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

Marios Kokkodis () and Theodoros Lappas ()
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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

Information Systems Research, 2020, vol. 31, issue 2, 412-430

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.

Keywords: online reputation systems; online reviews; hometown bias; popularity-difference bias (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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