From Anxiety to Action: How AI-FoMO Shapes Workers’ Digital Commerce Entrepreneurial Intention

Generative AI is reshaping how workers form digital commerce entrepreneurial intentions, yet how AI-related fear of missing out (AI-FoMO) converts into proactive entrepreneurial behavior remains unclear. Drawing on social cognitive theory, this study tests a model in which AI-FoMO drives AI reliance, which in turn shapes two distinct cognitive routes: a tool-referenced route through perceived task automation (PTA) and an agent-referenced route through borrowed entrepreneurial competence (BEC), a newly proposed construct capturing how human–AI coupling reshapes workers’ self-perceived capability for solo digital entrepreneurship. We further test whether digital self-efficacy (DSE) moderates the conversion of these perceptions into intention. Survey data from 328 Korean workers were analyzed using PLS-SEM. AI-FoMO predicted AI reliance (β = 0.391), which shaped both PTA (β = 0.531) and BEC (β = 0.291); both routes predicted entrepreneurial intention, and both serial mediation paths were significant, with the PTA route approximately 2.6 times stronger than the BEC route. DSE moderated only the BEC route (β = 0.105, p = 0.025), not the PTA route (β = −0.077, p = 0.109), an asymmetry confirmed by a formal coefficient-difference test. The findings reframe AI-FoMO as a potential driver of approach-oriented entrepreneurial behavior, introduce BEC as an extension of vicarious experience to the human–AI context, and show that digital self-efficacy selectively regulates how AI-induced perceptions convert into entrepreneurial intention.