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

Xiangyou Shen (Oregon State University, Corvallis, Oregon, USA)
Bing Pan (Penn State, University Park, Pennsylvania, USA)
Tao Hu (Hainan University, Haikou, China)
Kaijun Chen (Hainan University, Haikou, China)
Lin Qiao (Nankai University, Tianjin, China)
Jinyue Zhu (East China Normal University, Shanghai, China)

Journal of Hospitality and Tourism Insights

ISSN: 2514-9792

Article publication date: 30 August 2020

Issue publication date: 25 January 2021

423

Abstract

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.

Keywords

Acknowledgements

This research work was supported by the National Natural Science Foundation of China (under no. 71661007) and the Provincial Science Foundation of Hainan (under nos. 2019RC060 and 2019CXTD402). Appreciation is extended to all the students who assisted with the online and field data collection.

A part of the result was presented at the Academy of Leisure Sciences 2020 Conference held at Urbana–Champaign, the USA.

Citation

Shen, X., Pan, B., Hu, T., Chen, K., Qiao, L. and Zhu, J. (2021), "Beyond self-selection: the multilayered online review biases at the intersection of users, platforms and culture", Journal of Hospitality and Tourism Insights, Vol. 4 No. 1, pp. 77-97. https://doi.org/10.1108/JHTI-02-2020-0012

Publisher

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Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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