AI-Powered Recommendations and Consumer Value Co-Creation in a Personal Data Protection Law (PDPL)-Regulated Market: Engagement, Disclosure, and Intrusiveness

The use of AI-driven recommendations is becoming more prevalent in data-driven digital markets, particularly in areas with personal data protection laws governing the gathering, use, and sharing of consumer data. However, it is not well understood when such recommendations encourage consumer value co-creation and when they do so in a manner that feels intrusive, resisted or withdrawn. This study investigates the combined influence of AI recommendations, perceived intrusiveness, information disclosure, and consumer engagement on consumer value co-creation in the context of Personal Data Protection Law (PDPL) in Saudi Arabia’s e-commerce industry. Based on the Technology Acceptance Model (TAM), Uses and Gratifications Theory (UGT), the Privacy Paradox, and recent studies of human-AI interaction, a moderated-mediation model is proposed and tested empirically via a cross-sectional online survey. The researchers employed a non-probability stratified quota sampling technique with 486 Saudi online consumers, who were recruited using various social media platforms, to gather responses. The model was estimated using PLS-SEM with bootstrapping for the direct, indirect and moderation effects, using SmartPLS 4. Results show that AI-powered recommendations are a positive predictor of consumer value co-creation (β = .483, p < .001), and disclosure is a strong predictor of reduced perceived intrusiveness (β = .684, p < .001). Intrusiveness is found to be a factor that reduces the relationships of value co-created between the AI and the consumer as predicted by the model. The model has an excellent predictive ability for all the outcomes: R2CVC = 0.574; R2IN = 0.683; and R2EN = 0.211. The study explicitly connects TAM and UGT to the Privacy Paradox, as the perceived intrusiveness is not a sufficient reason to prevent consumers from co-creating value when the perceived relevance, gratifications and benefits for control outweigh the perceived discomfort with privacy. In practice, these results can be used to inform the design of platform managers and policymakers on the design of transparent and privacy-aware recommendation strategies that can continue to support consumer choice while promoting sustainable value co-creation.