Comparison of Customer Reviews for Local and Chain Restaurants: Multilevel Approach to Google Reviews Data

Abstract

Online reviews influence customer decisions and present publicly available data to investigate differences between customer evaluations for local and chain businesses. We conduct a text analysis on a sample of 80,728 online customer reviews of quick-service restaurants to examine how the impact of dining experience attributes on customer evaluation differs between the two restaurant types. Estimation of multilevel multinomial models reveals that customer reviews for local restaurants have less polarized sentiment than chain restaurants. This polarization is also evident for sentiment usage related to four dining experience attributes: food, service, ambience, and price. Although food offerings are essential to get high ratings for local restaurants, service quality has a relatively greater impact on customer satisfaction for chains. Although customer reviews favor local restaurants, they need powerful testimonials for differentiation due to high review valence among their local competitors.