in the future // Remove the target="_blank" $('ul.wi-affiliationList').find('a[href^="mailto:"]').each(function () { 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navbar-search-submit icon-menu_search"> <span class="screenreader-text">Search</span> </a> </div> </div> <div class="navbar-search-advanced"><a href="/jcr/advanced-search" class="advanced-search js-advanced-search">Advanced Search</a></div> </div> <div class="navbar-search-collapsed"><a href="javascript:;" class="icon-menu_search js_expand-navsearch"><span class="screenreader-text">Search Menu</span></a></div> </div> </div> <input id="routename" name="RouteName" type="hidden" value="jcr" /> </div> </div> </div> </div> <div id="ContentTab" class="content active"> <div class="widget widget-ArticleFulltext widget-instance-OUP_Article_FullText_Widget"> <div class="module-widget"> <div class="widget-items" data-widgetname="ArticleFulltext"> <p class="chapter-para">Early in our editorial tenure, we enjoyed an animated discussion of the expanding potential of artificial intelligence (AI). Bernd Schmitt wondered aloud if he would be the last human Editor-in-Chief at <em>JCR</em> to the (almost) universal eye-rolling of the other editors. That was in 2021. But in 2024, the question seems more prescient than ridiculous.</p><p class="chapter-para">The quick and easy answer is, <em>of course not</em>. There is a new human editorial team whose tenure runs from 2025 to 2028, and commitments will be honored.</p><p class="chapter-para">However, the long answer is considerably more difficult and is the focus of this editorial. Thinking through the ways that we utilize AI in the creation, review, and publication of consumer research requires a specificity of analysis that goes beyond a polarized black-or-white approach. We see essays on the topic that take a simple yes (“Welcome to our robot future!”) or no (“Robots in research are a sign of the end-times”) viewpoint. But increasingly, scholars must consider how human effort and AI can combine in productive and responsible ways to enhance the quality, efficiency, and accessibility of our work. Here, we offer a brief analysis specific to consumer research.</p> <h2 scrollto-destination=481193875 id="481193875" class="section-title js-splitscreen-section-title" >HOW IT BEGAN: THE DIGITIZATION OF CONSUMER RESEARCH PRACTICE</h2> <p class="chapter-para">It has been decades since the Internet and other technologies changed the way we do research. We no longer sit among the library stacks or borrow colleagues’ journals (and tap into their human memories) to find papers for our literature reviews. This has been beneficial. We access journals easily from any location, we can search for work that is obscure or forgotten, we search for literature in adjacent fields, and it all takes so much less time and less paper than it did before. What we lost was some of the human element—asking colleagues for ideas, browsing the library shelves, attending conference talks just to see what papers the speakers referenced. There was serendipity and magic in crafting a literature review, and it is easy to feel nostalgic for those times (at least for those of us who remember). Yet not every scholar had access to the colleagues, the libraries, and the conference circuit, and so digital technology has operated as a game-changer, making consumer research more widely accessible.</p><p class="chapter-para">Similarly, we have witnessed the rise of digital sources for data. We can survey and experiment with “real consumer” populations using online research panels, accessing research participants from all over the world. Digital tools also expand the reach of qualitative research, allowing for participants to respond <em>in situ</em> via prompts on their phones, to record their experiences, and to provide a richer collage of informational modalities. The online world of human communities provides opinions, reviews, sales pitches, influencer behaviors, and countless other forms of consumer and seller information that can be scraped, sorted, and analyzed using increasingly sophisticated machine learning tools. In each of these situations, the consumer research community has helped promote, instruct, and caution other community members about how these data sources can be best accessed and used. Over time, new digital sources have been adopted and adapted through the market forces of publication.</p><p class="chapter-para">And, without doubt, we use digital tools like email, instant messaging, and video software programs to communicate easily and at low cost with colleagues around the world. An entire editorial could be devoted to the benefits and costs of this transition from working only with colleagues within your university’s department to being able to work with anyone, anywhere.