An empirical investigation of online review helpfulness: A big data perspective

Abstract

This study investigates the determinants of online review helpfulness, adopting various predictors from three dimensions of the online review management: source factors, review factors, and context factors. Based on a large, comprehensive dataset that includes 14,051,211 online reviews in 24 product categories from an ecommerce retailer, Amazon.com, this study provides empirical evidence on the effect of source and reviews factors on perceived review helpfulness, which the extant literature has reached inconsistent conclusions. In addition, this study considers the effects on review helpfulness created by context factors: product satisfaction, product popularity, product intangibility, and product variety. These factors are scarcely discussed in the existing literature. The major findings include (1) review extremity, review depth, and reviewer expertise have a positive effect on review helpfulness; (2) review inconsistency, product intangibility, product satisfaction, product popularity, product variety, and reviewer experience have negative effects; and (3) product intangibility moderates the effect of review extremity and depth on review helpfulness.