Crowd-Driven Competitive Intelligence: Understanding the Relationship between Local Market Competition and Online Rating Distributions
. Crowdsourced online mean ratings of local businesses are increasingly being used to infer the market power of a business. An important consideration in making this inference is whether two identically rated businesses (e.g., 4 stars) encounter the same local competitive dynamics if they face contrasting local market competition. Stated differently, this requires investigating if the key distributional properties of mean ratings in a market change with competition. 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 heterogeneity • competitive market intelligence|
|Persistent URL||dx.doi.org/10.1287/isre.2019.0845, hdl.handle.net/1765/119964|
|Journal||Information Systems Research|
Gutt, D., Herrmann, P., & Rahman, M.S. (2019). Crowd-Driven Competitive Intelligence: Understanding the Relationship between Local Market Competition and Online Rating Distributions. Information Systems Research, 30(3), 980–994. doi:10.1287/isre.2019.0845