Given that theory is designed to explain how and why a phenomenon occurs, its explanatory focus has implications for knowledge generated about relevant cause and effect relationships. Not surprisingly, significant research attention has been devoted to determining the theoretical and methodological requirements best suited for establishing construct causal effects. Discovering and describing construct causal effects using carefully designed experiments with counterfactual conditions has immense value for making policy, generating managerial recommendations, and designing organizational interventions.
Unfortunately, discovering stable construct causal relationships does not help organizational researchers understand the actual processes responsible for observed phenomena. That is, construct theories with experimental and counterfactual research designs are well equipped to determine stable causal construct relationships; however, they are unable to specify the action/event sequences (which specific actors performed which specific actions) responsible for the observed construct relations. As demonstrated in the simulated example, extremely stable and strong statistical relationships between two constructs can be caused by processes different from the measured variables. Applying recommendations based on established construct causal relationships without knowledge of underlying process mechanisms can result in unintended negative consequences, and makes diagnosing and fixing any issues extremely difficult.
Alternatively, the goal of computational process theorizing (CPT) is to encompass all three layers of theoretical explanation: discovering the rules or process mechanisms which generate observed action sequences that account for construct relationships. By focusing on the rules that generate specific action sequences, CPT aims to discover the reasoning behind how/why specific actors take specific actions at specific times and how those actions influence subsequent processes. Such knowledge allows for the creation of recommendations and interventions directly addressing the affective, behavioral, cognitive, and social actions responsible for outcomes of interest. As such, knowing the processes responsible for observed statistical relationships between constructs allows for more precise recommendations and tailored interventions.