Smart Marketing
The Value of Genetic Data in Predicting Preferences: A Study of Food Taste
Remi Daviet & Gideon Nave
Journal of Marketing Research, forthcoming
Abstract:
The exponential expansion of consumer genetic testing has led to an accumulation of massive genomic datasets owned by governments and firms. The prospect of leveraging genetic data for enhancing consumer’s health, well-being and satisfaction, through improved personalization, segmentation, and targeting is promising. Nonetheless, this potential has not been studied empirically to date and it is unknown whether and when resources should be invested into incorporating genetic data into strategies and processes. We address this gap in a study of taste preferences, important drivers of food and beverages consumption. Using a large UK-based sample, we find that with sample sizes currently available, genetic data is expected to significantly improve prediction of taste preferences above traditionally used metrics such as demographics, behavioral variables, and even past consumption, especially for tastes that are uncommon in the local diet (e.g., spicy and sour) as they are less expressed behaviorally. We conclude that genetic data shows immense promise for prediction-based applications when other data sources are limited or uninformative. These findings could have significant implications for public health initiatives, potentially aiding development of personalized nutrition plans and dietary interventions.
AI can help people feel heard, but an AI label diminishes this impact
Yidan Yin, Nan Jia & Cheryl Wakslak
Proceedings of the National Academy of Sciences, 2 April 2024
Abstract:
People want to “feel heard” to perceive that they are understood, validated, and valued. Can AI serve the deeply human function of making others feel heard? Our research addresses two fundamental issues: Can AI generate responses that make human recipients feel heard, and how do human recipients react when they believe the response comes from AI? We conducted an experiment and a follow-up study to disentangle the effects of actual source of a message and the presumed source. We found that AI-generated messages made recipients feel more heard than human-generated messages and that AI was better at detecting emotions. However, recipients felt less heard when they realized that a message came from AI (vs. human). Finally, in a follow-up study where the responses were rated by third-party raters, we found that compared with humans, AI demonstrated superior discipline in offering emotional support, a crucial element in making individuals feel heard, while avoiding excessive practical suggestions, which may be less effective in achieving this goal. Our research underscores the potential and limitations of AI in meeting human psychological needs. These findings suggest that while AI demonstrates enhanced capabilities to provide emotional support, the devaluation of AI responses poses a key challenge for effectively leveraging AI’s capabilities.
The Spillover Effect of Fraudulent Reviews on Product Recommendations
Panagiotis (Panos) Adamopoulos
Management Science, forthcoming
Abstract:
As the prevalence of user-generated reviews has been growing, the pervasiveness of fraudulent reviews has been increasing as well. In an effort to alleviate the consequences of fraudulent reviews, platforms have been using machine-learning algorithms for fraudulent review detection. However, the current business practice of simply removing fraudulent reviews might not be sufficient, as even their temporary presence might forge spillover effects propagating through other shopping tools. In particular, we examine and discover the persistence of long-lasting significant adverse impact of fraudulent reviews through their propagation to recommender systems, even long after successfully detecting and removing all fraud incidents. We conduct additional analyses further examining the intensity and evolution of the spillover effect over time across different dimensions, such as the cost of the fraudulent activity, the effectiveness and timeliness of the detection algorithms, and the quality of products. The results illustrate the gravity of the identified effect and highlight that the current business practice of just removing the fraudulent reviews is insufficient in the presence of recommender systems, albeit potentially sufficient in their absence in the long run. Such findings are timely as they might inform current regulatory and policy discussions. Finally, we also design a simple remedy that can be easily introduced to collaborative filtering algorithms to debias the recommender systems and demonstrate its effectiveness in alleviating the impact of the identified spillover effect.
Does That Car Want to Give Me a Ride? Bio-Inspired Automotive Aesthetic Design
Bowei Chen et al.
