Unpacking the black box: How algorithmic transparency and user control shape trust, satisfaction, and purchase intent in the AI era

This study investigates the „personalisation-privacy paradox“ in AI-driven e-commerce, where consumers appreciate tailored experiences whilst growing concerned about extensive data collection by „black box“ algorithms. The research addresses a critical gap in understanding how algorithmic transparency and user control influence consumer responses to AI personalisation, moving beyond monolithic conceptualisations of AI systems. Using structural equation modelling with 389 online shoppers from Algeria, this study empirically tests the distinct mediating roles of Algorithmic Explainability and Perceived User Control. The quantitative cross-sectional survey employed validated measurement scales to examine relationships between constructs. Key findings reveal two distinct pathways: explainability builds cognitive trust through transparency, whilst perceived control enhances satisfaction through user autonomy. Satisfaction demonstrates nearly double the direct influence on purchase intent compared to trust. Critically, high privacy concerns completely nullify personalisation's positive effects on trust. These results demonstrate that ethically responsible personalisation strategies must integrate both algorithmic explainability and user control with robust privacy practices, as transparency alone is insufficient without foundational respect for user privacy.