How can academic marketing research help a novice artist decide whether to price a painting at $80 or $800? Or a household to determine whether changing its consumption of meat, dairy, or snacks matters most for reducing its environmental footprint? Or the city official to evaluate whether e-scooters expand mobility or introduce unintended safety risks? Or the social media researcher to understand how ads perform, not as isolated posts but embedded in a feed? How can academic marketing research help the franchisor wondering whether dense outlet clusters require tighter control or greater autonomy? Each of these decision makers could read a research article. Yet in the moment of decision, what they often need is something more immediate: tools that use their inputs, context, knowledge, and behaviors to explore alternatives, see consequences, and act.
Editorial: “Research-Driven Apps: The Last Mile of Marketing Scholarship,” by Detelina Marinova, Rajdeep Grewal, Rik Pieters, Pradeep Chintagunta, and Shrihari Sridhar
This special issue is about the last mile in marketing scholarship. The last mile is a metaphor borrowed from logistics and telecommunications, where it refers to the final segment of physical delivery that is often the costliest and the most frequent to fail. In marketing scholarship, the last mile is the final segment of intellectual delivery: the move from a published finding to a situated decision made in its light. Marketing research is strongest in the upstream work of scholarly production. The field generates insight, explanation, and generalizable knowledge through experiments, econometrics, machine learning, text analysis, multimodal analytics, and causal inference. It struggles at the last mile, where stakeholders must translate research findings into context-specific action. Between the published finding and the situated decision lies a gap of interpretation, adaptation, and execution. As authors, we frequently stop short of providing the instruments that would help readers cross it. Our three recommendations in the closing pages of a published article might be insufficient. This special issue contributes to closing this gap by advancing Research-Driven Apps (RDAs): interactive, stakeholder-facing tools that convert scholarly insight into usable knowledge while preserving the theory, evidence, uncertainty, and boundary conditions that make the contribution scholarly.
“Closing the Knowledge Gap: Understanding and Reducing the Environmental Impact of Food Choices,” by Bart J. Bronnenberg, Trang Bùi, Barbara Deleersnyder, Lesley Haerkens, George Knox, Arjen van Lin, Max J. Pachali, Anna Paley, Robert W. Smith, and Samuel Stäbler
The global food system has a large impact on the environment. By converting household grocery purchases into environmental cost factors like greenhouse gas (GHG) emissions and land use, this study examines the sustainability of food purchases over a ten-year period using household panel data in two prosustainability markets (Germany and the Netherlands). The environmental intensity of households’ grocery baskets has not declined over time in these countries. Even though younger and more educated households are starting to shift their diets, the share of plant-based alternatives in food diets remains very low. The authors propose that one contributing factor is a lack of environmental knowledge, which they address by developing an app that gives personalized feedback on the emissions associated with consumers’ food purchases. The app also allows users to create sustainable grocery bundles aligned with their dietary preferences. Two experiments demonstrate that interacting with the app (1) raises subjective and objective environmental knowledge related to food, with much of this improvement persisting for over six months, and (2) reduces GHG emissions associated with stated food choices by up to 33%. These reductions are driven by different information formats within the app, including peer comparisons and detailed information about food-category emissions.
Check out the Food Impact Tracker App
“A Recipe for Creativity: An Ingredient-Embedding Approach,” by Sibel Sozuer, Oded Netzer, and Kriste Krstovski
An idea is more than a simple collection of words or ingredients that make up the idea. What makes an idea original or appealing is how these elements are combined in the context in which they appear. This research leverages representation learning methods, specifically word embeddings, to measure internal coherence among the components of a creative idea and capture the relationship between its coherence and idea evaluation or success. Using a large-scale online recipe dataset with over 57,000 recipes, as well as an ideation dataset of 1,057 ideas, the authors investigate how the fit among idea components relates to downstream measures such as popularity and evaluation. The results consistently show that internal coherence of an idea promotes its popularity, trial, and posttrial evaluation. Counter to prior research on creativity, which suggests that creativity is mostly associated with positive outcomes, the study finds that ideas with unique ingredients have lower popularity or trial, but higher ratings given trial in the context of food recipes. Based on these findings, the authors develop a generative recipe tool that suggests recipe improvements by adding, removing, or substituting ingredients (http://recipecreativity.com/).
