The Choice Overload Effect in Online Recommender Systems

Xiaoyang Long (), Jiankun Sun (), Hengchen Dai (), Dennis Zhang (), Jianfeng Zhang (), Yujie Chen (), Haoyuan Hu () and Binqiang Zhao ()
Additional contact information
Xiaoyang Long: Wisconsin School of Business, University of Wisconsin–Madison, Madison, Wisconsin 53706
Jiankun Sun: Imperial College Business School, Imperial College London, London SW7 2AZ, United Kingdom
Hengchen Dai: Anderson School of Management, University of California, Los Angeles, Los Angeles, California 90095
Dennis Zhang: Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130
Jianfeng Zhang: Alibaba Group, Hangzhou, Zhejiang Province 311121, China
Yujie Chen: Alibaba Group, Hangzhou, Zhejiang Province 311121, China
Haoyuan Hu: Alibaba Group, Hangzhou, Zhejiang Province 311121, China
Binqiang Zhao: Alibaba Group, Hangzhou, Zhejiang Province 311121, China

Manufacturing & Service Operations Management, 2025, vol. 27, issue 1, 249-268

Abstract: 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.

Keywords: choice overload; retail operations; field experiment; platform operations; search cost (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/msom.2022.0659 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:27:y:2025:i:1:p:249-268

Access Statistics for this article

More articles in Manufacturing & Service Operations Management from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().