As online reviews become a major factor in the consumer decision making process, firms have started seeking ways to help consumers make informed decisions. To avoid potential information overload, most online retailers provide review helpfulness features by allowing consumers to cast a vote on the (un)helpfulness of a review. Previous research has extensively focused on identifying the drivers of review helpfulness while little attention has been paid to review unhelpfulness. Using an empirical analysis of a large review dataset from a major online retailer, this research investigates the drivers of both review helpfulness and unhelpfulness. By revealing the asymmetry between the consumers’ review helpfulness and unhelpfulness voting behaviors, this study provides practical implications for review platforms.

• Yoonsun Jeong(University of Texas at El Paso, Texas, USA)
• Fernando Jiménez(University of Texas at El Paso, Texas, USA)
• Ali Soltaninejad(University of Texas at El Paso, Texas, USA)