IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v71y2025i8p7200-7222.html
   My bibliography  Save this article

The Good, the Bad and the Picky: Consumer Heterogeneity and the Reversal of Product Ratings

Author

Listed:
  • Tommaso Bondi

    (Cornell Tech, Emma and Georgina Bloomberg Center, New York, New York 10044; and Johnson Graduate School of Management, Ithaca, New York 14853; and Center for Economic Studies and Ifo Institute for Economic Research, 81679 Munich, Germany)

  • Michelangelo Rossi

    (Center for Economic Studies and Ifo Institute for Economic Research, 81679 Munich, Germany; and Télécom Paris, Institut Polytechnique de Paris, 91120 Palaiseau, France)

  • Ryan L. Stevens

    (Ramp Corp., New York, New York 10010)

Abstract

We study the impact of consumer heterogeneity on online ratings. Consumers differ in their experience, which can affect both their choices and ratings. Thus, biases in average ratings can arise when the opinions of experienced and novice users are aggregated. We first build a two-period model to characterize the biases’ drivers and consequences. We test our theory combining data from IMDb and MovieLens, two well-known movie ratings platforms. We proxy users’ experience with the total number of ratings posted on the platforms. First, using external measures of quality, such as the Academy awards and nominations, we show that, on both platforms, experienced users, on average, rate movies of higher quality compared with novices. Moreover, they post more stringent ratings than novices for more than 98% of movies. Combined, these imply a compression in aggregate ratings, and thus a bias against high quality movies. We then propose a simple, fixed-point algorithm to debias ratings. Our debiased ratings demonstrate the presence of ranking reversals for more than 8% of comparisons in our sample. As a result, our debiased ratings better correlate with external measures of quality.

Suggested Citation

  • Tommaso Bondi & Michelangelo Rossi & Ryan L. Stevens, 2025. "The Good, the Bad and the Picky: Consumer Heterogeneity and the Reversal of Product Ratings," Management Science, INFORMS, vol. 71(8), pages 7200-7222, August.
    • Handle: RePEc:inm:ormnsc:v:71:y:2025:i:8:p:7200-7222
    DOI: 10.1287/mnsc.2022.03281
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.2022.03281
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.2022.03281?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
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:ormnsc:v:71:y:2025:i:8:p:7200-7222. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.