A Causal Map Analysis of Supply Chain Decentralization

We study the inclusion of loops in automated theory development based on causal logic. As an area of application, we formalize a model of learning, adaptation, and selection in supply chain management. Our methodological contribution is to analyze a causal network with propositional logic, explaining the difference between material and intentional causality and considering cumulative causality. In the application domain, we prove that the ability of a supply chain to attract resources in turbulent environments depends on its governance structures, the degree of decentralization, and learning incentives, while in stable environments, a supply chain fails to attract resources if a dominant firm appropriates the rents created by others or if it lacks the ability to replicate its own structure. Furthermore, in turbulent times, adequate resources and dynamic routines allow the supply chain to survive