Artificial Intelligence–Driven Personalization and Consumer Behaviour: A Structured Review and Conceptual Framework Based on Scholarly Literature

Abstract

Personalization based on artificial intelligence has become one of the hallmarks of modern digital marketing ecosystems, which allows companies to offer highly personalized recommendations, content, and advertisements using consumer data. Although these systems improve the consumer experience and efficiencies of marketing, the behavioural implications of these systems have not been incorporated effectively in the literature. It is a systematic review of academic literature on AI-based personalization and consumer behaviour that will synthesize the existing knowledge and define the main theoretical processes that influence consumer reactions to algorithmic decision-making systems. A search process based on PRISMA was conducted to conduct a literature screening of 186 relevant studies published in 2010 to 2025 and identify the main research themes and theoretical constructs in the field. The results indicate that there are four significant research streams, including the application of artificial intelligence in marketing, algorithmic recommendation systems, consumer behavioural reactions to personalization technologies, and ethical issues related to data privacy, transparency, and governance of algorithms. On this basis, the paper hypothesises an integrative conceptual model of how AI-mediated personalization affects consumer behavioural responses by the mediating effects of perceived relevance and algorithmic trust, and privacy issues mediate these relationships. The research will help in the growing body of literature on technology-enabled consumer behaviour by bringing together the views on marketing, information systems and artificial intelligence providing theoretical understanding and management implications to implement responsible AI-driven personalization practices.