Editorial: Donna L. Hoffman, C. Page Moreau, Stefan Stremersch, and Michel Wedel, “The Rise of New Technologies in Marketing: A Framework and Outlook”
As a scholarly field, marketing has a long tradition of studying the adoption of new technologies. This attention is certainly warranted, as studies consistently demonstrate that firms that invest heavily in new technology are more agile and enjoy a strong competitive advantage compared with firms that do not. However, what has received less attention in the literature is how new technologies give rise to innovations in marketing techniques, tools, and strategies themselves. In particular, there is a need for marketing scholars to develop theoretical paradigms of how marketers use technologies to develop a competitive advantage.
This special issue on “New Technologies in Marketing” presents cutting-edge scholarly research that recognizes the foundational role of new technologies in driving marketing theory and practice. The articles in the special issue study a broad range of new technologies, and we hope they will stimulate further research concerning new technologies in marketing and their application in practice.
Remi Daviet, Gideon Nave, and Jerry Wind, “Genetic Data: Potential Uses and Misuses in Marketing”
Exponential growth of the direct-to-consumer genetic testing (DTC-GT) industry has led to vast, privately-owned datasets containing individual-level genetic measures. Global companies, such as Spotify and AirBnB, have already partnered with DTC-GT companies and started incorporating genetic data into their business strategies. This study explores the impacts of this trend on the field of marketing. The authors build on past behavioral genetics research to incorporate genetic influences into existing consumer theory. They then survey potential uses of genetic data for marketing strategy and research.
Applications include reliance on genetics for identifying consumer needs, creative uses that develop consumers’ sense of community and personalization, use of genetically informed study designs to test causal relations, and better understanding consumer behavior by uncovering its biological underpinnings. Most importantly, the study evaluates ethical challenges related to autonomy, privacy, misinformation, and discrimination that are unique to the use of genetic data and are not sufficiently addressed by current regulations.
Neeraj Bharadwaj, Michel Ballings, Prasad A. Naik, Miller Moore, and Mustafa Murat Arat, “A New Livestream Retail Analytics Framework to Assess the Sales Impact of Emotional Displays”
Livestream retailing features hosts promoting and selling goods in real-time via screen-mediated sales presentations. This study showcases a new technology that analyzes livestream retail analytics data. Researchers investigated host facial expressions to evaluate whether emotional displays help or hurt sales.
The team analyzed about 100,000 sales pitches using machine learning to classify six emotional displays: happiness, sadness, surprise, anger, fear, and disgust. They found that negative emotions exhibit negative U-shaped effects on sales, as expected. Surprisingly, though, the positive emotion of happiness also exhibits a negative U-shaped effect on sales. This counterintuitive and provocative finding cautions salespeople to “sell with a straight face.”
The study highlights the importance of monitoring sales staff facial expressions. Marketers can use video feedback to train hosts on how to deliver effective sales pitches. Finally, marketers can use findings to refine bot marketing, as bots will increasingly be taught to express and reciprocate emotions in customer interactions.
Yong-Chin Tan, Sandeep R. Chandukala, and Srinivas K. Reddy, “Augmented Reality in Retail and Its Impact on Sales”
The rise of augmented reality (AR) technology presents marketers with promising opportunities to engage customers and transform their brand experience. While firms are keen to invest in AR, research documenting its real-world impact is sparse. This study outlines four broad uses of the technology in retail settings: to entertain customers, educate customers, help them evaluate product fit, and enhance the post-purchase consumption experience. The researchers focus specifically on the use of AR to facilitate product evaluation prior to purchase and empirically investigate its impact on sales in online retail.
The study finds that AR usage has a positive impact on product sales and certain products are more likely to benefit from the technology than others. In particular, the impact of AR is stronger for less popular brands and products. Additionally, customers who are new to the online channel or product category are more likely to purchase after using AR. These findings provide converging evidence that AR is most effective when product-related uncertainty is high.
Fred Miao, Irina V. Kozlenkova, Haizhong Wang, Tao Xie, and Robert W. Palmatier, “An Emerging Theory of Avatar Marketing”
Avatars are increasingly popular in contemporary marketing strategies, but their effectiveness for achieving performance outcomes (e.g., purchase likelihood) varies widely in practice. This study advances the discipline of avatar-based marketing in three ways.
First, it offers an overview of the various ways the term avatar has been defined to date, identifies, and critically evaluates key conceptual elements of these definitions, and proposes a definition on this basis. Researchers then present a typology of avatar design to isolate elements that managers can leverage to ensure avatars’ effectiveness for achieving specific goals.
