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Your Hometown Matters: Popularity-Difference Bias in Online Reputation Platforms

Author

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  • Marios Kokkodis

    (Carroll School of Management, Boston College, Chestnut Hill, Massachusetts 02467;)

  • Theodoros Lappas

    (School of Business, Stevens Institute of Technology, Hoboken, New Jersey 07030)

Abstract

We study a new source of bias in online review platforms that originates from the popularity difference between the traveling reviewer’s hometown and destination (popularity-difference bias). In particular, we model popularity-difference bias as a function of two opposing forces: (1) the travelers’ evaluation of performance and (2) the travelers’ expectations. The net result of these two forces leads to two competing views regarding the nature of popularity-difference bias: the first view is performance-dominant, whereas the second one is expectation-dominant. Through analyzing a large set of restaurant reviews from a major online reputation platform, we find empirical evidence in support of the performance-dominant view. Specifically, we find that popularity-difference bias affects both the assigned rating and the text-encoded sentiment of a review. When reviewers travel to a less popular location than their hometown, popularity-difference bias is negative. To the contrary, when reviewers travel to a more popular location than their hometown, popularity-difference bias is positive. Popularity-difference bias affects the average rating of restaurants up to 11%. As a result, a restaurant’s ratings skew lower if the restaurant tends to attract guests from more popular locations, whereas they skew higher if the restaurant tends to attract guests from less popular locations. This effect on ratings alters the probability that an average customer will consider a restaurant by up to 16%. Finally, awareness of popularity-difference bias allows managers to improve the design of their ranking systems: we show that such improvements can lead to up to 12% higher reviewer satisfaction, and up to 24% more diversified top-restaurant recommendations.

Suggested Citation

  • Marios Kokkodis & Theodoros Lappas, 2020. "Your Hometown Matters: Popularity-Difference Bias in Online Reputation Platforms," Information Systems Research, INFORMS, vol. 31(2), pages 412-430, June.
  • Handle: RePEc:inm:orisre:v:31:y:2020:i:2:p:412-430
    DOI: 10.1287/isre.2019.0895
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    Cited by:

    1. Marios Kokkodis & Theodoros Lappas & Gerald C. Kane, 2022. "Optional purchase verification in e‐commerce platforms: More representative product ratings and higher quality reviews," Production and Operations Management, Production and Operations Management Society, vol. 31(7), pages 2943-2961, July.
    2. Jindong Qin & Pan Zheng & Xiaojun Wang, 2024. "Product Redesign and Innovation Based on Online Reviews: A Multistage Combined Search Method," INFORMS Journal on Computing, INFORMS, vol. 36(3), pages 742-765, May.
    3. Marios Kokkodis, 2021. "Dynamic, Multidimensional, and Skillset-Specific Reputation Systems for Online Work," Information Systems Research, INFORMS, vol. 32(3), pages 688-712, September.
    4. Jürgen Neumann & Dominik Gutt & Dennis Kundisch, 2021. "Reviewing from a Distance: Uncovering the Negativity Bias of Psychological Distance in Online Word-of-Mouth," Working Papers Dissertations 78, Paderborn University, Faculty of Business Administration and Economics.
    5. Jürgen Neumann, 2021. "When Biased Ratings Benefit the Consumer - An Economic Analysis of Online Ratings in Markets with Variety-Seeking Consumers," Working Papers Dissertations 77, Paderborn University, Faculty of Business Administration and Economics.
    6. Hu, Xin & He, Liuyi & Liu, Junjun, 2022. "Status reinforcing: Unintended rating bias on online shopping platforms," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    7. Hu, Xin & He, Liuyi & Liu, Junjun, 2022. "The power of beauty: Be your ideal self in online reviews—an empirical study based on face detection," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).

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