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

Abstract

PurposeOnline 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/approachIn 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.FindingsThe 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/valueUsing 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.

Antecedents of online word-of-mouth reviews on hotels

“…Therefore, the study of such behavior can be approached from two perspectives: sender (by giving opinion) and receiver (by seeking and receiving Online word-ofmouth reviews opinion) (Kanje et al, 2020). Most research has generally focused on the sender (Berezan et al, 2015;Harrison-Walker, 2001;Line et al, 2020;Shen et al, 2020), and fewer researchers have studied on the receivers of WOM and its influence on purchase decisions (Sweeney et al, 2008;Zhu et al, 2014;Book and Tanford, 2019).…”

Understanding passengers’ satisfaction and loyalty towards ridesharing services

“…Customer loyalty has been claimed to be one of the essential factors that boost sales and help service providers to grow in the market (Abror et al., 2019). Among the possible reasons, past researchers mentioned that loyal customers tend to purchase more than new customers (Shen et al., 2020). In addition, loyal customers help to promote the service providers and their brand image through their recommendations (Nghia et al., 2020).…”

Motivations and attitudes towards consulting eWOM when booking accommodation

“…The review of the literature on eWOM contains a number of gaps that are addressed in this work. Firstly, most of the works focus on the sender (Fine et al 2017 ; Line et al 2020 ; Shen et al 2021 ; Tan and Lin 2021 ; Salagrama et al 2021 ), and there are even fewer studies that deal with eWOM queries (Kim et al 2011 ; Book and Tanford 2019 ; De Lima et al 2020 ). However, the study of these queries in the tourism context is especially important not only before the purchase, but also during the experience itself, since browsing a hotel’s website, searching for information online or consulting forums and social networks has become a common practice for decision-making and for the service consumption phase.…”