The Economic Value of Online Reviews

Abstract

T his paper investigates the economic value of online reviews for consumers and restaurants. We use a data set from Dianping.com, a leading Chinese website providing user-generated reviews, to study how consumers learn, from reading online reviews, the quality and cost of restaurant dining. We propose a learning model with three novel features: (1) different reviews offer different informational value to different types of consumers; (2) consumers learn their own preferences, and not the distribution of preferences among the entire population, for multiple product attributes; and (3) consumers update not only the expectation but also the variance of their preferences. Based on estimation results, we conduct a series of counterfactual experiments and find that the value from Dianping is about 7 CNY for each user, and about 8.6 CNY from each user for the reviewed restaurants in this study. The majority of the value comes from reviews on restaurant quality, and contextual comments are more valuable than numerical ratings in reviews.

The Economic and Behavioral Consequences of Online User Reviews

“…Variance (as a proxy of information consistency), instead, might depress purchasing intentions if it is perceived as related to the product itself (i.e., a higher rating variance might imply a more uncertain product quality) but it might also represent an opportunity if it is perceived as related to the degree of similarity between one's own taste and that of prior consumers. Finally, textual contents might be incorporated in one's decision‐making process as well and it might be more powerful than a review's numerical attributes (Wu et al ., 2015).…”

“…Finally, textual contents might be incorporated in one's decision-making process as well and it might be more powerful than a review's numerical attributes (Wu et al, 2015).…”

How online word‐of‐mouth impacts receivers

“…Receivers who are seeking out WOM to guide their choices can vary in their motivation levels, which affects their attention to different components of WOM. When motivation is low, receivers focus on product popularity and pay more attention to summary statistics such as average star ratings; when motivation is high, receivers focus on specific product information and pay more attention to review text (e.g., Doh & Hwang, ; Lee, Park, & Han, ; Martin & Lueg, ; Park & Lee, ; Park, Lee, & Han, ; Watts & Zhang, ; Wu, Che, Chan, & Lu, ; Wu & Wang, ). Because these specific WOM components can each affect sales (Babić Rosario et al, ; Chevalier & Mayzlin, ; Forman, Ghose, & Wiesenfeld, ), differential attention to these components should alter how WOM impacts receivers.…”

Aggregation of consumer ratings: an application to Yelp.com

“…Our paper also complements two other concurrent papers investigating the selection process. Wu et al (2015) use a Bayesian learning model on data from a Chinese restaurant review website similar to Yelp.com in order to measure the value of online reviews for consumers and firms.…”