報告時間：2024年11月9日（星期六）9:30-11:00
報告地點：管理學院新大樓925會議室
報 告 人：李喜彤 教授
工作單位：法國巴黎HEC商學院
舉辦單位：管理學院
報告簡介：
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.
報告人簡介：
李喜彤，法國巴黎HEC商學院教授，研究興趣包括社交媒體、眾籌、數字營銷、在線教育、算法及人工智能等領域，主要研究方法包括應用計量經濟分析、田野實驗和實驗室實驗等。他的研究成果已在許多頂級期刊上發表，包括Management Science、Information Systems Research、MIS Quarterly、Production and Operations Management等。
