LAUSR

With increasing volumes of customer reviews, ‘helpfulness’ features have been established by many online platforms as decision-aids for consumers to cope with potential information overload. In this study, we offer a differentiated perspective on the drivers of review helpfulness. Using a hurdle regression setup for both helpfulness and unhelpfulness voting behavior, we aim to disentangle the differential effects of what drives reviews to receive any votes, how many votes they receive and whether these effects differ for helpful against unhelpful review voting behavior. As potential driving factors we include reviews’ star rating deviations from the average rating (as a proxy for confirmation bias), the level of controversy among reviews and review sentiment (consistency of review content), as well as pricing information in our analysis. Albeit with opposite effect signs, we find that revealed review un-/helpfulness is consistently guided by the tonality (i.e., the sentiment of review texts) and that reviewers tend to be less critical for lower priced products. However, we find only partial support for a confirmation bias with differential effects for the level of controversy on helpfulness versus unhelpfulness review votings. We conclude that the effects of voting disagreement are more complex than previous literature suggests and discuss implications for research and management practice.