“Highly Recommended!” The Content Characteristics and Perceived Usefulness of Online Consumer Reviews

TLDR

The study aims to identify which content characteristics make online consumer reviews useful to consumers. The authors performed a content analysis of 400 Amazon reviews of experience and search products and related the identified characteristics to the proportion of useful votes received. The analysis shows that argumentation density and diversity strongly predict perceived usefulness, review valence predicts usefulness only for certain product types, and expertise claims have a weak effect.

Abstract

The aim of the present study was to gain a better understanding of the content characteristics that make online consumer reviews a useful source of consumer information. To this end, we content analyzed reviews of experience and search products posted on Amazon.com (N = 400). The insights derived from this content analysis were linked with the proportion of 'useful' votes that reviews received from fellow consumers. The results show that content characteristics are paramount to understanding the perceived usefulness of reviews. Specifically, argumentation (density and diversity) served as a significant predictor of perceived usefulness, as did review valence although this latter effect was contingent on the type of product (search or experience) being evaluated in reviews. The presence of expertise claims appeared to be weakly related to the perceived usefulness of reviews. The broader theoretical, methodological and practical implications of these findings are discussed.