The Steered Self: Algorithmic Consumer Identity Theory in Platformized Markets

Platformized markets increasingly organize consumer encounters through adaptive ranking and personalization systems that learn from behavioral traces and reorder what consumers see over time. Although consumer identity theory explains how consumers use marketplace resources to express and negotiate the self, it does not fully explain how recursive ranked exposure shapes identity trajectories. This article develops Algorithmic Consumer Identity Theory (ACIT) to address that gap. ACIT proposes that identity formation in platformized markets is conditioned by three interrelated mechanisms: algorithmic mirroring, through which consumers interpret personalized outputs as self-diagnostic signals; algorithmic steering, through which ranking and recommendation systems structure future exposure; and reinforcement-loop strength, which captures the inertia generated by recursive feedback among behavior, inference, and exposure. Together, these mechanisms produce the steered self, an emergent identity configuration shaped through repeated interaction with curated exposure environments. The theory specifies how adaptive personalization can increase identity salience, strengthen or fragment coherence, intensify dissonance under conditions of misrecognition, and reduce perceived data agency when contestability is weak. By distinguishing representational feedback from directional exposure governance, ACIT offers a mechanism-based and empirically falsifiable framework for understanding identity in AI-mediated markets. The article contributes to consumer identity theory, platformization research, and AI-in-marketing scholarship, and identifies implications for platform governance and identity-safe personalization design.