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Abstract

We investigate the aggregate impact of consumer reviews on market outcome in a differentiated product category. We model consumers as Bayesian learners who use online consumer reviews to learn and update their beliefs on product quality before their choice. For our empirical analysis, we use aggregate-level, longitudinal data from Amazon.com in the digital camcorder category and estimate the demand parameters. Using model estimates, we conduct two simulation studies and quantify the impact of consumer reviews on the market outcome. We report that the products experience heterogenous market share changes: the standard deviation of market share changes across products and time is 16.7%, ranging from -40% to 20%. In addition, consistent with the previous findings in experience goods, the marginal effect of low consumer ratings is greater than that of high consumer ratings. We discuss model limitations and offer directions for further research.

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