Modeling Governance Transitions in Industrial Clusters: A Bayesian Markov Approach to Resilience and Policy Design

The competitiveness and resilience of industrial clusters depend not only on their sectoral specialization but also on the adaptability of their governance structures. This study develops a stochastic modeling framework, based on absorbing Markov chains with Bayesian estimation, to analyze governance transitions and their impact on cluster performance. Using longitudinal data from 12 European clusters over a 36-month period, we classify governance regimes into four types and estimate transition probabilities under varying institutional and sectoral conditions. Results show that hybrid governance regimes are 2.3 times more likely to sustain high-performance states compared to hierarchical or purely collegial forms, and that targeted interventions at identified bifurcation points can enhance organizational resilience by 18% to 42%. The findings highlight the existence of a “governance goldilocks zone” where institutional complexity yields optimal innovation and performance outcomes. Beyond methodological contributions, the study provides actionable insights for policymakers and cluster managers: supporting hybrid governance mechanisms, monitoring weak signals of institutional transition and tailoring support policies to sectoral contingencies. These results contribute to strategic planning and policy design aimed at fostering competitiveness and resilience in dynamic socio-economic environments.