TY - JOUR
T1 - The effect of content depth and deviation on online review helpfulness
T2 - Evidence from double-hurdle model
AU - Wu, Chaojiang
AU - Mai, Feng
AU - Li, Xiaolin
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2021/3
Y1 - 2021/3
N2 - How does the content of a product review shape its perceived value? We propose two information theory-based constructs derived from probabilistic topic models and show their relationship with review helpfulness. The first construct, content depth, quantifies the breadth-depth tradeoff of a review and has an informational influence on readers’ voting behavior. The second construct, content deviation, indicates the deviance of the review content in comparison with others and exerts a normative influence on readers’ voting behavior. Noting the possibility that a review can get voted but has zero helpfulness score, we use a double-hurdle model to simultaneously estimate the probability of a review being voted and its helpfulness. The analyses on three product categories show that reviews with more depth and less content deviation are rated more helpful. Further, the relationships are moderated by a number of factors, including the deviation of numerical rating, recency of the review, and the reputation of the reviewer. The research contributes to the literature by showing how the content of a review and the interaction of content and numerical ratings jointly create value for consumers.
AB - How does the content of a product review shape its perceived value? We propose two information theory-based constructs derived from probabilistic topic models and show their relationship with review helpfulness. The first construct, content depth, quantifies the breadth-depth tradeoff of a review and has an informational influence on readers’ voting behavior. The second construct, content deviation, indicates the deviance of the review content in comparison with others and exerts a normative influence on readers’ voting behavior. Noting the possibility that a review can get voted but has zero helpfulness score, we use a double-hurdle model to simultaneously estimate the probability of a review being voted and its helpfulness. The analyses on three product categories show that reviews with more depth and less content deviation are rated more helpful. Further, the relationships are moderated by a number of factors, including the deviation of numerical rating, recency of the review, and the reputation of the reviewer. The research contributes to the literature by showing how the content of a review and the interaction of content and numerical ratings jointly create value for consumers.
KW - Dual-process theory
KW - Information theory
KW - Online reviews
KW - Review helpfulness
KW - Text mining
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U2 - 10.1016/j.im.2020.103408
DO - 10.1016/j.im.2020.103408
M3 - Article
AN - SCOPUS:85098231784
SN - 0378-7206
VL - 58
JO - Information and Management
JF - Information and Management
IS - 2
M1 - 103408
ER -