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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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    2. 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.
    3. 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.
    4. repec:osf:socarx:qga8j_v1 is not listed on IDEAS
    5. 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.
    6. 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).
    7. 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.
    8. 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).
    9. 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.
    10. 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.

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