Abstract:
Online reviews are integral to consumer decision-making while purchasing products on an e-commerce platform. Extant literature has conclusively established the effects of various review and reviewer related predictors towards perceived helpfulness. However, background research is limited in addressing the following problem: how can readers interpret the topical summary of many helpful reviews that explain multiple themes and consecutively focus in-depth? To fill this gap, we drew upon Shannon's Entropy Theory and Dual Process Theory to propose a set of predictors using NLP and text mining to examine helpfulness. We created four predictors - review depth, review divergence, semantic entropy and keyword relevance to build our primary empirical models. We also reported interesting findings from the interaction effects of the reviewer's credibility, age of review, and review divergence. We also validated the robustness of our results across different product categories and higher thresholds of helpfulness votes. Our study contributes to the electronic commerce literature with relevant managerial and theoretical implications through these findings.