An extended privacy calculus model for AI-driven digital marketing: understanding consumers’ data-sharing decisions

Purpose This paper aims to extend the privacy calculus model to explain consumers’ intention to disclose personal data in artificial intelligence (AI)-driven digital marketing environments. It investigates how AI-specific benefits and risks, mediated by trust, shape consumers’ data-sharing decisions, addressing a critical gap in understanding privacy behavior in complex, algorithmic contexts, while highlighting the implications for AI-enabled service experiences. Design/methodology/approach A research model integrating nine constructs, namely, information sensitivity, data control, personalization, customer support, loyalty rewards, perceived benefits, perceived privacy risks, trust and intentions to reveal personal data, was developed. A structured questionnaire collected 257 valid responses and hypotheses were tested using structural equation modeling to examine the relationships among constructs. Findings Trust plays a central role in consumers’ willingness to share personal data in AI-driven digital marketing. Perceived benefits, including personalization, responsive customer support and loyalty rewards, positively influence trust, while lower perceived privacy risks, shaped by information sensitivity and data control, enhance trust. Trust thus mediates the effect of both benefits and risks on data-sharing intentions, demonstrating how AI-enabled services can shape customer engagement and perceived service value. Practical implications The findings provide actionable guidance for managers and platform designers by highlighting the importance of transparent data governance, user control practices and value-driven AI personalization strategies to build trust and encourage voluntary consumer data sharing in AI-enabled digital marketing environments. Social implications By demonstrating how trust mediates consumers’ responses to AI-driven data practices, this study informs broader societal and policy discussions on ethical AI deployment, emphasizing the need for transparency, consumer empowerment and responsible data use to reduce privacy concerns and protect digital autonomy. Originality/value To the best of the authors’ knowledge, this paper is the first to systematically incorporate trust into the privacy calculus within AI-driven digital marketing. It demonstrates that trust acts as a decisive filter through which AI-enabled benefits and perceived privacy risks are translated into data-sharing behavior, offering both theoretical and managerial insights for addressing the AI-driven privacy paradox and highlighting contributions to the design and management of AI-driven services.