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Reciprocity and Unveiling in Two-Sided Reputation Systems: Evidence from an Experiment on Airbnb

Author

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  • Andrey Fradkin

    (Marketing, Boston University, Cambridge, Massachusetts 02139; MIT Initiative on the Digital Economy, MIT Sloan School of Management, Cambridge, Massachusetts 02142)

  • Elena Grewal

    (Yale School of the Environment, New Haven, Connecticut 06511)

  • David Holtz

    (MIT Initiative on the Digital Economy, MIT Sloan School of Management, Cambridge, Massachusetts 02142; Management of Organizations and Entrepreneurship and Innovation, Haas School of Business, University of California, Berkeley, California 94720)

Abstract

Reputation systems are used by nearly every digital marketplace, but designs vary and the effects of these designs are not well understood. We use a large-scale experiment on Airbnb to study the causal effects of one particular design choice—the timing with which feedback by one user about another is revealed on the platform. Feedback was hidden until both parties submitted a review in the treatment group and was revealed immediately after submission in the control group. The treatment stimulated more reviewing in total. This is due to users’ curiosity about what their counterparty wrote and/or the desire to have feedback visible to other users. We also show that the treatment reduced retaliation and reciprocation in feedback and led to lower ratings as a result. The effects of the policy on feedback did not translate into reduced adverse selection on the platform.

Suggested Citation

  • Andrey Fradkin & Elena Grewal & David Holtz, 2021. "Reciprocity and Unveiling in Two-Sided Reputation Systems: Evidence from an Experiment on Airbnb," Marketing Science, INFORMS, vol. 40(6), pages 1013-1029, November.
    • Handle: RePEc:inm:ormksc:v:40:y:2021:i:6:p:1013-1029
    DOI: 10.1287/mksc.2021.1311
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    References listed on IDEAS

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    1. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    2. Xiang Hui & Maryam Saeedi & Neel Sundaresan, 2018. "Adverse Selection or Moral Hazard, An Empirical Study," Journal of Industrial Economics, Wiley Blackwell, vol. 66(3), pages 610-649, September.
    3. Peng Ding & Jiannan Lu, 2017. "Principal stratification analysis using principal scores," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 757-777, June.
    4. Livan, Giacomo & Caccioli, Fabio & Aste, Tomaso, 2017. "Excess reciprocity distorts reputation in online social networks," LSE Research Online Documents on Economics 83563, London School of Economics and Political Science, LSE Library.
    5. Davide Proserpio & Wendy Xu & Georgios Zervas, 2018. "You get what you give: theory and evidence of reciprocity in the sharing economy," Quantitative Marketing and Economics (QME), Springer, vol. 16(4), pages 371-407, December.
    6. Luís Cabral & Lingfang (Ivy) Li, 2015. "A Dollar for Your Thoughts: Feedback-Conditional Rebates on eBay," Management Science, INFORMS, vol. 61(9), pages 2052-2063, September.
    7. Paul Resnick & Christopher Avery & Richard Zeckhauser, 1999. "The Market for Evaluations," American Economic Review, American Economic Association, vol. 89(3), pages 564-584, June.
    8. Tobias J. Klein & Christian Lambertz & Konrad O. Stahl, 2016. "Market Transparency, Adverse Selection, and Moral Hazard," Journal of Political Economy, University of Chicago Press, vol. 124(6), pages 1677-1713.
    9. Tommaso Bondi, 2019. "Alone, Together. Product Discovery Through Consumer Ratings," Working Papers 19-09, NET Institute.
    10. Lafky, Jonathan, 2014. "Why do people rate? Theory and evidence on online ratings," Games and Economic Behavior, Elsevier, vol. 87(C), pages 554-570.
    11. Ruomeng Cui & Jun Li & Dennis J. Zhang, 2020. "Reducing Discrimination with Reviews in the Sharing Economy: Evidence from Field Experiments on Airbnb," Management Science, INFORMS, vol. 66(3), pages 1071-1094, March.
    12. Georgios Zervas & Davide Proserpio & John W. Byers, 2021. "A first look at online reputation on Airbnb, where every stay is above average," Marketing Letters, Springer, vol. 32(1), pages 1-16, March.
    13. Steven D. Levitt & John A. List, 2007. "What Do Laboratory Experiments Measuring Social Preferences Reveal About the Real World?," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 153-174, Spring.
    14. Chris Nosko & Steven Tadelis, 2015. "The Limits of Reputation in Platform Markets: An Empirical Analysis and Field Experiment," NBER Working Papers 20830, National Bureau of Economic Research, Inc.
    15. 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.
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    Cited by:

    1. Hui, Xiang & Klein, Tobias & Stahl, Konrad, 2022. "Learning from Online Ratings," CEPR Discussion Papers 17006, C.E.P.R. Discussion Papers.
    2. Christoph Carnehl & Maximilian Schaefer & André Stenzel & Kevin Ducbao Tran, 2022. "Value for Money and Selection: How Pricing Affects Airbnb Ratings," Bristol Economics Discussion Papers 22/771, School of Economics, University of Bristol, UK.
    3. Boto-García, David & Balado-Naves, Roberto & Mayor, Matías & Baños-Pino, José Francisco, 2023. "Consumers' demand for operational licencing: evidence from Airbnb in Paris," Annals of Tourism Research, Elsevier, vol. 100(C).
    4. Jonathan Lafky & Robin Ng, 2024. "Ratings with Heterogeneous Preferences," CRC TR 224 Discussion Paper Series crctr224_2024_594, University of Bonn and University of Mannheim, Germany.

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