Digital firms increasingly operate as adaptive learning systems in which organizational knowledge evolves continuously through real-time data interactions and market feedback. Traditional organizational learning theories, developed in pre-digital contexts, fail to capture the velocity, volume, and interconnectedness of knowledge creation in platform-based and data-intensive environments. This theory-development article integrates insights from peer-reviewed studies on big data analytics, dynamic capabilities, digital transformation, and machine-augmented learning to reconceptualize digital firms as self-reinforcing learning systems. We propose that data streams serve as raw material for insight generation, while market feedback closes iterative loops that update organizational memory and renew capabilities. A conceptual model illustrates the continuous cycle of data ingestion, analytics-driven interpretation, strategic action, feedback reception, and knowledge accumulation. Six theoretical propositions explicate the causal mechanisms linking data interaction to capability development, feedback loops to adaptive decision systems, and analytics to the formation of long-term organizational memory. The framework advances management theory by shifting focus from episodic learning to perpetual, data-market co-evolution, offering scholars and executives a lens for understanding competitive advantage in volatile digital ecosystems. By foregrounding learning cycles over static resources, the article highlights how digital firms achieve sustained adaptation through embedded feedback architectures.
Institute for Management, Business, and Accounting Studies