The effect of content depth and deviation on online review helpfulness: Evidence from double-hurdle model

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.