TY - JOUR
T1 - The informational value of multi-attribute online consumer reviews
T2 - A text mining approach
AU - Yi, Jisu
AU - Oh, Yun Kyung
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2022/3
Y1 - 2022/3
N2 - In the digital age, customers use online reviews to minimize the risks associated with purchasing a product. Major online retailers help customers choose the right product by exposing reviews that received many “helpful” votes at the top of the review section. Given that reviews that have received the maximum helpfulness votes are considered more important in purchase decisions, understanding determinants of helpfulness votes offers clear benefits to online retailers and review platforms. This study focuses on the effect of review informativeness, which is measured by the number of attributes discussed in a review, and its interplay of review valence on customers' perception of review helpfulness. We applied a word-level bigram analysis to derive product attributes from review text and examined the influence of the number of attributes on the review's helpfulness votes. More importantly, we also suggested the moderating role of review valence. Estimation results of the Zero-inflated Poisson models on 21,125 reviews across 14 wireless earbuds indicated that as more attributes are discussed in a review, the more the review can earn helpfulness votes from customers. Furthermore, the positive association between the number of attributes and helpfulness was enhanced among negative reviews. This study contributes to customers' information processing literature and offers guidelines to online retailers in designing a better decision support system.
AB - In the digital age, customers use online reviews to minimize the risks associated with purchasing a product. Major online retailers help customers choose the right product by exposing reviews that received many “helpful” votes at the top of the review section. Given that reviews that have received the maximum helpfulness votes are considered more important in purchase decisions, understanding determinants of helpfulness votes offers clear benefits to online retailers and review platforms. This study focuses on the effect of review informativeness, which is measured by the number of attributes discussed in a review, and its interplay of review valence on customers' perception of review helpfulness. We applied a word-level bigram analysis to derive product attributes from review text and examined the influence of the number of attributes on the review's helpfulness votes. More importantly, we also suggested the moderating role of review valence. Estimation results of the Zero-inflated Poisson models on 21,125 reviews across 14 wireless earbuds indicated that as more attributes are discussed in a review, the more the review can earn helpfulness votes from customers. Furthermore, the positive association between the number of attributes and helpfulness was enhanced among negative reviews. This study contributes to customers' information processing literature and offers guidelines to online retailers in designing a better decision support system.
KW - Big data analysis
KW - Bigram analysis
KW - Multi-attribute reviews
KW - Review helpfulness
KW - Review informativeness
UR - https://www.scopus.com/pages/publications/85102511342
U2 - 10.1016/j.jretconser.2021.102519
DO - 10.1016/j.jretconser.2021.102519
M3 - Article
AN - SCOPUS:85102511342
SN - 0969-6989
VL - 65
JO - Journal of Retailing and Consumer Services
JF - Journal of Retailing and Consumer Services
M1 - 102519
ER -