Try the Generative Recipe Tool
“How E-Scooters Impact Shared Mobility and Consumer Safety,” by Ruichun Liu and Unnati Narang
New forms of shared micromobility services, such as e-scooters, are growing rapidly across cities. However, their impact beyond the retail and restaurant sectors is less understood in the marketing literature. The authors examine how the entry of e-scooters impacts other incumbent shared mobility services (i.e., ride-sharing and bike-sharing) and consumer safety (i.e., crimes) using data on the entry of e-scooters in parts of Chicago in 2019 and a generalized synthetic control approach. The results show that the entry of e-scooters increases the number of short rideshare trips by 15.72% but decreases the number of bikeshare trips by 7.62%. The effects are consistent with a category expansion mechanism for ride-sharing and a category cannibalization mechanism for bike-sharing. The authors also find that the entry of e-scooters increases the number of crimes (e.g., vehicle break-ins) by 17.94%, mostly due to street and vehicle crimes. The effects of e-scooters are heterogeneous by the age and racial composition of a neighborhood. Overall, e-scooters contribute about $8.1 million in ride-sharing revenues, but they also have an unintended negative environmental effect amounting to over 800 metric tons of carbon emissions per year. This research offers an app companion for stakeholders.
Check out the E-Scooter Research App
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E-Scooters Lead to Increased Rideshares—and Crime
“Natural Affect DEtection (NADE): Using Emojis to Infer Emotions from Text,” by Christian Hotz-Behofsits, Nils Wlömert, and Nadia Abou Nabout
Emotions are central to consumer communications, and extracting them from user-generated online content is crucial for marketers, given that such consumer opinions significantly shape brand perceptions, influence purchase decisions, and provide essential insights for marketing analytics. To leverage vast user-generated data, marketers and researchers require advanced text-to-emotion converters. However, existing tools for fine-grained emotion extraction face several limitations: Lexica are constrained by their dictionaries, machine learning models by human-annotated training data, and large language models by insufficient validation. As a result, marketing research still tends to rely on mere sentiment detection instead of extracting more nuanced emotions from text. This article introduces NADE (Natural Affect DEtection), a novel text-to-emoji-to-emotion converter that first “emojifies” language and then converts these emojis into intensity measures of well-established, theory-grounded emotions. This approach addresses the limitations of existing tools by leveraging the inherent emotional information in emojis. Using human raters and state-of-the-art converters as benchmarks, the authors establish the benefits of exploiting emojis, validate NADE, and demonstrate its use in several marketing applications using data from various social media platforms. Users can apply the proposed converter through an easy-to-use online app and programming packages for Python and R.
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“The Rise of Broadband and the Retail Landscape: Evidence from Consumer Packaged Goods,” by Uyen Tran
The proliferation of broadband internet has sparked concerns about the future of brick-and-mortar retail. This article explores consumer behavior in the U.S. consumer packaged goods sector during broadband’s proliferation from 2004 to 2019. Using household panel and retail scanner data covering more than 40,000 brands in 900 categories, the author analyzes nine household and retailer outcomes: brands purchased, trips taken, retailers visited, offline spending, any online spending, online spending share, prices, price dispersion, and demand elasticities. The author combines U.S. Census and Federal Communications Commission data to track the rollout of broadband. In contrast to established notions that broadband adoption would rapidly transform shopping behaviors in this period, the findings reveal a more nuanced and gradual pattern of change. Exploiting geographic variation in broadband growth, the author finds economically modest average effects with significant heterogeneity by age and household income. The analysis reveals generational differences: sharp declines in brick-and-mortar shopping among younger consumers counterbalanced by relative stability in larger older cohorts. In short, this article finds that broadband access drove a gradual evolution in consumer behavior, rather than a dramatic upheaval.
