The Economic Value of Online Reviews

This paper investigates the economic value of online reviews for consumers and restaurants. We use a dataset 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 dining a restaurants. We propose a learning model that has three novel features: (1) different reviews offer different informational value to different types of consumers; (2) consumers learn 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 the quality of restaurants, and contextual comments are more valuable than numerical ratings in reviews.