</p> <h2 scrollto-destination=481193879 id="481193879" class="section-title js-splitscreen-section-title" >HOW IT’S GOING: THE DEMOCRATIZATION OF POWERFUL AI TOOLS</h2> <p class="chapter-para">While many digital advances have emerged and evolved over time, affecting our lives as consumer researchers, academics, and broadly as people, the recent widespread emergence of generative AI (GenAI), particularly large language models (LLMs) such as OpenAI’s ChatGPT, feels different. Just two years ago, many scholars had not yet tried LLMs themselves and did not know how to approach it with their students, let alone in their research practice. The question then was, ‘should we embrace or exclude GenAI?’ Now, it is a question of <em>how</em> we embrace it in ways that make our work better and more meaningful. Consider how GenAI can be used in each stage of a research project.</p> <h3 scrollto-destination=481193881 id="481193881" class="section-title js-splitscreen-section-title" >Ideation: Idea Creator versus Idea Elaborator</h3> <p class="chapter-para">Current models of GenAI can be asked to generate new research ideas by assessing gaps in the extant literature. Because the relevant literature for many consumer researchers is quite large at this stage in our field’s development and because we are also considering adjacent fields given the inherently cross-disciplinary nature of consumer research, AI can exceed human abilities around this task. Perhaps AI-generated ideas could also be more applicable and more actionable because they can include both academic literatures and industry publications in their codex. So this could be helpful to researchers who want access to industry executives. GenAI may also be used to elaborate on human-generated research questions. Here, the ability of AI to search vast online sources may uncover related research questions, real-world stakeholders who ask the question, or cross-disciplinary sources relevant to the question. Many issues arise when we consider AI as an idea creator or an idea elaborator. Should scholars be required to disclose when AI was used in either of these instances? Should AI be listed as a coauthor when used as an idea creator? Should different AI sources be triangulated to improve information accuracy? Ethics committees may say yes and no to these questions, but our field continues to evolve, and we too need to debate these and other questions in our community.</p> <h3 scrollto-destination=481193883 id="481193883" class="section-title js-splitscreen-section-title" >Conceptualization: Hypothesis Generator versus Hypothesis Validator</h3> <p class="chapter-para">Researchers can use carefully crafted prompts to find papers for their literature review, summarize these papers, and offer hypotheses that logically follow from that summary. In generating hypotheses, AI is becoming increasingly better at performing synthesizing and conceptual tasks previously considered to be defining traits of human scholars. When will GenAI outperform human conceptual creativity? We imagine that GenAI will first be used as a hypothesis validator, one that can effectively draw out evidence across published studies. However, as GenAI improves, how will it step into creative roles? We expect that doctoral programs will have courses dedicated to the effective use of AI in conceptual development and hypothesis generation. AI-generated or AI-elaborated hypotheses may be very helpful to researchers (and potentially minimize costs of unsuccessful projects) by steering us away from studies where hypotheses are either not novel or not logistically consistent.</p> <h3 scrollto-destination=481193885 id="481193885" class="section-title js-splitscreen-section-title" >Data Collection and Analysis: Data Collector and Analyst versus Data and Analysis Recommender</h3> <p class="chapter-para">During data collection, researchers can use GenAI to create data through silicon samples and synthetic datasets. For example, creating well-designed digital personas allows marketers to create synthetic, yet insightful, interview data. But should academic researchers do that? There has been a flurry of research investigating whether GenAI can replicate human findings, whether a small subsample of humans will do, or whether we can do original research using no humans at all. Researchers could also use GenAI-created content not only for pretests and pilot studies, assessing survey items and creating stimuli, but also in the actual studies; for example, GenAI (e.g., DALL-E, RUNWAY) excels at creating verbal, pictorial, and video stimuli. But, will the rational “right” answer AI produces reflect what actual consumers (with all our biases) would answer? These and other issues remain significant, but the accuracy and relevance of LLMs are rapidly improving. While LLMs seem reasonably proficient at generating themes in qualitative data (largely because they are language-based), these systems still lack advanced quantitative skills. We anticipate that soon the data collection and analysis phase of our research <em>could</em> be fully automated: from procedures and stimuli to data collection and analysis. But <em>should</em> it be?