Surveillance in brick-and-mortar retail: theory and practice in the age of AI

The proliferation of Artificial Intelligence (AI)-enabled technologies in brick-and-mortar retail is intensifying surveillance practices and reshaping how consumers experience – and are experienced within – the retail environment, making AI-driven dynamics a pressing priority for retailers, consumers, and researchers alike. Surveillance studies can offer valuable explanations for various consumer phenomena within brick-and-mortar retail environments, yet the literature remains fragmented and lacking integration across disciplines. To address this gap, there is a critical need to understand the diverse phenomena through which consumers experience and respond to brick-and-mortar retail surveillance (retaillance) systems, as these systems are increasingly powered by AI. This paper adopts a three-phase research study: (Phase 1) a critical, interpretive review of surveillance literature across multiple disciplines; (Phase 2) thirty-eight semi-structured interviews with consumers, retail workers, and retail managers in North America; and (Phase 3) integration of theoretical and empirical insights to develop a conceptual framework explaining consumer responses to retaillance. The findings reveal four distinct consumer phenomena through which retaillance operates: (1) consumers’ self-discipline in response to perceived monitoring (panopticism), (2) consumers’ role as surveillers of workers and other shoppers (synopticism), (3) consumer seduction through rewards and incentives encouraging voluntary data sharing (post-panopticism), and (4) consumers’ response to data collection and the digital identity construction (virtual identities). The paper contributes to surveillance studies and consumer behavior literature by demonstrating that classical surveillance theories – when systematically applied to retail contexts and grounded in empirical research – provide powerful frameworks for understanding diverse consumer phenomena and that retaillance is best understood as an assemblage (a heterogeneous combination of technologies, people, procedures, and spaces) rather than as discrete surveillance systems. The paper also shows that AI-enabled surveillance intensifies existing dynamics while remaining explicable through classical theories, which retain relevance and productivity in the digital age.