Reframing marketing in the age of AI: the role of self- efficacy, trust, and well-being in technology adoption

The rapid diffusion of generative artificial intelligence is reshaping how individuals search, learn, decide, and interact with market offerings, calling for a rethinking of technology adoption beyond purely instrumental perspectives. This dissertation investigates the psychological mechanisms through which human–AI interaction influences both technology adoption and individual well-being, with a particular focus on the roles of self-efficacy and trust. Grounded in marketing, consumer psychology, and human–computer interaction, the work integrates acceptance theories with hedonic and eudaimonic conceptions of well-being to develop a human-centered framework for understanding AI use. The research adopts a multi-method, cumulative design. First, a large-scale bibliometric analysis maps the intellectual structure and thematic evolution of research at the intersection of marketing, technology, and well-being, revealing a shift from functional adoption models toward experiential, ethical, and welfare-oriented perspectives. Second, a protocol analysis based on think-aloud data examines cognitive and emotional processes during interaction with a generative AI system, highlighting how knowledge, perceived control, and trust are dynamically constructed and calibrated. Third, a series of controlled experiments tests the causal relationships linking self-efficacy, trust, hedonic and eudaimonic well-being, and intentions to adopt AI, including mediating and moderating mechanisms. Across studies, results show that self-efficacy functions as a central motivational and experiential driver of AI adoption, fostering both positive affect and a sense of personal growth, while trust operates as a relational anchor that conditions reliance on and engagement with intelligent systems. Well-being emerges not merely as an outcome of technology use, but as a key psychological pathway through which adoption intentions are formed. The dissertation contributes to theory by positioning generative AI adoption as an experiential and relational process embedded in users’ pursuit of autonomy, competence, and meaning. Managerially, it identifies design levers—such as feedback, transparency, and guidance—that support calibrated trust, strengthen self-efficacy, and promote responsible, well-being-enhancing use of AI in marketing contexts.