Decoding Recursive Personalization - A Conceptual Framework for Artificial Intelligence (AI) in Advertising Personalization
Harsh Shah, Sanjeev Verma, Vartika Srivastava · Journal of Promotion Management · 2026
Advertising personalization has evolved into an integrative, recursive phenomenon, while traditional advertising theories explain AI-driven changes only peripherally. The literature remains fragmented across psychology, marketing, sociology, and computer science. addressing only isolated aspects of advertising personalization. The present study synthesizes multidisciplinary literature by converging data inputs, creative execution, algorithms, outcomes, and perceptions. Using a systematic literature review, the study identifies emergent thematic dimensions and develops a holistic framework that bridges classical personalization theories and modern AI developments, providing a foundation for future research and more effective advertising personalization in the era of artificial intelligence. The present study advances advertising personalization theory by reconceptualizing AI-driven personalization as a recursive socio-technical system, transforming it from a message-level persuasion tactic into an adaptive organizational capability shaped by algorithmic agency, feedback loops, and governance constraints. The present study proposes a metatheoretical shift in advertising personalization research—from static persuasion models to adaptive, system-level theorization.