Research on data transaction compliance: A collaborative and co-governance approach considering buyer erroneous feedback - Fanghao Xiao; Xinqing Sun; Junxin Shen; Wenxia Yi (PLoS ONE, 2025)
Data transactions are frequently hindered by compliance risks due to participants’ lack of self-regulation and the presence weak regulatory mechanism. To address seller’s non-compliant transaction issues, this study proposes a collaborative governance model that integrates platform audits, government oversight, and buyer supervision. This model considers the heterogeneity of buyer utility and applies evolutionary game theory in a noisy feedback environment. The results indicate that accurate buyer feedback can promote compliance and reduce the supervisory burdens on platforms and governments. The reputation effect can enhance the positive behavior of sellers and platforms but has an “inverted U-shaped” relationship with government regulatory enthusiasm. The government’s subsidy and accountability should avoid a “heavy subsidy and light accountability “and the platform’s reward and punishment mechanism should steer clear of “heavy reward and heavy punishment”. Reducing the benefits of government coordination can also curb “free-riding” behaviors.