IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v44y2025i6p1258-1277.html

Alone, Together: A Model of Social (Mis)Learning from Consumer Reviews

Author

Listed:
  • Tommaso Bondi

    (Cornell Tech, New York, New York 10044; and SC Johnson School of Management, Cornell University, Ithaca, New York 14853)

Abstract

We develop a dynamic model of naïve social learning from consumer reviews. In our model, consumers decide if and what to buy based on both the products’ expected quality and their idiosyncratic taste for them. Products’ qualities are initially unknown and are (mis)learned from reviews. At the heart of the model lies a dynamic feedback loop between reviews, beliefs, and choices: period t reviews influence t + 1 consumers’ beliefs and, thus, choices; these determine the average of t + 1 reviews, which, in turn, influences t + 2 beliefs, choices, and reviews. We show that, in the long run ( t = ∞ ), reviews are systematically biased, leading some consumers astray. In particular, in both monopoly and duopoly, reviews relatively advantage lower quality and more polarizing products because these products induce stronger taste-based consumer self-selection. Thus, in stark contrast with the winner-takes-all dynamics of classic observational learning models in which consumers learn from the choices of their predecessors, social learning from opinions generates excessive choice fragmentation. Our findings have implications for interpreting the variance and number of reviews, pricing in the presence of reviews, and the short- and long-term effectiveness of fake reviews.

Suggested Citation

  • Tommaso Bondi, 2025. "Alone, Together: A Model of Social (Mis)Learning from Consumer Reviews," Marketing Science, INFORMS, vol. 44(6), pages 1258-1277, November.
  • Handle: RePEc:inm:ormksc:v:44:y:2025:i:6:p:1258-1277
    DOI: 10.1287/mksc.2023.0053
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2023.0053
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.2023.0053?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Imke Reimers & Joel Waldfogel, 2021. "Digitization and Pre-purchase Information: The Causal and Welfare Impacts of Reviews and Crowd Ratings," American Economic Review, American Economic Association, vol. 111(6), pages 1944-1971, June.
    2. Jacobsen, Grant D., 2015. "Consumers, experts, and online product evaluations: Evidence from the brewing industry," Journal of Public Economics, Elsevier, vol. 126(C), pages 114-123.
    3. Li Chen & Yiangos Papanastasiou, 2021. "Seeding the Herd: Pricing and Welfare Effects of Social Learning Manipulation," Management Science, INFORMS, vol. 67(11), pages 6734-6750, November.
    4. Glenn Ellison & Drew Fudenberg, 1995. "Word-of-Mouth Communication and Social Learning," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 93-125.
    5. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    6. Monic Sun, 2012. "How Does the Variance of Product Ratings Matter?," Management Science, INFORMS, vol. 58(4), pages 696-707, April.
    7. Itay P. Fainmesser & Dominique Olié Lauga & Elie Ofek, 2021. "Ratings, Reviews, and the Marketing of New Products," Management Science, INFORMS, vol. 67(11), pages 7023-7045, November.
    8. Nikhil Vellodi, 2018. "Ratings Design and Barriers to Entry," Working Papers 18-13, NET Institute.
    9. Catherine Tucker & Juanjuan Zhang, 2011. "How Does Popularity Information Affect Choices? A Field Experiment," Management Science, INFORMS, vol. 57(5), pages 828-842, May.
    10. Ilan Kremer & Yishay Mansour & Motty Perry, 2014. "Implementing the "Wisdom of the Crowd"," Journal of Political Economy, University of Chicago Press, vol. 122(5), pages 988-1012.
    11. Catherine Tucker & Juanjuan Zhang & Ting Zhu, 2013. "Days on market and home sales," RAND Journal of Economics, RAND Corporation, vol. 44(2), pages 337-360, June.
    12. Song Lin & Juanjuan Zhang & John R. Hauser, 2015. "Learning from Experience, Simply," Marketing Science, INFORMS, vol. 34(1), pages 1-19, January.
    13. Justin P. Johnson & David P. Myatt, 2006. "On the Simple Economics of Advertising, Marketing, and Product Design," American Economic Review, American Economic Association, vol. 96(3), pages 756-784, June.
    14. Heski Bar-Isaac & Guillermo Caruana & Vicente Cunat, 2012. "Search, Design, and Market Structure," American Economic Review, American Economic Association, vol. 102(2), pages 1140-1160, April.
    15. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Decision Analysis, INFORMS, vol. 40(3), pages 481-507, May-June.
    16. Juanjuan Zhang, 2010. "The Sound of Silence: Observational Learning in the U.S. Kidney Market," Marketing Science, INFORMS, vol. 29(2), pages 315-335, 03-04.
    17. Ramon Caminal & Xavier Vives, 1996. "Why Market Shares Matter: An Information-Based Theory," RAND Journal of Economics, The RAND Corporation, vol. 27(2), pages 221-239, Summer.
    18. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    19. Nikhil Vellodi, 2018. "Ratings Design and Barriers to Entry," Working Papers 18-13, NET Institute.
    20. Dina Mayzlin & Yaniv Dover & Judith Chevalier, 2014. "Promotional Reviews: An Empirical Investigation of Online Review Manipulation," American Economic Review, American Economic Association, vol. 104(8), pages 2421-2455, August.
    21. Sherry He & Brett Hollenbeck & Davide Proserpio, 2022. "The Market for Fake Reviews," Marketing Science, INFORMS, vol. 41(5), pages 896-921, September.
    22. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    23. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    24. Michael Luca & Georgios Zervas, 2016. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Management Science, INFORMS, vol. 62(12), pages 3412-3427, December.
    25. David Godes & José C. Silva, 2012. "Sequential and Temporal Dynamics of Online Opinion," Marketing Science, INFORMS, vol. 31(3), pages 448-473, 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. Tommaso Bondi & Michelangelo Rossi, 2026. "Online Reviews: Information Content, Drivers, and Platform Design," CESifo Working Paper Series 12427, CESifo.
    2. Xiao, Lei & Dong, Ruixiao, 2026. "When should sellers offer rebates for consumer reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 90(C).
    3. Mehrzad Khosravi & Max Kleiman-Weiner & Hema Yoganarasimhan, 2026. "Boundedly Rational Meta-Learning in Sequential Consumer Choice," Papers 2605.16532, arXiv.org.

