Organizations are complex adaptive systems in which interdependent actors interact dynamically across levels and over time. This presentation provides an example of how computational process theorizing (CPT) deepens explanation across the layers of theoretical explanation (LTE), thus revealing how organizational phenomena emerge and evolve across different levels. In this study, we demonstrate the explanatory power of CPT and show how it can yield deeper, fine-grained causal explanations. Our focus is on the phenomenon of information sharing in S&OP executive meetings, where effective coordination depends on within-team alignment, expressed as strategic consensus. In these meetings, the Top Management Team (COO, CMO, and CFO) must reconcile distinct functional forecasting plans while pursuing different performance goals, making the S&OP process inherently conflictual. Each executive holds unique private information that must be evaluated for sharing, and if information is withheld, the overall process performance is hindered. Previous research describes how alignment appears to influence S&OP effectiveness, but findings remain at the surface level of explanation, stopping at detailing action and event sequences without uncovering the underlying mechanisms responsible for team shared information emergence. To expand this work, we develop an agent-based model (ABM) that formalizes how strategic consensus shapes the interaction rules guiding information exchange among executives.
Through repeated simulations, the ABM reveals how differences in alignment reflect in different interaction rules, producing distinct emergent patterns of information integration, plan reconciliation, and process effectiveness. By linking the factor layer (strategic consensus), the actor layer (interaction and coordination dynamics), and the driver layer (generative mechanisms of disclosure), this CPT approach provides a more fine-grained theoretical explanation. It showcases how LTE and CPT go beyond descriptive and surface-level understanding of phenomena to reveal the causal architecture underlying alignment, information sharing, and performance in complex organizational systems.