“Neither a Picasso nor a Leonardo da Vinci: An Examination of Novice Artwork Pricing with Multimodal Data,” by Sharmistha Sikdar, Ishita Chakraborty, and Nika Dogonadze
Novice art pricing is an understudied domain. Novice artists operate as microenterprises, making crucial price-setting decisions. Research shows that newcomers often risk overpricing or underpricing their work, and existing online tools offer basic, cost-based pricing advice. Using a three-study framework, the authors examine novice art pricing on Etsy, where artwork listings include structured data, images, and textual descriptions. They first analyze how these inputs relate to final selling prices using a hedonic regression on structured data, followed by a multimodal fusion deep learning model that integrates structured, visual, and textual features. The results show that features related to artist authenticity (e.g., certificates), customer service (e.g., shipping, returns, personalization), and art style (e.g., genre) are important price predictors. Thus, novice art sales on online platforms exhibit some features typical of mature art markets (e.g., authenticity and reputation) but emphasize customer-focused services. Finally, using a Cox proportional hazards model, the authors show that, while higher artist reputation is associated with faster sales, discounting correlates with longer time on market. These associations suggest the importance of price setting. From these insights, the authors develop a price recommender application that predicts both selling prices and time to sell, offering practical guidance for newcomer artists and online platforms.
Try the Etsy Art Price & Sales Timeline Predictor
“DICE: Advancing Social Media Research Through Digital In-Context Experiments,” by Hauke Roggenkamp, Johannes Boegershausen, and Christian Hildebrand
This article introduces Digital In-Context Experiments (DICE), an experimental paradigm that enables researchers to study entire social media feeds while tracking users’ granular behavioral data at the post level. Current research paradigms (vignette-based experiments, online platform studies, and observational studies) predominantly focus on isolated social media posts without examining how users consume content within the broader context of a feed. This isolation overlooks the competing attention between content (e.g., sponsored posts or ads) within a feed and contextual spillovers that occur when users scroll through continuous streams of content. DICE complements existing paradigms by presenting posts within scrollable feeds, more closely resembling how users experience content on social media. This allows researchers to systematically manipulate entire feed compositions while unobtrusively tracking participants’ scrolling behavior. To demonstrate the potential of DICE, the article presents two illustrative case studies that examine contextual spillovers and predict ad recall in environments where content competes for attention. The authors conclude with directions for future research and managerial perspectives derived from expert interviews with marketing professionals. An accompanying open-source app, available at https://dice-app.org, enables researchers to conveniently integrate the experimental paradigm into their preferred workflow.
“Surviving Economic Adversity: Governance of Franchise Clusters,” by Xu (Vivian) Zheng, Yajing Fan, and Mrinal Ghosh
Despite research on clustering strategies in franchised systems, little is known about its impact on outlet survival during times of economic adversity. In this research, the authors identify two governance mechanisms used by franchisors that vary at a cluster or outlet level—franchisee ownership fragmentation and franchisor on-site supervision—and develop a framework to suggest the efficacy of these mechanisms in mitigating the negative effect of economic adversity on the survival of franchised outlets operating in a cluster. They test this framework using a novel clustering algorithm and survival analysis on a uniquely constructed dataset of 8,677 outlets across 18 franchisors over 14 years. They find that when economic conditions deteriorate, franchisee ownership fragmentation, which reflects greater diversity in cluster composition and knowledge sources, reduces franchised outlet failure hazard within denser clusters. In contrast, franchisor on-site supervision, which denotes lower franchisee autonomy, intensifies outlet failure hazard in such clusters. This research offers actionable insights for franchisors on managing in-cluster outlets during economic adversity and introduces FranClusterer, a practical app-based tool for cluster identification and governance.
Check out the FranClusterer App
“How Closely Should You Follow a Trend? Atypicality and Engagement on Social Media,” by Marc Bravin, Melanie Clegg, Reto Hofstetter, Marc Pouly, and Jonah Berger
Following trends on social media has become increasingly popular. But what is the best way to do so? Should brands and other creators copy the trend as closely as possible, or should they put a more unique spin on it? To answer this question, the authors develop a multimodal, unsupervised video analytics tool (MUVID) to quantify the typicality of over 85,000 TikTok dance videos. Results indicate that more atypical videos (i.e., more differentiated from the trend) generate more engagement. Consistent with the notion that atypicality drives engagement, this relationship is amplified when atypicality is easier to observe (i.e., when audiences have seen more trend videos). Follow-up experiments, including a content-creator field experiment, manipulate atypicality and confirm its causal impact. The findings provide practical guidance on how to create more impactful content, shed light on effective trend-following, and offer a tool (available through an app) that researchers and practitioners can use to quantify typicality and analyze short videos more generally.
Try the the MUVID Video Analyzer App
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