Crowd-Driven Competitive Intelligence: Understanding the Relationship Between Local Market Competition and Online Rating Distributions

Dominik Gutt (), Philipp Herrmann () and Mohammad Rahman ()
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Dominik Gutt: Paderborn University
Philipp Herrmann: Consultant
Mohammad Rahman: Purdue University

No 41, Working Papers Dissertations from Paderborn University, Faculty of Business Administration and Economics

Abstract: In this paper, we analyze how changes in local market structure affect the properties of a market’s mean rating distribution. To this end, we combine demographic, socioeconomic, and Yelp restaurant review data for 372 isolated markets in the United States. Our empirical estimates demonstrate that an increase in overall competition – measured as total number of businesses in a market – leads to a broader range and to a decrease in the average of a market’s mean rating distribution. The implication is that a larger market has proportionately more lower rated restaurants, whereas higher rated restaurants have relatively fewer comparable substitutes and face less competition in such a market. These effects are particularly pronounced when the analysis is limited to specific cuisine types where vertical differentiation is more natural or when we control for city-specific unobserved heterogeneity. Our findings highlight that practitioners and scholars using online mean ratings of businesses from disparate markets should account for the local market structure to judiciously analyze the relative market power of a business.

Keywords: Local Market Competition; Online Ratings; Online Offline Interplay; Geographic (search for similar items in EconPapers)
JEL-codes: D22 D43 L11 M31 (search for similar items in EconPapers)
Pages: 36
Date: 2018-12
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