IDEAS home Printed from https://ideas.repec.org/p/hal/wpaper/hal-03869071.html
   My bibliography  Save this paper

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)

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
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Beatty, Sharon E. & Homer, Pamela & Kahle, Lynn R., 1988. "The involvement--commitment model: Theory and implications," Journal of Business Research, Elsevier, vol. 16(2), pages 149-167, March.
    2. Nedungadi, Prakash, 1990. "Recall and Consumer Consideration Sets: Influencing Choice without Altering Brand Evaluations," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(3), pages 263-276, December.
    3. Imai, Kosuke & Keele, Luke & Tingley, Dustin & Yamamoto, Teppei, 2011. "Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies," American Political Science Review, Cambridge University Press, vol. 105(4), pages 765-789, November.
    4. Daniel Fleder & Kartik Hosanagar, 2009. "Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity," Management Science, INFORMS, vol. 55(5), pages 697-712, May.
    5. Erik Brynjolfsson & Yu (Jeffrey) Hu & Duncan Simester, 2011. "Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales," Management Science, INFORMS, vol. 57(8), pages 1373-1386, August.
    6. Shapiro, Stewart & MacInnis, Deborah J & Heckler, Susan E, 1997. "The Effects of Incidental Ad Exposure on the Formation of Consideration Sets," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 24(1), pages 94-104, June.
    7. Anuj Kumar & Kartik Hosanagar, 2019. "Measuring the Value of Recommendation Links on Product Demand," Information Systems Research, INFORMS, vol. 30(3), pages 819-838, September.
    8. Hinz, Oliver & Eckert, Jochen & Skiera, Bernd, 2011. "Drivers of the Long Tail Phenomenon: An Empirical Analysis," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 56544, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    9. Irina Heimbach & Oliver Hinz, 2018. "The Impact of Sharing Mechanism Design on Content Sharing in Online Social Networks," Information Systems Research, INFORMS, vol. 29(3), pages 592-611, September.
    10. Raymond Hicks & Dustin Tingley, 2011. "Causal mediation analysis," Stata Journal, StataCorp LP, vol. 11(4), pages 605-619, December.
    11. Quentin Jones & Gilad Ravid & Sheizaf Rafaeli, 2004. "Information Overload and the Message Dynamics of Online Interaction Spaces: A Theoretical Model and Empirical Exploration," Information Systems Research, INFORMS, vol. 15(2), pages 194-210, June.
    12. Luke Keele & Dustin Tingley & Teppei Yamamoto, 2015. "Identifying Mechanisms Behind Policy Interventions Via Causal Mediation Analysis," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 34(4), pages 937-963, September.
    13. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    14. Dokyun Lee & Kartik Hosanagar, 2021. "How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages?," Management Science, INFORMS, vol. 67(1), pages 524-546, January.
    15. Prabuddha De & Yu (Jeffrey) Hu & Mohammad S. Rahman, 2010. "Technology Usage and Online Sales: An Empirical Study," Management Science, INFORMS, vol. 56(11), pages 1930-1945, November.
    16. Aishwarya Deep Shukla & Guodong (Gordon) Gao & Ritu Agarwal, 2021. "How Digital Word-of-Mouth Affects Consumer Decision Making: Evidence from Doctor Appointment Booking," Management Science, INFORMS, vol. 67(3), pages 1546-1568, March.
    17. Dokyun Lee & Kartik Hosanagar, 2019. "How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment," Service Science, INFORMS, vol. 30(1), pages 239-259, March.
    18. Daria Dzyabura & John R. Hauser, 2019. "Recommending Products When Consumers Learn Their Preference Weights," Marketing Science, INFORMS, vol. 38(3), pages 417-441, May.
    19. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
    20. Anuj Kumar & Yinliang (Ricky) Tan, 2015. "The Demand Effects of Joint Product Advertising in Online Videos," Management Science, INFORMS, vol. 61(8), pages 1921-1937, August.
    21. Gediminas Adomavicius & Jesse C. Bockstedt & Shawn P. Curley & Jingjing Zhangc, 2018. "Effects of Online Recommendations on Consumers’ Willingness to Pay," Information Systems Research, INFORMS, vol. 29(1), pages 84-102, March.
    22. Hauser, John R & Wernerfelt, Birger, 1990. "An Evaluation Cost Model of Consideration Sets," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(4), pages 393-408, March.
    23. Rex E. Pereira, 2001. "Influence of Query-Based Decision Aids on Consumer Decision Making in Electronic Commerce," Information Resources Management Journal (IRMJ), IGI Global, vol. 14(1), pages 31-48, January.
    24. Xinshu Zhao & John G. Lynch & Qimei Chen, 2010. "Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 37(2), pages 197-206, August.
    25. Kartik Hosanagar & Daniel Fleder & Dokyun Lee & Andreas Buja, 2014. "Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation," Management Science, INFORMS, vol. 60(4), pages 805-823, April.
