Machine Learning–Driven Personalized Marketing and Consumer Purchase Intention

Abstract

The rapid integration of machine learning (ML) into digital marketing has transformed how organizations personalize customer interactions and influence purchasing decisions. Despite widespread adoption of ML-driven recommendation systems, limited empirical evidence explains the psychological mechanisms through which personalized marketing shapes consumer purchase intention, particularly in emerging digital economies. This study develops and empirically validates an integrated behavioural model examining the relationships among Machine Learning–Driven Personalized Marketing (MLP), Consumer Trust (CT), Information Privacy Concerns (IPC), Customer Satisfaction (CS), and Purchase Intention (PI). Drawing upon Relationship Marketing Theory, the Technology Acceptance Model, Privacy Calculus Theory, Expectation Confirmation Theory, and the Stimulus–Organism–Response framework, the study proposes that consumer trust and customer satisfaction sequentially mediate the influence of personalized marketing on purchase intention, while information privacy concerns negatively affect trust. A quantitative cross-sectional survey was conducted among 306 consumers with prior experience of AI-enabled digital shopping. Data were analysed using reliability analysis, validity assessment, correlation, multiple regression, and bootstrap mediation analysis. The findings indicate that machine learning–driven personalized marketing significantly enhances consumer trust, which subsequently improves customer satisfaction and strengthens purchase intention. Information privacy concerns exhibit a significant negative relationship with consumer trust. The mediation analysis further demonstrates that consumer trust and customer satisfaction fully and sequentially mediate the relationship between personalized marketing and purchase intention, indicating that technological sophistication alone does not directly influence consumer behaviour. Instead, consumers respond positively when AI-enabled personalization establishes credibility, satisfaction, and confidence in digital platforms. The study contributes to the growing literature on AI-enabled consumer behaviour by integrating technological, psychological, and behavioural perspectives within a single empirical framework. It also offers practical guidance for organizations seeking to implement responsible AI-driven personalization strategies that balance marketing effectiveness with consumer trust and privacy protection. The findings are particularly relevant for organizations operating in rapidly expanding digital marketplaces where sustainable competitive advantage increasingly depends upon ethical, transparent, and consumer-centred AI applications.