Causal Explanation in the Coordinating Process: A Critical Realist Case Study of Federated IT Governance Structures

Large, multi-unit organizations are continually challenged to balance demands for centralization of information technology that lead to cost and service efficiencies through standardization while providing flexibility at the local unit level in order to meet unique business, customer, and service needs. This has led many organizations to adopt hybrid federated information technology governance (ITG) structures to find this balance. This approach to ITG establishes demand for various means to coordinate effectively across the organization to achieve the desired benefits. Past research has focused on the efficacy of various coordination mechanisms (e.g., steering committees, task forces) to coordinate activities related to information technology. However, we lack insights as to how and why these various coordination approaches help organizations achieve desired coordinated outcomes. This research specifically identifies coordinating as a process. Adopting the philosophy of critical realism, we conducted a longitudinal, comparative case study of two coordinating efforts in a federated ITG structure. Through a multifaceted approach to scientific logic employing deductive, inductive, and retroductive elements, we explicate two causal mechanisms, consensus making and unit aligning, which help to explain the coordinating process and the coordination outcomes observed in these efforts. We additionally elaborate the operation of the mechanisms through the typology of macro-micro-macro influences. Further, we demonstrate the value of the causal mechanisms to understanding the coordinating process by highlighting the complementarity in insights relative to the theories of power and politics and of rational choice. The study contributes to our understanding of coordinating as a process and of governance in federated IT organizations. Importantly, our study illustrates the value of applying critical realism to develop causal explanations and generate insights about a phenomenon.

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