Driving E-Commerce Purchase Intention Through AI-Powered Marketing Personalization - Zhang Wei; Li Junjie; Chen Xiaoyan (Aurora: Journal of Emerging Business Paradigms, 2025)
This study aims to examine the impact of AI-driven personalized marketing on purchase intention in Chinese e-commerce platforms, with perceived relevance and consumer trust incorporated as key explanatory mechanisms. A quantitative research design was employed using a cross-sectional survey of active users of major Chinese e-commerce platforms. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess both the measurement and structural models. The results reveal that AI-driven personalized marketing has a significant positive effect on purchase intention. Additionally, AI personalization significantly enhances perceived relevance and consumer trust, both of which, in turn, positively influence purchase intention. The findings further indicate that perceived relevance and consumer trust partially mediate the relationship between AI-driven personalized marketing and purchase intention. The structural model explains a substantial proportion of variance in purchase intention, highlighting the strategic importance of AI-based personalization in shaping consumer behavior in digital commerce. From a theoretical perspective, this study extends the literature on digital marketing and consumer behavior by empirically validating the mechanisms through which AI-driven personalization affects purchase intention in the context of China’s digital economy. Practically, the findings suggest that e-commerce platforms should prioritize relevant, transparent, and trustworthy AI personalization strategies to strengthen consumer engagement and improve conversion performance. Overall, this research provides valuable insights for academics, practitioners, and policymakers regarding the effective implementation of AI-driven personalized marketing in e-commerce environments.