AI and Machine Learning Advances

This study investigates AI-driven personalisation’s effects on consumer behaviour in the GCC automotive e-commerce sector, addressing the trust-privacy paradox amid rapid digital transformation. Despite projected market growth to $50 billion by 2030, Western-centric models often overlook cultural nuances such as Islamic ethics and collectivism, leading to suboptimal outcomes. To expand on these gaps and opportunities, this study employed a sequential explanatory mixedmethods design, including a six-month randomised field experiment in E-commerce Automobile sector of GCC with 50,000 interactions and cross-sectional surveys to operationalise AI personalisation as a binary treatment using collaborative filtering and predictive analytics. Results reveal a 72.9% conversion lift, 60.3% loyalty increase, and 158.9% ROAS improvement, moderated by trust perceptions (β=0.0375, p=0.039), though CAC rose 13.2%. Trust emerged as the top predictor (18.75% importance), confirming the paradox’s role in collectivist contexts. These findings refine Privacy Calculus Theory for GCC, highlighting the inverted U-shaped trust effect of transparency and its cultural moderators. Practically, they advocate ethical AI deployment with user controls to balance personalisation benefits and privacy risks, advancing sustainable marketing in emerging markets.