IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v40y2021i6p1013-1029.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2021.1311
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.2021.1311?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. 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. Klein, Tobias & Lambertz, C. & Stahl, K.O., 2016. "Market transparency, adverse selection, and moral hazard," Other publications TiSEM e1b72b65-7277-4991-8880-4, Tilburg University, School of Economics and Management.
    10. Tommaso Bondi, 2019. "Alone, Together. Product Discovery Through Consumer Ratings," Working Papers 19-09, NET Institute.
    11. Lafky, Jonathan, 2014. "Why do people rate? Theory and evidence on online ratings," Games and Economic Behavior, Elsevier, vol. 87(C), pages 554-570.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    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. 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.
    2. 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).

    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. Yan Chen & Peter Cramton & John A. List & Axel Ockenfels, 2021. "Market Design, Human Behavior, and Management," Management Science, INFORMS, vol. 67(9), pages 5317-5348, September.
    2. Foster, Joshua, 2022. "How rating mechanisms shape user search, quality inference and engagement in online platforms: Experimental evidence," Journal of Business Research, Elsevier, vol. 142(C), pages 791-807.
    3. Xiang Hui & Tobias J. Klein & Konrad Stahl, 2021. "When and Why Do Buyers Rate in Online Markets?," CRC TR 224 Discussion Paper Series crctr224_2021_267v1, University of Bonn and University of Mannheim, Germany.
    4. 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.
    5. Arslan Aziz & Hui Li & Rahul Telang, 2023. "The Consequences of Rating Inflation on Platforms: Evidence from a Quasi-Experiment," Information Systems Research, INFORMS, vol. 34(2), pages 590-608, June.
    6. Lingfang (Ivy) Li & Steven Tadelis & Xiaolan Zhou, 2020. "Buying reputation as a signal of quality: Evidence from an online marketplace," RAND Journal of Economics, RAND Corporation, vol. 51(4), pages 965-988, December.
    7. Gesche, Tobias, 2018. "Reference Price Shifts and Customer Antagonism: Evidence from Reviews for Online Auctions," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181650, Verein für Socialpolitik / German Economic Association.
    8. Gary E. Bolton & David J. Kusterer & Johannes Mans, 2015. "Inflated reputations: Uncertainty, leniency & moral wiggle room in trader feedback systems," Cologne Graduate School Working Paper Series 06-04, Cologne Graduate School in Management, Economics and Social Sciences, revised 29 Jul 2016.
    9. Gary E. Bolton & David J. Kusterer & Johannes Mans, 2019. "Inflated Reputations: Uncertainty, Leniency, and Moral Wiggle Room in Trader Feedback Systems," Management Science, INFORMS, vol. 65(11), pages 5371-5391, November.
    10. 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.
    11. Johnen, Johannes & Ng, Robin, 2023. "Ratings and Reciprocity," LIDAM Discussion Papers CORE 2023006, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    12. Tobias Gesche, 2022. "Reference‐price shifts and customer antagonism: Evidence from reviews for online auctions," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(3), pages 558-578, August.
    13. Gary Bolton & Kevin Breuer & Ben Greiner & Axel Ockenfels, 2023. "Fixing feedback revision rules in online markets," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 32(2), pages 247-256, April.
    14. Davide Crapis & Bar Ifrach & Costis Maglaras & Marco Scarsini, 2017. "Monopoly Pricing in the Presence of Social Learning," Management Science, INFORMS, vol. 63(11), pages 3586-3608, November.
    15. Britta Hoyer & Dirk van Straaten, 2021. "Anonymity and Self-Expression in Online Rating Systems - An Experimental Analysis," Working Papers Dissertations 70, Paderborn University, Faculty of Business Administration and Economics.
    16. , 2015. "Quality Versus Quantity in Information Transmission: Theory and Experimental Evidence," Working Paper 539, Department of Economics, University of Pittsburgh, revised Jan 2015.
    17. 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.
    18. Keser, Claudia & Späth, Maximilian, 2021. "The value of bad ratings: An experiment on the impact of distortions in reputation systems," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 95(C).
    19. Wang Zhongmin, 2010. "Anonymity, Social Image, and the Competition for Volunteers: A Case Study of the Online Market for Reviews," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 10(1), pages 1-35, May.
    20. 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.

    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:40:y:2021:i:6:p:1013-1029. 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.