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How Do Recommender Systems Lead to Consumer Purchases? A Causal Mediation Analysis of a Field Experiment

Author

Listed:
  • Xitong Li

    (HEC Paris - Ecole des Hautes Etudes Commerciales)

  • Jörn Grahl
  • Oliver Hinz

    (Goethe University Frankfurt = Goethe-Universität Frankfurt am Main)

Abstract

How do recommender systems induce consumers to buy? Extant research neglects to examine the causal paths through which the use of recommender systems leads to consumer purchases. In this study, we conduct a randomized controlled field experiment on the website of an online book retailer and explore the causal paths by employing the recently developed causal mediation approach. Not surprisingly, the results show that the presence of personalized recommendations increases consumers' propensity to buy by 12.4% and basket value by 1.7%. More importantly, we find that these positive economic effects are largely mediated through affecting the consumers' consideration sets. Specifically, the presence of personalized recommendations increases both the size of consumers' consideration set (breadth) and how they involve with each alternative in consideration (depth). It is the two changes that go on to increase consumers' propensity to buy and basket value. Furthermore, we find that the proportion of the total effects mediated through the breadth of consideration set is much larger and more significant than that mediated through the depth.

Suggested Citation

  • Xitong Li & Jörn Grahl & Oliver Hinz, 2021. "How Do Recommender Systems Lead to Consumer Purchases? A Causal Mediation Analysis of a Field Experiment," Working Papers hal-03869071, HAL.
  • Handle: RePEc:hal:wpaper:hal-03869071
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    Cited by:

    1. Shuang Zheng & Siliang (Jack) Tong & Hyeokkoo Eric Kwon & Gordon Burtch & Xianneng Li, 2025. "Frontiers: Recommending What to Search: Sales Volume and Consumption Diversity Effects of a Query Recommender System," Marketing Science, INFORMS, vol. 44(3), pages 516-524, May.
    2. Panagiotis (Panos) Adamopoulos, 2024. "The Spillover Effect of Fraudulent Reviews on Product Recommendations," Management Science, INFORMS, vol. 70(12), pages 8818-8832, December.
    3. Grahl, Jörn & Hinz, Oliver & Rothlauf, Franz & Abdel-Karim, Benjamin M. & Mihale-Wilson, Cristina, 2023. "How do likes influence revenue? A randomized controlled field experiment," Journal of Business Research, Elsevier, vol. 167(C).
    4. Anandasivam Gopal & Pei-yu Chen & Wonseok Oh & Sean Xin Xu & Suprateek Sarker, 2024. "On Crafting Effective Theoretical Contributions for Empirical Papers in Economics of Information Systems: Some Editorial Reflections," Information Systems Research, INFORMS, vol. 35(3), pages 917-935, September.
    5. Xiang (Shawn) Wan & Anuj Kumar & Xitong Li, 2024. "Retargeted vs. Generic Product Recommendations: When is it Valuable to Present Retargeted Recommendations?," Information Systems Research, INFORMS, vol. 35(3), pages 1403-1421, September.
    6. Cong Wang & Yansong Shi & Xunhua Guo & Guoqing Chen, 2025. "Probing Digital Footprints and Reaching for Inherent Preferences: A Cause-Disentanglement Approach to Personalized Recommendations," Information Systems Research, INFORMS, vol. 36(3), pages 1314-1332, September.
    7. Keran Zhao & Yili Hong & Tengteng Ma & Yingda Lu & Yuheng Hu, 2025. "Lost in the Crowd: How Group Size and Content Moderation Shape User Engagement in Live Streaming," Information Systems Research, INFORMS, vol. 36(4), pages 2076-2095, December.
    8. repec:osf:socarx:qga8j_v1 is not listed on IDEAS
    9. Cathy & Yang & Kevin Bauer & Xitong Li & Oliver Hinz, 2025. "My Advisor, Her AI and Me: Evidence from a Field Experiment on Human-AI Collaboration and Investment Decisions," Papers 2506.03707, arXiv.org.
    10. Xiaopeng Luo & Cheng He & Yu Jeffrey Hu & Xitong Li & Yuan Cheng, 2025. "The Impact of Mobile Data Cost on Consumer Price Sensitivity: A Study of a Hotel Booking App," Information Systems Research, INFORMS, vol. 36(3), pages 1912-1925, September.
    11. Markus Lill & Nastasia Gallitz & Lucas Stich & Martin Spann, 2026. "How Platform Endorsement Shapes Consumer Search and Choice in Online Retail," Rationality and Competition Discussion Paper Series 569, CRC TRR 190 Rationality and Competition.
    12. Chang, Woondeog & Park, Jungkun, 2024. "A comparative study on the effect of ChatGPT recommendation and AI recommender systems on the formation of a consideration set," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    13. Sujin Park & Ali Tafti & Galit Shmueli, 2024. "Transporting Causal Effects Across Populations Using Structural Causal Modeling: An Illustration to Work-from-Home Productivity," Information Systems Research, INFORMS, vol. 35(2), pages 686-705, June.
    14. Kevin Bauer & Andrej Gill, 2024. "Mirror, Mirror on the Wall: Algorithmic Assessments, Transparency, and Self-Fulfilling Prophecies," Information Systems Research, INFORMS, vol. 35(1), pages 226-248, March.
    15. Sai Chand Chintala & Jūra Liaukonytė & Nathan Yang, 2024. "Browsing the Aisles or Browsing the App? How Online Grocery Shopping is Changing What We Buy," Marketing Science, INFORMS, vol. 43(3), pages 506-522, May.
    16. Chenshuo Sun, 2025. "How Does Prepopulating Search Bars with Keywords Affect Online Consumer Behavior? A Field Experiment," Marketing Science, INFORMS, vol. 44(6), pages 1217-1231, November.
    17. Tianshu Sun & Zhe Yuan & Chunxiao Li & Kaifu Zhang & Jun Xu, 2024. "The Value of Personal Data in Internet Commerce: A High-Stakes Field Experiment on Data Regulation Policy," Management Science, INFORMS, vol. 70(4), pages 2645-2660, April.
    18. Xiang (Shawn) Wan & Anuj Kumar & Xitong Li, 2024. "How Do Product Recommendations Help Consumers Search? Evidence from a Field Experiment," Management Science, INFORMS, vol. 70(9), pages 5776-5794, September.
    19. Cathy (Liu) Yang & Kevin Bauer & Xitong Li & Oliver Hinz, 2026. "My Advisor, Her AI, and Me: Evidence from a Field Experiment on Human–AI Collaboration and Investment Decisions," Management Science, INFORMS, vol. 72(1), pages 242-264, January.
    20. Lanfei Shi & Jin Liu & Yongjun Li & Natasha Zhang Foutz, 2025. "Ephemeral State-Dependent Recommendation for Digital Content," Information Systems Research, INFORMS, vol. 36(4), pages 2344-2357, December.
    21. Zhe Yuan & AJ Yuan Chen & Yitong Wang & Tianshu Sun, 2025. "How Recommendation Affects Customer Search: A Field Experiment," Information Systems Research, INFORMS, vol. 36(1), pages 84-106, March.

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