DIGITAL RESOURCE THEORY: A CONFIGURATIONAL EXTENSION OF THE RESOURCE-BASED VIEW FOR AI, DATA, AND CLOUD ENVIRONMENTS

Abstract

This study develops Digital Resource Theory (DRT) as a theoretical extension of the Resource-Based View (RBV) to explain competitive advantage in digitally intensive environments. While RBV emphasizes valuable, rare, inimitable, and non-substitutable resources,its assumptions of resource ownership, scarcity, and relative stability limit its applicability in contexts shaped by artificial intelligence, big data, and cloud computing. Drawing on insights from the Digital Resource View, Dynamic Capability Theory, and the Knowledge-Based View, the study adopts a conceptual theory-building approach to propose a configurational framework in which competitive advantage is argued to emerge from the synergistic interaction of AI capability, data resources, and cloud infrastructure. The framework further introduces recombinability as a driver of innovation capability and conceptualizes scalability and environmental uncertainty as key boundary conditions shaping digital resource effectiveness. It is proposed that value creation in digital environments is configuration-driven, scalable, and context-dependent rather than based on isolated resource possession. The study contributes by advancing a coherent theoretical structure, introducing novel constructs, and offering a set of testable propositions suitable for empirical validation using approaches such as PLS-SEM and fsQCA. The framework provides a foundation for understanding digital competition, particularly in emerging economies undergoing rapid technological transformation.