<![CDATA[Artificial intelligence has transformed e-commerce through algorithmic personalization systems that provide customized recommendations, predictive advertising, and individualized shopping experiences. By analyzing behavioral and transactional data in real time, these systems enhance consumer convenience, purchasing efficiency, and platform engagement. However, increased reliance on data collection and predictive analytics has intensified concerns regarding privacy intrusion, surveillance, and loss of control over personal information. This study aimed to examine the relationship between AI-driven personalization and consumer privacy anxiety in e-commerce environments. Particular attention was given to the effects of algorithmic customization on consumer trust, purchase intention, perceived convenience, emotional discomfort, and privacy-related concerns. A mixed-methods explanatory sequential design was employed involving 450 active e-commerce consumers from five major online shopping platforms. Quantitative data were collected through standardized questionnaires, while qualitative data were obtained through behavioral simulations, reflective response forms, and semi-structured interviews. Structural equation modeling, regression analysis, and correlation testing were used to examine relationships among variables. Results revealed that AI personalization significantly increased consumer engagement, perceived shopping convenience, and purchasing intention. Nevertheless, privacy anxiety and surveillance concerns remained evident despite positive attitudes toward personalization benefits. Perceived algorithmic transparency and greater consumer control over personal data reduced emotional discomfort and strengthened trust in digital platforms. These findings indicate that sustainable AI personalization requires the integration of technological efficiency, ethical transparency, consumer empowerment, and responsible data governance.]]>
