Behavioral Intention to Use Ai-Enabled Bim in Malaysia – An Extended Utaut-1 Model
TL;DR
Investigating the factors influencing behavioral intention to use AI-enabled BIM systems in Malaysia's construction sector by extending the UTAUT-1 framework with initial trust and individual difference variables shows that performance expectancy, effort expectancy, social influence, and initial trust significantly influence behavioral intention.
Abstract
The integration of artificial intelligence (AI) with Building Information Modeling (BIM) has strong potential to transform the construction industry, yet adoption remains limited, especially in developing contexts. This study investigates the factors influencing behavioral intention to use AI-enabled BIM systems in Malaysia’s construction sector by extending the UTAUT-1 framework with initial trust and individual difference variables. A quantitative research design was used, based on survey data collected from G7 construction firms. The proposed model examined performance expectancy, effort expectancy, social influence, self-efficacy, personal innovativeness, and initial trust and assessed the mediating role of trust. Structural equation modeling was applied to test the hypothesized relationships. The findings show that performance expectancy, effort expectancy, social influence, and initial trust significantly influence behavioral intention, with effort expectancy emerging as the strongest predictor. By contrast, self-efficacy and personal innovativeness do not have significant direct effects. Initial trust also partially mediates the relationships between performance expectancy, social influence, and self-efficacy with behavioral intention, but not those involving effort expectancy or personal innovativeness. However, even though self-efficacy does not have a significant direct effect on behavioral intention, it has a meaningful indirect effect through initial trust, indicating that confidence in one’s abilities helps build trust in AI-enabled BIM systems. The study contributes to theory by integrating UTAUT-1, trust theory, and individual difference perspectives, while also challenging the view that trust always mediates adoption relationships. Practically, the findings highlight the importance of system usability, performance benefits, organizational support, and trust-building measures. Overall, the study deepens understanding of technology adoption in AI-driven construction environments and provides a useful basis for future research and industry implementation.