Revisiting technology acceptance: how social capital shapes AI migration decision quality and acceptance

Purpose This study investigates how social capital influences users' readiness for technological change, which further shapes their AI migration decision-quality and acceptance. Design/methodology/approach The study adopts a cross-sectional quantitative research design collecting survey data from 297 millennials via Prolific Academic. The proposes framework is tested using covariance-based structural equation modeling (CB-SEM). Findings The findings of the study indicate that AI competence, AI adaptability and AI platform accessibility positively influence generational technological change readiness which is turn significantly predicts the AI migration decision quality and AI migration acceptance. Further, generational technological change readiness significantly mediates the relationship between AI competence, AI adaptability, AI platform accessibility and AI migration decision quality. Moreover, generational technological change readiness also mediates the relationship between AI competence, AI platform accessibility and AI migration acceptance. Finally, both ethical awareness and engagement intensity significantly moderate the relationship involving AI adaptability & AI platform accessibility. Originality/value The novelty of this research grounds in advancing social capital framework and AI research by reframing AI engagement as a technology migration mechanism. Further, the study also differentiates users' behavioral willingness (AI migration acceptance) and analytical evaluation (AI migration decision quality) in shaping AI-related transitions.