Revenue distribution and behavioral regulation in data trading with intermediaries involved – based on a tripartite evolutionary game

Purpose This paper aims to examine the governance of personal data trading when data intermediaries are involved, especially investigates how revenue-sharing arrangements, compliance costs and regulatory intensity jointly influence the evolutionary strategic choices of individuals, data intermediaries and the government. Design/methodology/approach A tripartite evolutionary game model is constructed, which integrates a dynamic revenue-sharing mechanism, marginal excess returns from non-compliance and a dual-parameter regulatory intensity. The study uses Lyapunov stability theory and MATLAB numerical simulations to analyse how the interplay of these specific factors drives the system towards a desirable equilibrium. Findings The results reveal an endogenous linkage mechanism between revenue-sharing design, non-compliant excess returns and regulatory intensity. Firstly, in the early stage of personal data markets, reward–penalty regulation is necessary to compensate for compliance costs and initiate trust-based participation. Secondly, appropriately designed revenue-sharing arrangements can reshape compliance incentives and partially substitute for regulatory enforcement, especially as market conditions continue to improve. Thirdly, regulatory strategies evolve endogenously with governance costs. When the marginal cost of strong regulation exceeds its incentive effect, the system shifts towards a weak-regulation equilibrium, which can be destabilised by high black market returns, indicating the importance of combining subsidies with deterrent penalties. Originality/value Unlike prior studies that rarely analyse revenue distribution systematically, it integrates revenue-sharing arrangements as an endogenous driver into the model, exploring how they interact with compliance costs and regulatory intensity to shape stakeholders’ strategic choices. This approach extends the application of tripartite evolutionary game theory to personal data markets, enhancing theoretical rigour and providing actionable implications for building a stable and compliant data-trading ecosystem.