A Conceptual Review of Consumer Behavior in AI-Personalized Digital Environments

Abstract

The rapid integration of artificial intelligence (AI) into digital platforms has fundamentally transformed consumer behavior through personalized interactions and algorithmic decision support. This study provides a conceptual review of consumer behavior in AI-personalized digital environments, aiming to reconceptualize traditional theories that are increasingly inadequate in explaining contemporary market dynamics. Drawing on interdisciplinary literature from marketing, behavioral economics, and information systems, this study identifies critical shifts in how consumer decisions are formed, influenced, and constrained by algorithmic systems. The findings highlight three major theoretical developments. First, consumer decision-making is no longer solely bounded by cognitive limitations but is increasingly shaped by algorithmic bounded rationality, where technological architectures define available choices. Second, AI personalization contributes to preference closure, reinforcing existing behaviors while limiting exploratory consumption. Third, consumer autonomy is reframed as constructed autonomy, where perceived freedom of choice exists within algorithmically curated environments. Additionally, the study identifies emerging phenomena such as the transparency paradox, algorithmic trust, and privacy resignation. Based on these insights, this study proposes an integrated conceptual framework that emphasizes the dynamic interaction between consumer, algorithmic, and structural domains. The study contributes to the literature by offering a novel theoretical perspective on consumer behavior in the digital age and by challenging conventional assumptions of rationality and autonomy. Managerially, the findings underscore the importance of balancing personalization with transparency, trust, and ethical considerations. Future research is encouraged to empirically validate the proposed framework across different digital contexts.