Bayesian Hierarchical Decomposition of Dynamic Capability Portfolios: A Multi-Layered Optimization Framework for Competitive Advantage Reconfiguration Under Environmental Volatility
Keywords:
dynamic capabilities, Bayesian hierarchical modeling, capability reconfiguration, resource orchestration, strategic portfolio optimization, environmental volatility, competitive advantage, Markov Chain Monte Carlo estimation, multinational enterprise strategyAbstract
Sustained competitive advantage under conditions of environmental volatility demands a rigorous reconfiguration of dynamic capability portfolios, yet existing frameworks lack the quantitative precision necessary for strategic decision-making at scale. This study introduces a Bayesian hierarchical decomposition model that operationalizes capability-sensing, seizing, and reconfiguring routines as probabilistic latent constructs, enabling multi-level optimization across strategic business units. Drawing on longitudinal panel data from 318 multinational enterprises across 14 industries (2018–2023), the model is estimated via Markov Chain Monte Carlo sampling with nested hyperprior structures. Results demonstrate that capability reconfiguration velocity significantly moderates the relationship between environmental dynamism and Tobin's Q. The proposed framework advances resource orchestration theory and provides executives with a tractable, data-driven protocol for portfolio-level strategic realignment.
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