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Aggregation of Consumer Ratings: An Application to Yelp.com

Author

Listed:
  • Weijia Dai
  • Ginger Z. Jin
  • Jungmin Lee
  • Michael Luca

Abstract

Because consumer reviews leverage the wisdom of the crowd, the way in which they are aggregated is a central decision faced by platforms. We explore this "rating aggregation problem" and offer a structural approach to solving it, allowing for (1) reviewers to vary in stringency and accuracy, (2) reviewers to be influenced by existing reviews, and (3) product quality to change over time. Applying this to restaurant reviews from Yelp.com, we construct an adjusted average rating and show that even a simple algorithm can lead to large information efficiency gains relative to the arithmetic average.

Suggested Citation

  • Weijia Dai & Ginger Z. Jin & Jungmin Lee & Michael Luca, 2012. "Aggregation of Consumer Ratings: An Application to Yelp.com," NBER Working Papers 18567, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:18567
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    References listed on IDEAS

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    1. Dan Ariely & Anat Bracha & Stephan Meier, 2009. "Doing Good or Doing Well? Image Motivation and Monetary Incentives in Behaving Prosocially," American Economic Review, American Economic Association, vol. 99(1), pages 544-555, March.
    2. Nolan Miller & Paul Resnick & Richard Zeckhauser, 2005. "Eliciting Informative Feedback: The Peer-Prediction Method," Management Science, INFORMS, vol. 51(9), pages 1359-1373, September.
    3. Chrysanthos Dellarocas, 2006. "Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms," Management Science, INFORMS, vol. 52(10), pages 1577-1593, October.
    4. Yan Chen & F. Maxwell Harper & Joseph Konstan & Sherry Xin Li, 2010. "Social Comparisons and Contributions to Online Communities: A Field Experiment on MovieLens," American Economic Review, American Economic Association, vol. 100(4), pages 1358-1398, September.
    5. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2007. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Working Papers 07-36, NET Institute.
    6. David Godes & Dina Mayzlin, 2009. "Firm-Created Word-of-Mouth Communication: Evidence from a Field Test," Marketing Science, INFORMS, vol. 28(4), pages 721-739, 07-08.
    7. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    8. Pope, Devin G., 2009. "Reacting to rankings: Evidence from "America's Best Hospitals"," Journal of Health Economics, Elsevier, vol. 28(6), pages 1154-1165, December.
    9. Glazer, Jacob & McGuire, Thomas G. & Cao, Zhun & Zaslavsky, Alan, 2008. "Using global ratings of health plans to improve the quality of health care," Journal of Health Economics, Elsevier, vol. 27(5), pages 1182-1195, September.
    10. 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.
    11. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, Oxford University Press, vol. 107(3), pages 797-817.
    12. Jonathan E. Alevy & Michael S. Haigh & John A. List, 2007. "Information Cascades: Evidence from a Field Experiment with Financial Market Professionals," Journal of Finance, American Finance Association, vol. 62(1), pages 151-180, February.
    13. Dina Mayzlin, 2006. "Promotional Chat on the Internet," Marketing Science, INFORMS, vol. 25(2), pages 155-163, 03-04.
    14. Michael Luca & Jonathan Smith, 2013. "Salience in Quality Disclosure: Evidence from the U.S. News College Rankings," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 22(1), pages 58-77, March.
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    Citations

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    Cited by:

    1. Michael Luca, 2016. "Designing Online Marketplaces: Trust and Reputation Mechanisms," NBER Chapters,in: Innovation Policy and the Economy, Volume 17, pages 77-93 National Bureau of Economic Research, Inc.
    2. Liad Wagman & Vincent Conitzer, 2014. "False-name-proof voting with costs over two alternatives," International Journal of Game Theory, Springer;Game Theory Society, vol. 43(3), pages 599-618, August.
    3. Michael Luca, 2016. "Designing Online Marketplaces: Trust and Reputation Mechanisms," NBER Working Papers 22616, National Bureau of Economic Research, Inc.
    4. Michael Luca & Georgios Zervas, 2013. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Harvard Business School Working Papers 14-006, Harvard Business School, revised May 2015.
    5. Michael Luca, 2017. "Designing Online Marketplaces: Trust and Reputation Mechanisms," Innovation Policy and the Economy, University of Chicago Press, vol. 17(1), pages 77-93.
    6. Michael Luca, 2016. "Designing Online Marketplaces: Trust and Reputation Mechanisms," Harvard Business School Working Papers 17-017, Harvard Business School.
    7. Benjamin Edelman & Micahel Luca, 2014. "Digital Discrimination: The Case of Airbnb.com," Harvard Business School Working Papers 14-054, Harvard Business School.
    8. Amedeo Piolatto, 2015. "Online booking and information: competition and welfare consequences of review aggregators," Working Papers 2015/11, Institut d'Economia de Barcelona (IEB).

    More about this item

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • 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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