AI acceptance among non-IT professionals in real estate and construction: an exploratory factor structure and priority analysis using PLS-SEM and IPMA

In the real estate and construction industry, decision-making is often led by non-IT specialists operating in capital-intensive, high-risk environments; yet empirical research on this group’s acceptance of artificial intelligence (AI) remains limited. This study examines 122 real estate and construction professionals in South Korea, taking measurement items commonly employed in technology acceptance research as a starting point. The factor structure of AI acceptance is derived inductively through Exploratory Factor Analysis (EFA); the measurement model is then examined using Confirmatory Composite Analysis (CCA); structural pathways are estimated through Partial Least Squares Structural Equation Modelling (PLS-SEM); and strategic priorities are mapped using Importance-Performance Map Analysis (IPMA). The EFA identified four factors: AI Utility (F1), AI Familiarity (F2), No-Code AI Accessibility (F3), and Organisational Support (F4). Notably, peer recommendation converged with task efficiency and cost-effectiveness into a single factor. In the PLS-SEM results, F1, F2 and F3 were positively associated with behavioural intention, whereas F4 was not significant (R2 = .487). The IPMA further indicated a gap between the importance of AI Familiarity and current experience levels. These findings suggest that AI acceptance in this group may be more closely associated with practical value and usage experience than with top-down organisational support.