Vertical Versus Horizontal Variance in Online Reviews and Their Impact on Demand

Abstract

This article examines the differential impact of variances in the quality and taste comments found in online customer reviews on firm sales. Using an analytic model, the authors show that although increased variance in consumer reviews about taste mismatch normally decreases subsequent demand, it can increase demand when mean ratings are low and/or quality variance is high. In contrast, increased variance in quality always decreases subsequent demand, although this effect is moderated by the amount of variance in tastes. Since these theoretical demand effects are predicated on the assumption that consumers can differentiate between the two sources of variation in ratings, the authors conduct a survey to test this assumption, demonstrating that participants are indeed able to reliably distinguish quality from taste evaluations in two subsets of 5,000 reviews taken from larger data sets of reviews for 4,305 restaurants and 3,460 hotels. The authors use these responses to construct sets of reviews that they use in a controlled laboratory experiment on restaurant choice, finding strong support for the theoretical predictions. These responses are also used to train classifiers using a bag-of-words model to predict the degree to which each review in the larger data sets relates to quality and/or taste, allowing the authors to estimate the two types of review variances. Finally, the authors estimate the effects of these variances in overall ratings on establishment sales, again finding support for the theoretical results.