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How do Platform Participants respond to an Unfair Rating? An Analysis of a Ride-Sharing Platform Using a Quasi-Experiment

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Listed:
  • Anuj Kapoor

    () (University of Utah)

  • Catherine Tucker

    () (MIT Sloan)

Abstract

Online rating systems can lead, on occasion, to reviews that are unfair or unrepresentative of the true quality provided. On the one hand, receiving an unfairly low rating once, might induce someone a platform supplier to exert more effort and receive a better rating the next time. On the other hand, it might dispirit suppliers and make them exert less effort. We use data from a ride-sharing platform in India where driver ratings were made particularly salient to the driver after each trip. Our data suggests that if a customer experiences a ride cancellation, they are more likely to unfairly blame the replacement driver. We use this as a exogenous source of unfair negative ratings for the driver. We show that drivers are more likely to respond negatively to a bad rating and receive subsequently bad ratings if they were blameless for the previous negative rating. This effect is larger in contexts where there is a higher potential for an emotional response and when there is a greater need for driver skill in the subsequent ride.

Suggested Citation

  • Anuj Kapoor & Catherine Tucker, 2017. "How do Platform Participants respond to an Unfair Rating? An Analysis of a Ride-Sharing Platform Using a Quasi-Experiment," Working Papers 17-19, NET Institute.
  • Handle: RePEc:net:wpaper:1719
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    File URL: http://www.netinst.org/Kapoor_17-19.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    The Sharing Economy; User Generated Content; Ratings;

    JEL classification:

    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising

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