</p> <h3 scrollto-destination=481193887 id="481193887" class="section-title js-splitscreen-section-title" >Writing: Co-Writer versus Writing Assistant</h3> <p class="chapter-para">Should GenAI help co-write one’s own scholarly work or not is a very timely and pressing question. Even now, scholars can use a collection of careful prompts in ChatGPT to write reasonable sounding analytical narratives. The quality of this writing has improved dramatically in recent times and is expected to improve at even higher rates. Yet, this particular use of AI has created the strongest pushback among academics. For those whose superlative (and often hard-won) skills in writing have conferred success, using GenAI to draft a manuscript may feel like cheating. For those whose native language is not English, it may feel like empowering and equalizing. And in universities where resources are not abundant, significantly reducing the time that scholars spend writing up completed studies is a material saving. In many fields, and we argue that consumer research is one of them, the goal of most manuscripts is to offer straightforward, accurate, and purposeful language, not boundary-pushing creative or emotive prose. Using AI as a writing assistant, however, seems less daunting. AI can take existing passages and edit them to match the style of different journals and avoid errors of grammar and spelling effectively and efficiently. Journals are publishing policies on the use of GenAI in writing, and we imagine that many will revise these policies as norms change.</p> <h3 scrollto-destination=481193889 id="481193889" class="section-title js-splitscreen-section-title" >Publishing: Editor versus Editorial Manager</h3> <p class="chapter-para">As a journal, we must also consider how far GenAI will replace human editors, reviewers, and publishing managers. To what extent is the title of this editorial a future fact or a foolish fantasy? With several years of editorial experience in a quickly evolving landscape, we believe that several common editorial tasks could potentially be “outsourced” to AI. First, <em>JCR</em> receives large numbers of submissions. Many are not suitable for review. The job of determining and communicating desk-rejection decisions could be a trainable task for GenAI. A more complex, and arguably subjective, editorial task is determining whether the paper has the potential to make a strong theoretical or substantive contribution. Would an effectively trained AI make these decisions more consistently and equitably? The selection of AEs and reviewers could certainly be more efficiently done by AI; currently editors rely on their overtaxed memories to find the most appropriate reviewers while, at the same time, trying their best to avoid conflicts of interest. It is hard to imagine GenAI as reviewers. After all, humans have the nuance, instinct, and expertise to judge research. At the same time, however, human reviewers can be biased and territorial, forget the criteria of the journal or submission type, use faulty statistical heuristics, have pet peeves, and may respond harshly to unfamiliar ideas. If there is disclosure of the use of AI, will reviewers be influenced by such disclosures and make inferences about the work based on the use of AI? Conversely, will authors respond to AI reviewers more (or less) effectively than to human reviewers? Likewise, when reviews are completed, could GenAI produce the AE report? Could it synthesize the reviews, offer a realistic path toward publication, and make valid recommendations to the editor? Could it make the ultimate editorial decision? With learning, GenAI might be able to make increasingly accurate assessments of what the most likely editorial decision is. But editorships come with the call to chart a strategic course and sometimes that means going somewhere new. AI makes its decisions based on its training data—executing a novel strategic vision may not be its strong suit.</p> <h2 scrollto-destination=481193891 id="481193891" class="section-title js-splitscreen-section-title" >WHERE IT’S GOING: THE HUMAN ADOPTION OF AI IN RESEARCH</h2> <p class="chapter-para">Like any innovation, the tasks we assign to GenAI will likely begin with <em>supporting roles</em> like idea elaborator, hypothesis validator, data/analysis recommender, writing assistant, and editorial manager. But before very long, scholars will experiment with <em>defining roles</em> like idea creator, hypothesis generator, data collector, data analyst, writer, and editor. How we think about the use of GenAI in 21st century research will depend on many of the same factors studied in the 1960–70s when computers and other technologies were transforming 20th century scholarship from its 19th century forebears. Here are a few factors that we think are important to consider.