How Do Product Recommendations Help Consumers Search? Evidence from a Field Experiment

• 成果类型: Article
• 署名作者: Wan, Xiang (Shawn); Kumar, Anuj; Lic, Xitong
• 署名单位: Santa Clara University; State University System of Florida; University of Florida; Hautes Etudes Commerciales (HEC) Paris
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.4951
• 发表日期: 2024
• 页码: 5776-5794
• 关键词: product recommendations; benefits of recommendations; consumer search; horizontal fit; field experiment

摘要

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.