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Buying Reputation as a Signal of Quality: Evidence from an Online Marketplace

Author

Listed:
  • Lingfang (Ivy) Li
  • Steven Tadelis
  • Xiaolan Zhou

Abstract

Reputation is critical to foster trust in online marketplaces, yet leaving feedback is a public good that can be under-provided unless buyers are rewarded for it. Signaling theory implies that only high quality sellers would reward buyers for truthful feedback. We explore this scope for signaling using Taobao's "reward-for-feedback" mechanism and find that items with rewards generate sales that are nearly 30% higher and are sold by higher quality sellers. The market design implication is that marketplaces can benefit from allowing sellers to use rewards to build reputations and signal their high quality in the process.

Suggested Citation

  • Lingfang (Ivy) Li & Steven Tadelis & Xiaolan Zhou, 2016. "Buying Reputation as a Signal of Quality: Evidence from an Online Marketplace," NBER Working Papers 22584, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:22584
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    References listed on IDEAS

    as
    1. Lingfang (Ivy) Li, 2010. "Reputation, Trust, and Rebates: How Online Auction Markets Can Improve Their Feedback Mechanisms," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 19(2), pages 303-331, June.
    2. Lafky, Jonathan, 2014. "Why do people rate? Theory and evidence on online ratings," Games and Economic Behavior, Elsevier, vol. 87(C), pages 554-570.
    3. Bar-Isaac, Heski & Tadelis, Steven, 2008. "Seller Reputation," Foundations and Trends(R) in Microeconomics, now publishers, vol. 4(4), pages 273-351, August.
    4. 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.
    5. 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.
    6. Kihlstrom, Richard E & Riordan, Michael H, 1984. "Advertising as a Signal," Journal of Political Economy, University of Chicago Press, vol. 92(3), pages 427-450, June.
    7. Kosuke Uetake & Ken ONISHI & Kei Kawai, 2013. "Signaling in Online Credit Markets," 2013 Meeting Papers 516, Society for Economic Dynamics.
    8. Matthew Backus & Tom Blake & Steven Tadelis, 2015. "Cheap Talk, Round Numbers, and the Economics of Negotiation," NBER Working Papers 21285, National Bureau of Economic Research, Inc.
    9. Nelson, Philip, 1974. "Advertising as Information," Journal of Political Economy, University of Chicago Press, vol. 82(4), pages 729-754, July/Aug..
    10. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    11. Fan, Ying & Ju, Jiandong & Xiao, Mo, 2016. "Reputation premium and reputation management: Evidence from the largest e-commerce platform in China," International Journal of Industrial Organization, Elsevier, vol. 46(C), pages 63-76.
    12. Chrysanthos Dellarocas & Charles A. Wood, 2008. "The Sound of Silence in Online Feedback: Estimating Trading Risks in the Presence of Reporting Bias," Management Science, INFORMS, vol. 54(3), pages 460-476, March.
    13. Daniel W. Elfenbein & Ray Fisman & Brian Mcmanus, 2012. "Charity as a Substitute for Reputation: Evidence from an Online Marketplace," Review of Economic Studies, Oxford University Press, vol. 79(4), pages 1441-1468.
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    Cited by:

    1. Ruediger Bachmann & Gabriel Ehrlich & Dimitrije Ruzic, 2017. "Firms and Collective Reputation: The Volkswagen Emission Scandal as a Case Study," CESifo Working Paper Series 6805, CESifo Group Munich.
    2. repec:nbr:nberch:14008 is not listed on IDEAS
    3. Paul Belleflamme & Martin Peitz, 2018. "Inside the Engine Room of Digital Platforms: Reviews, Ratings, and Recommendations," Working Papers halshs-01714549, HAL.
    4. Paul R. Milgrom & Steven Tadelis, 2018. "How Artificial Intelligence and Machine Learning Can Impact Market Design," NBER Chapters,in: The Economics of Artificial Intelligence: An Agenda National Bureau of Economic Research, Inc.

    More about this item

    JEL classification:

    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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