Algorithmic Capital Governance in the Age of Artificial Intelligence: A Conceptual Framework Grounded in the Resource-Based View (RBV) and Intellectual Capital Theory (International Journal of Financial, Administrative and Economic Sciences, 2026)
As artificial intelligence becomes embedded in organizational operations and decisions, governance scholarship has concentrated on the intelligent system as an object of compliance and risk management, while the algorithm as a value-bearing capital asset has remained largely outside the analytical focus. This conceptual study addresses that gap by developing a framework for Algorithmic Capital Governance that treats algorithmic capital as a strategic capital resource within AI-augmented organizations, thereby shifting the unit of analysis from the intelligent system to the algorithmic asset. Drawing on the Resource-Based View (RBV) and Intellectual Capital (IC) theory, and following an established conceptual-paper design, the study subjects the algorithmic asset to the criteria of a strategic resource and demonstrates that it is valuable, rare, inimitable, and non-substitutable, thereby acquiring a capital character that warrants a governance logic derived from its own capital characteristics. The study advances an integrated framework for Algorithmic Capital Governance, linking the asset’s characteristics to its distinctive risks, governance principles, six governance dimensions, and the four phases of its life cycle. The framework is founded on the concept of Derived-Asset Governance (DAG): a governance logic derived not from the technology itself, but from the nature of the algorithmic asset as a capital resource. The study further formulates five theoretically grounded propositions for future empirical testing and offers nine recommendations spanning the research, academic, and professional levels.
Keywords: AI Governance; Algorithmic Capital Governance; Algorithmic Capital; Algorithmic Asset; Intellectual Capital; Resource-Based View