The devil is in the details! Effect of differentiated platform governance on online review manipulation

Qiang Wang, Wen Zhang (), Jian Li, Feng Mai and Zhenzhong Ma
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Qiang Wang: Wenzhou University of Technology
Wen Zhang: Beijing University of Technology
Jian Li: Beijing University of Technology
Feng Mai: Stevens Institute of Technology
Zhenzhong Ma: University of Windsor

Palgrave Communications, 2024, vol. 11, issue 1, 1-14

Abstract: Abstract Recent years have witnessed an increasing number of manipulated online reviews in e-commerce platforms. Previous research has provided substantial evidence that vendor manipulation of online reviews has a significant negative impact on the stakeholders involved in the e-commerce business. Many platforms take various governance measures to filter manipulated reviews. Nevertheless, the effectiveness of these measures still remains unknown to a large extent. To bridge this research gap, this paper investigates the effect of differentiated platform governance, including defined as interventions to counterattack manipulation intensity, manipulation duration, and perceived quality manipulated, on the probability of future review manipulation. We develop a game theoretical model that incorporates the strategic interactions between the platform and vendors, which yield several testable hypotheses. We then conduct an empirical analysis of platform governance and review manipulation by using the review manipulation data collected from Amazon.com. Results of the analytical model and empirical analysis show that platform governance that targets manipulation intensity and manipulation duration can both effectively mitigate review manipulation probability. On the contrary, platform governance to counterattack manipulating perceived product quality exhibits an inverted U-shape relationship with review manipulation probability. This study provides novel insights into how to better mitigate online review manipulation for e-commerce platforms.

Date: 2024
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DOI: 10.1057/s41599-024-03859-9

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