Online reviews are integral to consumer decision-making while purchasing products on an ecommerce
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.
Item Type:
Article (Published)
Refereed:
Yes
Additional Information:
Article number: 113799
Uncontrolled Keywords:
Online reviews; Natural language processing (NLP); Shannon's entropy; Text analytics; Zero-truncated regression