The Choice Overload Effect in Online Recommender Systems: Theoretical Framework and Field Experiment

41 Pages Posted: 9 Aug 2021 Last revised: 15 Jun 2022

See all articles by Xiaoyang Long

Xiaoyang Long

University of Wisconsin - Madison - School of Business

Jiankun Sun

Imperial College Business School

Hengchen Dai

University of California, Los Angeles (UCLA) - Anderson School of Management

Dennis Zhang

Washington University in St. Louis - John M. Olin Business School

Jianfeng Zhang

Alibaba Group

Yujie Chen

Alibaba Group

Haoyuan Hu

Alibaba Group

Binqiang Zhao

Alibaba Group

Date Written: June 12, 2022

Abstract

Firms are increasingly relying on recommender systems to help guide consumer choice. An important but under-studied question is how many products to offer in a recommendation set. In this work, via a field experiment involving 1.6 million consumers on an online retail platform, we causally demonstrate that consumers' likelihood of making a purchase first increases and then decreases as the number of products in a recommendation set grows. Importantly, as much as 64% of the decrease in purchase rate (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 illustrate via a two-stage behavioral choice model that these results are consistent with anticipated regret (as opposed to information overload) as the main mechanism of the choice overload effect. We further discuss alternative mechanisms and analyze heterogeneous treatment effects via both reduced-form regressions and a causal forest approach. Altogether, this work presents real-world experimental evidence for the choice overload effect in recommender systems, highlights the importance of consumer search behavior in driving this effect, and provide insights into when limiting the number of options in a recommender system may be particularly beneficial to online retailers.

Keywords: choice overload, search cost, anticipated regret, field experiment, recommender systems, retailing

Suggested Citation

Long, Xiaoyang and Sun, Jiankun and Dai, Hengchen and Zhang, Dennis and Zhang, Jianfeng and Chen, Yujie and Hu, Haoyuan and Zhao, Binqiang, The Choice Overload Effect in Online Recommender Systems: Theoretical Framework and Field Experiment (June 12, 2022). Available at SSRN: https://ssrn.com/abstract=3890056 or http://dx.doi.org/10.2139/ssrn.3890056

Xiaoyang Long

University of Wisconsin - Madison - School of Business ( email )

Madison, WI
United States

Jiankun Sun (Contact Author)

Imperial College Business School ( email )

Imperial College London
South Kensington Campus
London, SW7 2AZ
United Kingdom

Hengchen Dai

University of California, Los Angeles (UCLA) - Anderson School of Management ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States

Dennis Zhang

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

Jianfeng Zhang

Alibaba Group ( email )

Yujie Chen

Alibaba Group ( email )

Haoyuan Hu

Alibaba Group ( email )

Binqiang Zhao

Alibaba Group ( email )

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