    26. J. Yannis Bakos, 1997. "Reducing Buyer Search Costs: Implications for Electronic Marketplaces," Management Science, INFORMS, vol. 43(12), pages 1676-1692, December.
    27. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2019. "Modeling Consumer Footprints on Search Engines: An Interplay with Social Media," Management Science, INFORMS, vol. 65(3), pages 1363-1385, March.
    28. Gediminas Adomavicius & Jesse C. Bockstedt & Shawn P. Curley & Jingjing Zhang, 2013. "Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects," Information Systems Research, INFORMS, vol. 24(4), pages 956-975, December.
    29. Gerald Häubl & Valerie Trifts, 2000. "Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids," Marketing Science, INFORMS, vol. 19(1), pages 4-21, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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).
    6. 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.
    7. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yi, Sangyoon & Kim, Dongyeon & Ju, Jaehyeon, 2022. "Recommendation technologies and consumption diversity: An experimental study on product recommendations, consumer search, and sales diversity," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    2. Dokyun Lee & Kartik Hosanagar, 2021. "How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages?," Management Science, INFORMS, vol. 67(1), pages 524-546, January.
    3. Dongwon Lee & Anandasivam Gopal & Sung-Hyuk Park, 2020. "Different but Equal? A Field Experiment on the Impact of Recommendation Systems on Mobile and Personal Computer Channels in Retail," Information Systems Research, INFORMS, vol. 31(3), pages 892-912, September.
    4. 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.
    5. Tom Fangyun Tan & Serguei Netessine & Lorin Hitt, 2017. "Is Tom Cruise Threatened? An Empirical Study of the Impact of Product Variety on Demand Concentration," Information Systems Research, INFORMS, vol. 28(3), pages 643-660, September.
    6. Xuan Bi & Gediminas Adomavicius & William Li & Annie Qu, 2022. "Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1644-1660, May.
    7. Ruiqi Rich Zhu & Cheng He & Yu Jeffrey Hu, 2023. "The Effect of Product Recommendations on Online Investor Behaviors," Papers 2303.14263, arXiv.org, revised Nov 2023.
    8. Tobias Kretschmer & Christian Peukert, 2020. "Video Killed the Radio Star? Online Music Videos and Recorded Music Sales," Information Systems Research, INFORMS, vol. 31(3), pages 776-800, September.
    9. Park, YoungSoo & Sim, Jeongeun & Kim, Bosung, 2022. "Online retail operations with “Try-Before-You-Buy”," European Journal of Operational Research, Elsevier, vol. 299(3), pages 987-1002.
    10. Dokyun Lee & Kartik Hosanagar, 2019. "How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment," Service Science, INFORMS, vol. 30(1), pages 239-259, March.
    11. Molaie, Mir Majid & Lee, Wonjae, 2022. "Economic corollaries of personalized recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    12. Chen Liang & Zhan (Michael) Shi & T. S. Raghu, 2019. "The Spillover of Spotlight: Platform Recommendation in the Mobile App Market," Information Systems Research, INFORMS, vol. 30(4), pages 1296-1318, December.
    13. Konstantin Bauman & Alexander Tuzhilin, 2022. "Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes," Information Systems Research, INFORMS, vol. 33(1), pages 179-202, March.
    14. Miguel Godinho de Matos & Pedro Ferreira, 2020. "The Effect of Binge-Watching on the Subscription of Video on Demand: Results from Randomized Experiments," Information Systems Research, INFORMS, vol. 31(4), pages 1337-1360, December.
    15. Anuj Kumar & Kartik Hosanagar, 2019. "Measuring the Value of Recommendation Links on Product Demand," Information Systems Research, INFORMS, vol. 30(3), pages 819-838, September.
    16. Joan Calzada & Nestor Duch-Brown & Ricard Gil, 2021. "Do search engines increase concentration in media markets?," UB School of Economics Working Papers 2021/415, University of Barcelona School of Economics.
    17. Nathan M. Fong, 2017. "How Targeting Affects Customer Search: A Field Experiment," Management Science, INFORMS, vol. 63(7), pages 2353-2364, July.
    18. Amelia Fletcher & Peter L Ormosi & Rahul Savani, 2023. "Recommender Systems and Supplier Competition on Platforms," Journal of Competition Law and Economics, Oxford University Press, vol. 19(3), pages 397-426.
    19. Hsing Kenneth Cheng & D. Daniel Sokol & Xinyu Zang, 2024. "The rise of empirical online platform research in the new millennium," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 416-451, March.
    20. Jun Li & Serguei Netessine, 2020. "Higher Market Thickness Reduces Matching Rate in Online Platforms: Evidence from a Quasiexperiment," Management Science, INFORMS, vol. 66(1), pages 271-289, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:wpaper:hal-03869071. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.