DIGITAL CONSUMER TRUST, AI-PERSONALIZATION, AND E-COMMERCE ADOPTION: A CROSS-CULTURAL STUDY BETWEEN PAKISTAN AND EMERGING ASIAN MARKETS

Abstract

Digital transformation in e-commerce has increasingly been driven by artificial intelligence (AI)-enabled personalization systems that tailor consumer experiences based on behavioral data and predictive analytics. This study examines the impact of AI-personalization on digital consumer trust and e-commerce adoption, with a cross-cultural comparison between Pakistan and selected emerging Asian markets. A quantitative, cross-sectional research design was employed using survey data collected from active online consumers with prior e-commerce experience. Structural Equation Modeling (SEM) was applied to analyze relationships among constructs and test direct, mediating, and moderating effects. The results indicate that AI-personalization significantly enhances digital consumer trust, which in turn strongly predicts e-commerce adoption behavior. Trust was found to partially mediate the relationship between AI-personalization and adoption, highlighting its central role in reducing perceived risk in digital transactions. Furthermore, cross-cultural differences revealed that consumers in Pakistan exhibit higher trust sensitivity compared to other emerging Asian markets, indicating stronger reliance on perceived credibility and system transparency in online purchasing decisions. The study concludes that AI-personalization is an effective driver of e-commerce adoption only when supported by strong trust-building mechanisms. The findings contribute to the extension of technology adoption theories by integrating trust and cultural context into AI-driven consumer behavior models.