In the Eye of the Beholder? Empirically Decomposing Different Economic Implications of the Online Rating Variance

The growing body of literature on online ratings has reached a consensus of the positive impact of the average rating and the number of ratings on economic outcomes. Yet, little is known about the economic implication of the online rating variance, and existing studies have presented contradictory results. Therefore, this study examines the impact of the online rating variance on the prices and sales of digital cameras from Amazon.com. The key feature of our study is that we employ and validate a machine learning approach to decompose the online rating variance into a product failure-related and taste-related share. In line with our theoretical foundation, our empirical results highlight that the failure-related variance share has a negative impact on price and sales, while the impact of the taste-related share is positive. Our results highlight a new perspective on the online rating variance that has been largely neglected by prior studies. Sellers can benefit from our results by adjusting their pricing strategy and improving their sales forecasts. Review platforms can facilitate the identification of product failure-related ratings to support the purchasing decision process of customers.