Thesis title: Reframing Marketing in the age of AI: the role of self- efficacy, trust, and well-being in Technology Adoption
This dissertation theorizes technology adoption as a well-being–mediated process that is co-produced with generative AI during interaction. It advances a cumulative design: (i) a bibliometric field map that documents the shift in marketing from utilitarian acceptance models toward perspectives that foreground well-being, trust, and self-efficacy; (ii) an in-situ protocol analysis that opens the “black box” of human–AI interaction; and (iii) a multi-study experimental program that identifies dual hedonic and eudaimonic pathways from self-efficacy to adoption intentions.
The qualitative evidence shows that users do not merely “use” AI; they develop efficacy and calibrate trust through dialogue. Novices tend to follow an affective, trust-led route scaffolded by the system’s fluency, whereas experienced users enact a cognitive, efficacy-led route that treats AI as a co-processor in recursive reasoning. In controlled studies, increases in self-efficacy elevate intentions to adopt AI via well-being: short-run hedonic gains (ease, enjoyment) and longer-horizon eudaimonic gains (competence, growth). Human–computer trust emerges as a boundary condition: when trust is low, hedonic benefits dominate; as trust rises, eudaimonic benefits become the primary engine of sustained use.
Theoretically, the work integrates acceptance frameworks with consumer-welfare and well-being perspectives by specifying the experiential mechanisms that connect interaction with generative systems to user outcomes and by recasting trust as a dynamic property of interaction rather than a static trait. Managerially, it isolates design levers—feedback and transparency—that build capability and calibrate reliance, guiding human-centered AI services that support autonomy, competence, and durable engagement.