</p> <h3 scrollto-destination=481193893 id="481193893" class="section-title js-splitscreen-section-title" >Lowered Costs</h3> <p class="chapter-para">Every A-level business journal publication costs the author’s university hundreds of thousands of USDs. The largest contributor to this damning calculation is the professor’s salary. One way to significantly shrink this number is by increasing productivity. If GenAI is fundamentally beneficial for tasks that take a lot of time when done by humans, an increase in productivity is possible. Imagining increasing productivity as we work now is a stress-inducing vision. But with a radical change in how the most time-intensive aspects of research (making sure our ideas are novel, honing a hypothesis, gathering data, and writing the manuscript) are conducted, perhaps the financial burden of research will ease.</p> <h3 scrollto-destination=481193895 id="481193895" class="section-title js-splitscreen-section-title" >Equity and Accessibility to Conduct Research</h3> <p class="chapter-para">As <em>JCR</em> works to embrace a more global community, we see that there are some privileges associated with being part of an English-speaking, North American, Tier 1 research university. The <em>JCR</em> Editorial Review Board disproportionately reflected this for many years and is still not where it could be. Using GenAI can bring researchers who lack some of these resources to a more equitable level. AI can free time, access the world’s publications, read top scholars’ online opinions, collect data, analyze data, and write a manuscript in excellent English and “the style of <em>JCR</em>.” AI-assisted review and publication of works could be fairer by avoiding in-group signals and reviewer idiosyncrasies. However, an AI-assisted review process has many issues. Poorly done research destined for rejection could be added to the training data, and authors (and participants) need privacy protection. Perhaps future authors will be asked to grant permission for an AI-assisted review process.</p> <h3 scrollto-destination=481193897 id="481193897" class="section-title js-splitscreen-section-title" >Researcher Satisfaction and Morale</h3> <p class="chapter-para">It is not clear how the increasing use of GenAI in consumer research will affect researcher satisfaction and morale. We think of our field as a fun one—full of interesting topics, methodologies, and personalities. Unlike other fields, our endeavors are not solo efforts to write a book, nor giant lab efforts where authorship is a cast of thousands. Some researchers may find AI to be the help that makes life/work balance possible. Some researchers may find that using AI robs the research process of a handmade authenticity or the social serendipity of collegial input. Productivity will take on a new signal and it is hard to tell now if it will increase or decrease scholarly prestige.</p> <h3 scrollto-destination=481193899 id="481193899" class="section-title js-splitscreen-section-title" >The Likelihood of Segmentation</h3> <p class="chapter-para">GenAI offers enough of an existential disruption to traditional processes that we expect different scholars will have different preferences about the best way to do “real research.” And, as consumer researchers, we all should expect groups of like-minded “consumers” (academics) to cluster together. Segmentation is not a problem to be solved, but a sustainable reality. Consider the current world of farming. Some consumers appreciate the low cost and accessibility of produce from factory farming that utilizes cutting-edge technology to make harvests cheap and reliable. Others happily pay higher prices for produce from traditional small-batch farms, where local harvests are smaller and more variable. For all consumers, it is important that both local heirloom tomatoes and low-priced hothouse tomatoes sit on the shelf together. For those privileged to afford it, the heirloom tomatoes will be preferred—as long as the small-batch handmade approach yields better quality. When that changes, norms may shift.</p><p class="chapter-para">Ultimately, the use of GenAI within research is not only up to individual researchers. New AI innovations will be impacted by the regulatory environment (human editors, policy makers, etc.), competition (other authors), and the development and capabilities of the technology. We have offered some possible scenarios, and we believe that the field should now have more conversations about AI. As has always been the case at <em>JCR</em>, we will rely on our community of scholars to suggest, consider, deconstruct, adapt, tinker, and test the ways that we can best use AI to improve our research.</p>  <div class="widget widget-ArticlePubStateInfo widget-instance-OUP_ArticlePubStateInfo"> </div> <div class="article-metadata-standalone-panel clearfix"></div> <div class="copyright copyright-statement">© The Author(s) 2024. Published by Oxford University Press on behalf of Journal of Consumer Research, Inc. All rights reserved. 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