首页|How Recommendation Affects Customer Search: A Field Experiment

How Recommendation Affects Customer Search: A Field Experiment

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Product recommendation and search are two technology-mediated channels through which e-commerce platforms can help customers find products. However, the relationship between the two channels and the underlying mechanisms and implications for platform design are not well understood. We leverage a randomized field experiment with 555,800 customers on a large e-commerce platform to investigate how product recommendation affects customer search. We vary the relevance of the recommendation that users experience upon arriving at the home page of the platform and find that a decrease in recommendation relevance leads to a significant increase in consumers' use of the search channel, indicating a (partial) substitution effect between the two at the aggregate level. We find substantial heterogeneity across product categories, propose a conceptual framework, and theorize how different states of customer demand-demand fulfillment and demand formation-may drive such heterogeneity. The results are aligned with our framework and provide evidence that both demand formation and fulfillment are at work in the channel interactions between recommendation and search. Specifically, when customers receive more product recommendations in a category, they search more in that category with generic query words, which indicates complementarity between recommendation and search. However, when customers receive fewer product recommendations in a category of interest, they compensate for this reduction by searching more in that category with long-tail query words, which indicates a substitution between recommendation and search. This experimental study is among the first to examine the causal relationship between the recommendation channel and search channel and offers implications for the design of e-commerce platforms.

recommendationsearchchannel substitutionchannel complementaritye-commercefield experiment recommendation systempersonalizationdata regulations

Zhe Yuan、AJ Yuan Chen、Yitong Wang、Tianshu Sun

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School of Economics, Center for Research of Private Economy, Future Regional Development Laboratory, and Academy of Financial Research, Zhejiang University, Hangzhou 310058, China

Marshall School of Business, University of Southern California, Los Angeles, California 90089||Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada

Kogod School of Business, American University, Washington, District of Columbia 20016

Cheung Kong Graduate School of Business, Beijing 100006, China

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2025

10.1287/isre.2022.0294

Information systems research

Information systems research

ISSN：1047-7047
年,卷(期)：2025.36(1)
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