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Designing Informative Rating Systems: Evidence from an Online Labor Market

Author

Listed:
  • Nikhil Garg

    (Department of Electrical Engineering, Stanford University, Stanford, California 94035)

  • Ramesh Johari

    (Department of Management Science and Engineering, Stanford University, Stanford, California 94035)

Abstract

Problem definition : Platforms critically rely on rating systems to learn the quality of market participants. In practice, however, ratings are often highly inflated and therefore, not very informative. In this paper, we first investigate whether the platform can obtain less inflated, more informative ratings by altering the meaning and relative importance of the levels in the rating system. Second, we seek a principled approach for the platform to make these choices in the design of the rating system. Academic/practical relevance : Platforms critically rely on rating systems to learn the quality of market participants, and so, ensuring these ratings are informative is of first-order importance. Methodology : We analyze the results of a randomized, controlled trial on an online labor market in which an additional question was added to the feedback form. Between treatment conditions, we vary the question phrasing and answer choices; in particular, the treatment conditions include several positive-skewed verbal rating scales with descriptive phrases or adjectives providing specific interpretation for each rating level. We then develop a model-based framework to compare and select among rating system designs and apply this framework to the data obtained from the online labor market test. Results : Our test reveals that current inflationary norms can be countered by reanchoring the meaning of the levels of the rating system. In particular, positive-skewed verbal rating scales yield substantially deflated rating distributions that are much more informative about seller quality. Further, we demonstrate that our model-based framework for scale design and optimization can identify the most informative rating system and substantially improve the quality of information obtained over baseline designs. Managerial implications : Our study illustrates that practical, informative rating systems can be designed and demonstrates how to compare and design them in a principled manner.

Suggested Citation

  • Nikhil Garg & Ramesh Johari, 2021. "Designing Informative Rating Systems: Evidence from an Online Labor Market," Manufacturing & Service Operations Management, INFORMS, vol. 23(3), pages 589-605, May.
  • Handle: RePEc:inm:ormsom:v:23:y:2021:i:3:p:589-605
    DOI: 10.1287/msom.2020.0921
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Yeon-Koo Che & Johannes Horner, 2015. "Optimal Design for Social Learning," Cowles Foundation Discussion Papers 2000, Cowles Foundation for Research in Economics, Yale University.
    4. Lingfang (Ivy) Li & Erte Xiao, 2014. "Money Talks: Rebate Mechanisms in Reputation System Design," Management Science, INFORMS, vol. 60(8), pages 2054-2072, August.
    5. Christina Aperjis & Ramesh Johari, 2010. "Optimal Windows for Aggregating Ratings in Electronic Marketplaces," Management Science, INFORMS, vol. 56(5), pages 864-880, May.
    6. Luís Cabral & Ali Hortaçsu, 2010. "The Dynamics Of Seller Reputation: Evidence From Ebay," Journal of Industrial Economics, Wiley Blackwell, vol. 58(1), pages 54-78, March.
    7. Steven Tadelis, 2016. "Reputation and Feedback Systems in Online Platform Markets," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 321-340, October.
    8. 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.
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    Cited by:

    1. Vahideh Manshadi & Scott Rodilitz, 2022. "Online Policies for Efficient Volunteer Crowdsourcing," Management Science, INFORMS, vol. 68(9), pages 6572-6590, September.
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    3. Jin Liu & Xingchen Xu & Xi Nan & Yongjun Li & Yong Tan, 2023. ""Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets," Papers 2308.05201, arXiv.org, revised Jun 2024.

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