The Choice Overload Effect in Online Recommender Systems

• 成果类型: Article
• 署名作者: Long, Xiaoyang; Sun, Jiankun; Dai, Hengchen; Zhang, Dennis; Zhang, Jianfeng; Chen, Yujie; Hu, Haoyuan; Zhao, Binqiang
• 署名单位: University of Wisconsin System; University of Wisconsin Madison; Imperial College London; University of California System; University of California Los Angeles; Washington University (WUSTL); Alibaba Group
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2022.0659
• 发表日期: 2025

摘要

Problem definition: Online retailing platforms are increasingly relying on personalized recommender systems to help guide consumer choice. An important but understudied question in such settings is how many products to include in a recommendation set. In this work, we study how the number of recommended products influences consumers' search and purchase behavior in an online personalized recommender system within a retargeting setting. Methodology/results: Via a field experiment involving 1.6 million consumers on an online retailing platform, we causally demonstrate that consumers' likelihood of purchasing any product from the recommendation set first increases then decreases as the number of recommended products increases. Importantly, as much as 64% of the decrease in purchase probability (i.e., the choice overload effect) can be attributed to a decrease in consumers' likelihood of starting a search (i.e., clicking on any recommended product). We discuss the possible behavioral mechanisms driving these results and analyze how these effects could be heterogeneous across different product categories, price ranges, and timing. Managerial implications: This work presents real-world experimental evidence for the choice overload effect in online retailing platforms, highlights the important role of consumer search behavior in driving this effect, and sheds light on when and how limiting the number of options in a recommender system may be beneficial to online retailers.