Toward a Federated Organizational Intelligence Capability Model: Cross-Silo Federated Learning as a Distributed Dynamic Capability

In response to the growing need for coordinated intelligence in highly regulated and data-fragmented environments, this study develops a Federated Organizational Intelligence Capability Model that conceptualizes cross-silo federated learning as a distributed organizational capability. While existing research has primarily focused on algorithmic performance and privacy protection, limited attention has been given to how federated systems contribute to organizational capability development and long-term adaptation. Building on the Unified Theory of Acceptance and Use of Technology, Dynamic Capabilities Theory, and Privacy by Design, the study proposes a theoretically grounded framework that explains how federated infrastructures can be translated into higher-order organizational capabilities. Technology acceptance is conceptualized as enabling Federated Knowledge Integration, which supports the development of Federated Decision Intelligence, subsequently enhancing Organizational Agility and, over time, Organizational Adaptability. Privacy Governance Assurance is incorporated as a governance-enabling mechanism that conditions early-stage capability transitions by reinforcing trust, compliance, and collaboration under regulatory and data sovereignty constraints. While the framework is presented sequentially for analytical clarity, it acknowledges the potential for iterative dynamics in practice. Overall, the study advances existing literature by clarifying how cross-silo federated learning supports the emergence and coordination of distributed organizational capabilities, offering a structured and theory-driven basis for examining capability development under conditions of data fragmentation and governance constraints.