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The impact of high arousal and displeasure on online review helpfulness: Exploring the moderating role of product involvement

Authors
Yun, SeongjunJun, SoyoungKim, Jong Woo
Issue Date
Sep-2024
Publisher
Elsevier BV
Keywords
Discrete emotion analysis; E-commerce platforms; Machine learning; Online customer review; Product involvement; Review helpfulness; Text mining
Citation
Electronic Commerce Research and Applications, v.67, pp 1 - 15
Pages
15
Indexed
SCIE
SSCI
SCOPUS
Journal Title
Electronic Commerce Research and Applications
Volume
67
Start Page
1
End Page
15
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/195061
DOI
10.1016/j.elerap.2024.101436
ISSN
1567-4223
1873-7846
Abstract
Online reviews help consumers make purchasing decisions on e-commerce platforms by addressing uncertainties in online shopping. In the context of information overload, the value of review helpfulness has become increasingly evident. This study aims to explore the effects of discrete emotions on helpfulness by categorizing Ekman's six basic emotions based on arousal and valence, and extracting their probabilities from Amazon reviews using BERT (Bidirectional Encoder Representations from Transformers). The results indicate high arousal or negative emotions adversely influence review usefulness. Furthermore, anger, disgust, and fear, characterized by high arousal and displeasure, significantly affect the perceived helpfulness of the reviews. Additionally, we identify product involvement as a significant moderator, suggesting that consumers’ investment in products influences their interpretation of reviews’ emotional content. This study contributes to understanding the relationship between discrete emotions, product involvement, and review helpfulness in e-commerce, offering insights for online retailers and platform developers to enhance consumer decision-making.
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