Helpful online reviews can decrease information overload, help customers make better decisions and increase customers' satisfaction about the websites. Therefore, so what makes a helpful online review has become an important topic for scholars and practitioners. Individual review rating has been investigated to be an import factor impacting the review helpfulness by the extant literature, but the literature provides inconsistent views on the direction of the impact. From the perspective of attribution theory, the inconsistent findings are reconciled by proposing the inconsistency between the individual review rating and the average rating as a factor influencing review helpfulness and taking the number of product reviews and the direction of rating inconstitency as moderators. 832233 online hotel reviews are collected from Ctrip.com using a Web data crawler and analyzed by zero-inflated negative binomial regression model. The empirical results show that rating inconsistency has a significant negative impact on review helpfulness. The high rating inconsistency will lead consumers' non-product-related attributions of the review and decrease the review helpfulness. The number of product reviews and the direction of rating inconsistency moderate the impact of rating inconsistency on review helpfulness. The impacts are more salient for the reviews with high product review volumns and for the reviews whose rating is bigger than the average rating. Our findings provide new perspective for understanding the review helpfulness and for online market owners on how to design online review systems and manage reviews on their websites.
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