Algorithmic Market Structuring: How AI Reconfigures Competitive Boundaries in Platform-Based Economies

Abstract

Digital platforms increasingly rely on algorithmic systems to allocate visibility, define evaluative metrics, and recalibrate participation conditions. While prior research has examined platform governance, market shaping, and algorithmic control, strategic management scholarship has yet to fully theorize how algorithmic infrastructures reconfigure the boundaries of competition itself. This article introduces the concept of algorithmic market structuring to explain how competitive boundaries in platform-based economies become endogenous to governance architectures. Integrating boundary theory, competitive dynamics, ecosystem strategy, attention-based theory, performance feedback models, and increasing returns logic, we develop a mechanism-based framework comprising four interrelated processes: visibility-based boundary making, metric re-specification, feedback-loop concentration, and rule volatility–induced adaptation. We argue that algorithmic centrality transforms rivalry from category-based competition to exposure-mediated competition, generating cumulative advantage dynamics and accelerating boundary reconfiguration. Importantly, these effects are not deterministic; their structural consequences depend on differentiation levels, switching costs, and governance automation intensity. By reframing competitive arenas as dynamically curated through algorithmic infrastructures rather than statically defined by industry structure, this study advances strategic management theory and clarifies how digital governance architectures shape the evolution of rivalry in contemporary platform markets.