Time: November 9, 2024 9: 30 a.m.-11: 00 a.m.
Venue: Meeting Room 925, Engineering Management & Intelligent Manufacturing Research Center
Speaker: Xitong Li, HEC Paris, Professor
Organizer: School of Management, Hefei University of Technology
Abstract:
Product recommendations can benefit consumers’ online product search via multiple underlying mechanisms, such as showing products that offer them high value, facilitating navigation on the website, or exposing more product information. However, it is unclear ex ante which is the primary underlying mechanism that drives the benefits of product recommendations to consumers. We conducted a randomized field experiment to estimate the benefits of an item-based collaborative filtering (CF) recommendation system to consumers. We collect unique data on the affinity scores computed by an item-based CF algorithm to develop measures of a product’s net value and horizontal (taste) fit for consumers. Our results indicate that product recommendations help consumers search for higher-value products that are lower priced, fit their tastes better, or both. Besides that, we find that the ability to find higher-value products (rather than easy navigation or exposure to more product information) is the primary driver for consumers’ higher purchase probabilities under recommendations. We further find a higher benefit of recommendations in product categories with higher price dispersion and heterogeneity in consumers’ tastes, providing additional evidence for the lower price and better horizontal fit mechanisms. Finally, we find that when made available, consumers substitute their usage of other search tools on the website with product recommendations. Our findings have important implications for online retailers, policymakers, regulators, and item-based CF recommendation system design.
Biography:
Dr. Xitong Li is a professor of information systems at HEC Paris and a research fellow of Hi! PARIS, the joint research center between HEC Paris and Polytechnic Institute of Paris. His primary research interests are in the economics of information and AI technologies, including social media, FinTech, digital marketing, online education, human-AI/algorithms collaboration. His primary research methods include applied econometric analysis, field and laboratory experiments. Xitong’s research appears in leading international journals, such as Management Science, Information Systems Research, Management Information Systems Quarterly, Production and Operations Management, Journal of Management Information Systems, and various ACM/IEEE Transactions. Xitong’s research has been granted by ANR AAPG France (solo PI), equivalent to National Science Foundation (NSF) in the U.S., for 2018-2023. His research has also been granted by Hi! PARIS Research Fellowship for 2021-2025. Xitong currently serves as an Associate Editor for Information Systems Research, a top journal in the information systems field. He also served as a guest senior editor for Production and Operations Management, a top journal in the operations management field.
