Beyond self-selection: the multilayered online review biases at the intersection of users, platforms and culture

Purpose - Online review bias research has predominantly focused on self-selection biases on the user's side. By collecting online reviews from multiple platforms and examining their biases in the unique digital environment of "Chinanet," this paper aims to shed new light on the multiple sources of biases embedded in online reviews and potential interactions among users, technical platforms and the broader social- cultural norms. Design/methodology/approach - In the first study, online restaurant reviews were collected from Dianping.com, one of China's largest review platforms. Their distribution and underlying biases were examined via comparisons with offline reviews collected from on-site surveys. In the second study, user and platform ratings were collected from three additional major online review platforms - Koubei, Meituan and Ele.me - and compared for possible indications of biases in platform's review aggregation. Findings - The results revealed a distinct exponential-curved distribution of Chinese users' online reviews, suggesting a deviation from previous findings based on Western user data. The lack of online "moaning" on Chinese review platforms points to the social-cultural complexity of Chinese consumer behavior and online environment that goes beyond self-selection at the individual user level. The results also documented a prevalent usage of customized aggregation methods by review service providers in China, implicating an additional layer of biases introduced by technical platforms. Originality/value - Using an online-offline design and multi-platform data sets, this paper elucidates online review biases among Chinese users, the world's largest and understudied (in terms of review biases) online user group. The results provide insights into the unique social-cultural cyber norm in China's digital environment and bring to light the multilayered nature of online review biases at the intersection of users, platforms and culture.