The Extreme Distribution of Online Reviews: Prevalence, Drivers and Implications

Abstract

In this research, we investigate the prevalence and possible reasons underlying the so-called extreme distribution of reviews in which reviews are heavily skewed to the positive end of the rating scale, with a few reviews in the mid-range and some reviews at the negative end of the scale. Putting together a large dataset of online reviews - over 280 million reviews from 25 major online platforms - we find that the extreme distribution of reviews occurs on different platforms, but the platforms vary with respect to the prevalence of the extreme distribution of their products based on how selective customers are in reviewing products on the platform. Using a cross platform and multi-method approach, including secondary data, experiments as well as survey data, we identify polarity self-selection, described as the higher tendency of consumers with extreme experiences to provide a review, as the main driver of the extreme distribution. Additionally, we describe and demonstrate that polarity self-selection and the extreme distribution reduce the informativeness of online reviews.

Keywords: User Generated Content, Online Reviews, Online Ratings, Extreme Distribution, Self-Selection

JEL Classification: M30, M31