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The Choice Overload Effect in Online Recommender Systems

Xiaoyang Long1; J Sun2; Hengchen Dai3; Dennis Zhang4; Jianfeng Zhang5; Yujie Chen5; Haoyuan Hu5; Binqiang Zhao5

1 Wisconsin School of Business, University of Wisconsin-Madison, Madison, Wisconsin 53706; · 2 Imperial College Business School, Imperial College London, London SW7 2AZ, United Kingdom · 3 Anderson School of Management, University of California, Los Angeles, Los Angeles, California 90095 · 4 Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130 · 5 Alibaba Group, Hangzhou, Zhejiang Province 311121, China

Manufacturing and Service Operations Management 2025

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. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0659 .

DOI
10.1287/msom.2022.0659
Volume
27 (1)
Pages
249-268
Language
en
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BibTeX
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