AI-Driven Personalization In E-Commerce: Impact On Consumer Purchase Intention And Trust

Abstract

This research examines the impact of artificial intelligence-driven personalization strategies on consumer purchase intention and trust in e-commerce platforms. Through a mixed-methods approach combining quantitative surveys (n=847) and qualitative interviews (n=32), this study investigates how personalization mechanisms influence consumer behavior. Results indicate that AI-driven personalization significantly enhances purchase intention (β=0.68, p<0.001) while simultaneously presenting complex trust dynamics. The study reveals that transparency in data usage (β=0.54, p<0.001) and perceived control over personalization (β=0.47, p<0.01) mediate the relationship between personalization and consumer trust. Findings suggest that while personalization increases conversion rates by approximately 31%, concerns about data privacy can diminish trust by 23% when personalization is perceived as invasive. This research contributes to the literature by proposing a conceptual framework integrating Technology Acceptance Model (TAM) with Privacy Calculus Theory, offering practical implications for e-commerce practitioners balancing personalization benefits with privacy considerations.