Second, the study produces a 2 x 2 taxonomy comprised of avatars’ form realism and behavioral realism. The level of (mis)alignment between an avatar’s form and behavioral realism across different contingencies explains when an avatar is most effective.
Third, the study offers an integrative framework of avatar performance that provides a foundation for a contemporary theory of avatars to spur future research.
Chiara Longoni and Luca Cian, “Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The “Word-of-Machine” Effect”
More and more companies are leveraging technological advances in AI, machine learning, and natural language processing to provide relevant and in-the-moment recommendations to consumers. But when do consumers trust the “word of machine,” and when do they resist it?
This study documents a word-of-machine effect, the phenomenon by which utilitarian and hedonic trade-offs determine preference for, or resistance to, AI recommenders. The word-of-machine effect stems from a widespread belief that AI systems are more competent than humans in dispensing advice when functional and practical qualities (utilitarian) are desired and less competent when the desired qualities are experiential and sensory-based (hedonic). Consequently, the importance of utilitarian attributes determine preference for AI recommenders over human ones. Conversely, the importance of hedonic attributes determine resistance to AI recommenders over human ones, but this resistance can be reduced if AI works along with humans.
Julian R. K. Wichmann, Nico Wiegand, and Werner J. Reinartz, “The Platformization of Brands”
Digital platforms such as Amazon, Zalando, and JD occupy the interface to consumers by offering vast assortments of competing products and vendors. This puts considerable pressure on traditional product brands, which are facing increasingly fierce price competition and diminished differentiation on these platforms. However, the poison can also be the antidote, as this research shows: Brands like Nike, adidas, AISICS, and Bosch are operating their own brand flagship platforms that incorporate a host of functionalities and stakeholders.
The study shows that brands can assemble these flagship platforms from a set of five key building blocks based on overarching consumer goals. For example, Nike’s Run Club addresses the overarching consumer goal of living an active and healthy lifestyle by offering exclusive products, events, tracking and competitive features, and personal coaching. The authors argue that distinct consumer-platform relationships emerge that can range from transaction-oriented to highly self-relevant, committed, and durable. The study also highlights potential risks of operating a platform.
Cammy Crolic, Felipe Thomaz, Rhonda Hadi, and Andrew T. Stephen, “Blame the Bot: Anthropomorphism and Anger in Customer–Chatbot Interactions”
Chatbots are increasingly replacing human customer service agents on companies’ websites, social media pages, and messaging services. Designed to mimic humans, they often have human names (e.g., Amazon’s Alexa), humanlike appearances (e.g., avatars), and the capability to converse like humans. The common assumption is that humanlike qualities make chatbots more effective in customer service roles. However, this is not always the case.
The authors of this study analyze a large real-world dataset from an international telecommunications company and carry out four experiments. The studies find that when customers are angry, deploying humanlike chatbots can negatively impact customer satisfaction, overall firm evaluation, and subsequent purchase intentions. Why? Because humanlike chatbots raise unrealistic expectations of how helpful they will be.
When using chatbots, firms should attempt to gauge whether a customer is angry before entering the chat environment (e.g., via natural language processing) and then deploy the most effective, either humanlike or non-humanlike, chatbot.
Deepa Chandrasekaran, Gerard J. Tellis, and Gareth M. James, “Leapfrogging, Cannibalization, and Survival During Disruptive Technological Change: The Critical Role of Rate of Disengagement”
During times of technological change, industry incumbents and entrants face difficult choices. For incumbents, the critical dilemma is whether to cannibalize their own successful offerings and introduce the new technology, survive with the old offerings, or invest in both. The entrant’s dilemma is whether to target a niche to avoid incumbent reaction or target the mass market and incur incumbents’ wrath. The solution is to know to what extent the new technology cannibalizes the old one versus co-existing in equilibrium.
This study develops a generalized model of the diffusion of successive technologies, which allows for the rate of disengagement from the old technology to differ from the rate of adoption of the new. The model can provide important signals about disruption versus survival for entrants and incumbents. The key is estimating cannibalization versus co-existence by forecasting the evolution of four critical consumer segments: leapfroggers, switchers, opportunists, and dual users.
