The digital transformation of business has given rise to data-intensive firms in which competitive advantage and strategic positioning are no longer adequately explained by traditional resource-based or dynamic-capability theories. This conceptual theory-development paper reconceptualizes competitive advantage as the outcome of three interdependent mechanisms—digital infrastructure, algorithmic learning, and organizational data accumulation. Digital infrastructure functions as the enabling platform for seamless data flows; organizational data accumulation converts raw information into strategic capabilities; and algorithmic learning provides the adaptive engine that translates accumulated data into real-time repositioning. Synthesizing peer-reviewed studies published across leading outlets, the paper identifies critical theoretical gaps in isolated treatments of these constructs. Six formal propositions articulate causal, moderating, and synergistic relationships that produce a novel competitive logic unique to data-intensive environments. The resulting framework advances digital business theory by demonstrating how these elements collectively generate dynamic, ecosystem-level strategic positioning that traditional models cannot capture. Contributions extend to both scholarly understanding of data-driven strategy and managerial imperatives for sustained advantage in hyper-competitive markets.
Institute for Management, Business, and Accounting Studies