Artificial Intelligence–Driven Personalization and Its Impact on Consumer Purchase Intentions and Brand Loyalty

Abstract

The rapid proliferation of artificial intelligence (AI) across digital commerce has fundamentally transformed consumer–brand interactions, shifting marketing practice from static, rule-based segmentation toward hyper-personalized, real-time experiences powered by machine learning, deep neural networks, and generative AI. This study synthesizes multi-disciplinary empirical and theoretical literature to examine how AI-driven personalization influences consumer purchase intentions and brand loyalty. Four foundational paradigms guide the analysis: the Stimulus–Organism–Response (S-O-R) framework, Privacy Calculus Theory, the Technology Acceptance Model paired with Trust in Automation Theory, and Self-Determination Theory. Evidence consistently demonstrates that algorithmic personalization possesses no inherent behavioral force; its commercial efficacy depends on activating intermediate organismic states perceived relevance, cognitive effort reduction, digital trust, affective engagement, and customer satisfaction. Structural equation modeling across diverse geographic cohorts confirms that direct paths from personalization stimuli to behavioral responses become non-significant when these mediators are controlled. The review further identifies significant boundary conditions, including demographic cohort, market maturity, regulatory frameworks, and cultural–ethical alignment. It also examines the personalization–privacy paradox, algorithmic opacity, perceived creepiness, and psychological reactance, which collectively generate defensive disengagement and purchase regret when personalization exceeds consumer comfort thresholds. Sustainable brand loyalty is shown to depend more on affective engagement than transactional satisfaction. The review concludes with managerial implications advocating experience calibration over personalization maximization, transparent consent-based data governance, and agile cross-functional operating models to balance commercial value creation against consumer trust preservation in intelligent commerce ecosystems.