Special Issue Editors
University of Wisconsin–Madison
George Washington School of Business
Erasmus University and IESE Business School
University of Maryland
C. Page Moreau
C. Page Moreau is a Co-editor of the Journal of Marketing the John R. Nevin Professor of Marketing and Executive Director of the Center for Brand and Product Management at the Wisconsin School of Business, University of Wisconsin–Madison. Page’s research centers on innovation and creativity, with the goal of understanding the demand for and creation of new products. Specifically, she examines how and why consumers learn about, purchase, and create new products. Her work has appeared in the Journal of Marketing, the Journal of Consumer Research, the Journal of Marketing Research, the Journal of Consumer Psychology, and the Journal of Product Innovation Management and has been covered by NPR, Fast Company, Inc. Magazine, and Psychology Today. Her work has been honored with a Journal of Consumer Research Best Article Award, and she has been recognized as a Marketing Science Institute Young Scholar. Page has served as an Associate Editor at the Journal of Consumer Research and as a member of the editorial review boards at the Journal of Marketing, the Journal of Marketing Research, the Journal of Consumer Psychology, the Journal of Product Innovation Management, and IJRM. She has served two terms on the Board of the Association for Consumer Research. Page’s complete profile can be found here.
Donna L. Hoffman
Donna L. Hoffman is the Louis Rosenfeld Distinguished Professor of Marketing and Co-Director of the Center for the Connected Consumer at The George Washington School of Business in Washington, D.C. Donna’s current research is focused on consumer experience with AI devices, services and systems. Her work has been published in many of the field’s top journals including Journal of Marketing, Journal of Consumer Research, Journal of Marketing Research, Marketing Science, Management Science, Journal of Consumer Psychology, and Science. Donna’s work enjoys wide impact: she has over 26,000 Google Scholar citations and an H-index of 40 and has been awarded many of the field’s most prestigious research awards, notably the Sheth Foundation/Journal of Marketing Award for long-term contributions, the William O’Dell/Journal of Marketing Research Award for long-term research impact, the Robert B. Clarke Educator of the Year Award from the DMEF, the Stellner Distinguished Scholar Award from the University of Illinois, and others. A sought-after industry speaker who has work with start-ups to Fortune 500s for several decades, Donna has served as an Academic Trustee of the Marketing Science Institute and a member of the Procter & Gamble Digital Advisory Board. She previously co-founded and co-directed the Sloan Center for Internet Retailing at Vanderbilt University. She received her Ph.D. from the L.L. Thurstone Psychometric Laboratory at the UNC-Chapel Hill and was named a Distinguished Graduate Alumnus of UNC in 2002. Donna’s complete profile can be found here.
Stefan Stremersch
Stefan Stremersch is the Desiderius Erasmus Distinguished Chair of Economics and Chair of Marketing, both at Erasmus School of Economics, Erasmus University Rotterdam, the Netherlands and is Professor of Marketing at IESE Business School, Barcelona, Spain. His main research interests are innovation, marketing of technology and science, and pharmaceutical marketing. Stefan has won several awards, such as the Harold H. Maynard Best Paper Award for the Journal of Marketing (2002), the IJRM Best Paper Award (2012 & 2014), the JC Ruigrok Prize for the most productive young research in the Netherlands (2005; awarded only once every 4 years to an economists), the Rajan Varadarajan Early Career Award of the American Marketing Association (2008), the American Marketing Association’s Award for Global Marketing (2006). In 2015, Ghent University (Belgium) and the Francqui Foundation awarded him the honorary International Francqui Chair, selected across all sciences. In 2018, he was appointed EMAC Fellow. He is an Associate Editor at Journal of Marketing and serves on the Editorial Review Boards of Journal of Marketing Research and Marketing Science, as well as IJRM, for which he was the Editor-in-Chief from 2006 to 2009. Stefan’s complete profile can be found here.
Michel Wedel
Michel Wedel is the Pepsico Chaired Professor of Consumer Science at the Robert H. Smith School of Business, and a Distinguished University Professor, at the University of Maryland. He holds the Henri Theil Visiting Chair in Marketing and Econometric at the Econometric Institute of the Erasmus University. Michel has improved the understanding of consumer behavior and marketing decision making through the development and application of statistical and econometric methods. He is a pioneer and leading expert in the development of methods for response-based market segmentation, and for the analysis of eye movements to improve visual marketing. Michel has published three books, seven software packages, more than 175 peer reviewed articles, and over 20 book chapters. His work has been cited over 20,000 times and he has been ranked the most productive marketing researcher in the world. Michel’s work has received several best paper awards, and he received the Muller award for outstanding contributions to the social sciences from the Royal Dutch Academy of the Sciences, and both the Churchill and Parlin awards for lifetime contributions to marketing research from the American Marketing Association. He is a fellow of the American Statistical Association, the American Marketing Association, and the Institute for Operations research and Management Science. Michel’s complete profile can be found here.