Algorithmic Integration Boundaries: Rethinking the Theory of the Firm in Platform Ecosystems

Abstract

The theory of the firm has long assumed alignment between ownership, coordination authority, and capability execution, conceptualizing firm boundaries as governance solutions that internalize control when markets become inefficient. The diffusion of artificial intelligence (AI) infrastructures within platform ecosystems destabilizes this alignment by relocating decision-relevant intelligence beyond formal ownership domains. Firms increasingly embed externally governed algorithmic systems into pricing, forecasting, visibility management, and workflow coordination, allowing coordination and monitoring to occur without asset transfer or hierarchical integration. This article reconceptualizes firm boundaries as algorithmic integration boundaries defined by infrastructural control over coordination and learning processes. A mechanism-based framework identifies four cumulative processes driving boundary reconfiguration: data dependency intensification, workflow embedding, algorithmic visibility and control redistribution, and capability redistribution. Together, these mechanisms produce algorithmic boundary permeability, a condition in which legal ownership persists while effective coordination authority and adaptive capacity extend into externally governed infrastructures. This reconceptualization refines boundary theory, extends resource-based and dynamic capability perspectives through the notion of infrastructure-dependent capabilities, and identifies algorithmic mediation as a structural source of interorganizational power asymmetry.