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.

产品评论的内容如何塑造其感知价值？我们提出了两种基于概率论主题模型的基于信息论的构造，并显示了它们与评论有用性的关系。第一个结构是内容深度，它可以量化评论的广度深度折衷，并且会对读者的投票行为产生信息性影响。第二种结构，即内容偏差，表示评论内容与其他内容相比的偏差，并对读者的投票行为产生规范影响。注意到评论可以被投票但有用度得分为零的可能性，我们使用双障碍模型来同时估算评论被投票的可能性及其帮助。对三个产品类别的分析表明，深度更深，内容偏差更少的评论被评为更有帮助。此外，这些关系还受许多因素的调节，包括数字评分的偏差，评论的近期性以及评论者的声誉。该研究通过显示评论的内容以及内容和数字评级的相互作用如何共同为消费者创造价值，为文献做出了贡献。