University of Southern California Working Paper, October 2023
Abstract:
Product aesthetic design is pivotal in shaping consumer evaluations, especially in automotive industry where captivating designs often secure a market advantage. Consistent with prior research on knowledge transfer and schema congruity, we discover that consumers attribute faster acceleration and power to a sedan when primed to believe that its design is inspired by cheetah, the world's fastest land animal. Building upon this insight, we propose a deep-learning-based computational framework to morph aesthetic features of the product domain (e.g., sports sedans) with features from a different domain (e.g., sleek body curves of running cheetahs) in a mid-generational automotive facelift. We discover that cheetah-inspired new automobile facelifts are consistently preferred over the original looks, suggesting that our framework allows automakers to elevate automobiles' visual appeal cost-effectively without overhauling their fundamental body structures. Furthermore, our research suggests that higher degrees of cheetah morphing are preferred for premium sedans, extra sporty models, and by high-income consumers, compared to their counterparts. Lastly, we learn that certain colors (e.g., red) or print patterns (e.g., cheetah prints) further boost the visual appeal of cheetah curve-inspired automotive facelifts. Our framework serves as a proof-of-concept of using AI to morph between two distinct subject domains for product aesthetic design.
The Interactive Effect of Incentive Salience and Prosocial Motivation on Prosocial Behavior
Rin Yoon & Kaitlin Woolley
Psychological Science, forthcoming
Abstract:
Charities often use incentives to increase prosocial action. However, charities sometimes downplay these incentives in their messaging (pilot study), possibly to avoid demotivating donors. We challenge this strategy, examining whether increasing the salience of incentives for prosocial action can in fact motivate charitable behavior. Three controlled experiments (N = 2,203 adults) and a field study with an alumni-donation campaign (N = 22,468 adults) found that more (vs. less) salient incentives are more effective at increasing prosocial behavior when prosocial motivation is low (vs. high). This is because more (vs. less) salient incentives increase relative consideration of self-interest (vs. other-regarding) benefits, which is a stronger driver of behavior at low (vs. high) levels of prosocial motivation. By identifying that prosocial motivation moderates the effect of incentive salience on charitable behavior, and by detailing the underlying mechanism, we advance theory and practice on incentive salience, motivation, and charitable giving.
Digital Content Creation: An Analysis of the Impact of Recommendation Systems
Kun Qian & Sanjay Jain
Management Science, forthcoming
Abstract:
The success of digital content platforms, such as YouTube, relies on both the creativity of independent content creators and the efficiency of content distribution. By sharing advertising revenue with content creators, these platforms can motivate creators to exert greater effort. Most platforms use recommendation systems to deliver personalized content recommendations to each consumer. As creators’ revenues are contingent on their demand, the demand allocation criteria inherent in the recommendation system can influence their content creation behavior. In this paper, we investigate the influence of a platform’s recommendation system on revenue-sharing plans, content creation, profits, and welfare. Our results show that a platform could benefit by biasing recommendations, that is, recommending content that is not an ideal match to a consumer’s preference, to incentivize creators to produce better-quality content. We refer to this as a biased recommendation strategy. Interestingly, we find that such a biased recommendation strategy may lead to a win-win in which the platform, consumers, and content creators can benefit. Our study also shows that consumers may be worse off when they are more knowledgeable and less dependent on the recommendation system. In addition, the platform, consumers, and creators can benefit when the platform has more accurate information on consumer preferences.
The Power of a Star Rating: Differential Effects of Customer Rating Formats on Magnitude Perceptions and Consumer Reactions
Annika Abell, Carter Morgan & Marisabel Romero
Journal of Marketing Research, forthcoming
Abstract:
Average product ratings are displayed on many major websites, including online retailers, search engines, and social media sites. Research in both academia and industry has shown that consumers rely heavily on product ratings when making purchase decisions. Websites may show customer ratings in different shapes, colors, or formats. For example, some websites present their rating in a numerical format (e.g., 4.0/5) while others utilize an analog format (e.g., êêêêê). The present research investigates how a website’s rating presentation may influence magnitude perceptions of the rating, which subsequently affect choices and purchase intentions. We propose and find that when the average customer rating is at or above the midpoint (i.e., .5 - .9) of each rating level (i.e., 1 – 5 on a 5-pt. scale) the numerical (vs. analog) rating format is perceived to be lower in magnitude due to left-digit anchoring, leading to differences in choice likelihood, purchase intent, and ad click likelihood for products and services. Across nine experiments, including an eye tracking study, and a multiple-ad study without holdout, we show robust evidence for our proposed effect.