    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. Tommaso Bondi, 2019. "Alone, Together. Product Discovery Through Consumer Ratings," Working Papers 19-09, NET Institute.
    2. Tommaso Bondi & Michelangelo Rossi & Ryan L. Stevens, 2025. "The Good, the Bad and the Picky: Consumer Heterogeneity and the Reversal of Product Ratings," Management Science, INFORMS, vol. 71(8), pages 7200-7222, August.
    3. Pocchiari, Martina & Proserpio, Davide & Dover, Yaniv, 2025. "Online reviews: A literature review and roadmap for future research," International Journal of Research in Marketing, Elsevier, vol. 42(2), pages 275-297.
    4. Luis Aguiar, 2026. "Bad Apples on Rotten Tomatoes: Critics, Crowds, and Gender Bias in Product Ratings," Marketing Science, INFORMS, vol. 45(1), pages 63-79, January.
    5. Luis Aguiar, 2024. "Bad Apples on Rotten Tomatoes: Critics, Crowds, and Gender Bias in Product Ratings," CESifo Working Paper Series 11422, CESifo.
    6. Xiang Hui & Zekun Liu & Weiqing Zhang, 2023. "From High Bar to Uneven Bars: The Impact of Information Granularity in Quality Certification," Management Science, INFORMS, vol. 69(10), pages 6109-6127, October.
    7. Jin Huang, 2017. "To Glance or to Peruse: Observational and Active Learning from Peer Consumers," Working Papers wp2018_1716, CEMFI.
    8. Jin Huang, 2017. "To Glance or to Peruse: Observational and Active Learning from Peer Consumers," Working Papers wp2017_1716, CEMFI.
    9. Weijia (Daisy) Dai & Ginger Jin & Jungmin Lee & Michael Luca, 2018. "Aggregation of consumer ratings: an application to Yelp.com," Quantitative Marketing and Economics (QME), Springer, vol. 16(3), pages 289-339, September.
    10. Chen Jin & Luyi Yang & Kartik Hosanagar, 2023. "To Brush or Not to Brush: Product Rankings, Consumer Search, and Fake Orders," Information Systems Research, INFORMS, vol. 34(2), pages 532-552, June.
    11. Hui, Xiang & Klein, Tobias & Stahl, Konrad, 2022. "Learning from Online Ratings," CEPR Discussion Papers 17006, Centre for Economic Policy Research.
    12. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    13. Dominik Gutt & Jürgen Neumann & Steffen Zimmermann & Dennis Kundisch & Jianqing Chen, 2018. "Design of Review Systems - A Strategic Instrument to shape Online Review Behavior and Economic Outcomes," Working Papers Dissertations 42, Paderborn University, Faculty of Business Administration and Economics.
    14. Young Joon Park & Jaewoo Joo & Charin Polpanumas & Yeujun Yoon, 2021. "“Worse Than What I Read?” The External Effect of Review Ratings on the Online Review Generation Process: An Empirical Analysis of Multiple Product Categories Using Amazon.com Review Data," Sustainability, MDPI, vol. 13(19), pages 1-22, September.
    15. Mina Ameri & Elisabeth Honka & Ying Xie, 2019. "Word of Mouth, Observed Adoptions, and Anime-Watching Decisions: The Role of the Personal vs. the Community Network," Marketing Science, INFORMS, vol. 38(4), pages 567-583, July.
    16. Sungsik Park & Woochoel Shin & Jinhong Xie, 2023. "Disclosure in Incentivized Reviews: Does It Protect Consumers?," Management Science, INFORMS, vol. 69(11), pages 7009-7021, November.
    17. Ni Huang & Tianshu Sun & Peiyu Chen & Joseph M. Golden, 2019. "Word-of-Mouth System Implementation and Customer Conversion: A Randomized Field Experiment," Information Systems Research, INFORMS, vol. 30(3), pages 805-818, September.
    18. Andrey Fradkin & David Holtz, 2023. "Do Incentives to Review Help the Market? Evidence from a Field Experiment on Airbnb," Marketing Science, INFORMS, vol. 42(5), pages 853-865, September.
    19. Aleksei Smirnov & Egor Starkov, 2022. "Bad News Turned Good: Reversal under Censorship," American Economic Journal: Microeconomics, American Economic Association, vol. 14(2), pages 506-560, May.
    20. Le Wang & Xin (Robert) Luo & Liangfei Qiu & Feng Xu & Xueying Cui, 2025. "Win by Hook or Crook? Self-Injecting Favorable Online Reviews to Fight Adjacent Rivals," Information Systems Research, INFORMS, vol. 36(3), pages 1333-1353, September.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:inm:ormksc:v:44:y:2025:i:6:p:1258-1277. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.