(untitled) (International Journal of Latest Technology in Engineering Management & Applied Science, 2026)
The construction industry is undergoing a significant digital transformation driven by Construction 4.0 technologies, with Building Information Modeling (BIM) and Artificial Intelligence (AI) emerging as key enablers of improved productivity, collaboration, and decision-making. The integration of AI with BIM has given rise to AI-enabled BIM systems, which combine BIM's data-rich environment with advanced capabilities such as machine learning, predictive analytics, natural language processing, and intelligent automation. Despite the potential benefits of AI-enabled BIM, its adoption within the Malaysian construction industry remains limited, and empirical evidence on the factors influencing its acceptance is scarce. This study examines the determinants of behavioral intention to adopt AI-enabled BIM systems among construction professionals in Malaysia. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), the study extends the model by incorporating personal innovativeness and initial trust as additional predictors. Data were collected from 261 professionals employed by CIDB Grade 7 (G7) construction firms randomly selected using probability sampling and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that performance expectancy, effort expectancy, social influence, and personal innovativeness have significant positive effects on behavioral intention to adopt AI-enabled BIM systems. Personal innovativeness was a particularly important predictor, exerting both direct effects on behavioral intention and indirect effects through performance expectancy and effort expectancy. Initial trust did not directly influence behavioral intention; however, it significantly enhanced perceptions of usefulness and ease of use, which in turn indirectly affected adoption intention through these mediating constructs. The model demonstrated satisfactory explanatory and predictive power. This study contributes to the literature by extending UTAUT to the emerging context of AI-enabled BIM adoption and by providing one of the first empirical investigations of this technology in Malaysia. The findings offer practical implications for construction firms, policymakers, and software developers seeking to accelerate AI-enabled BIM adoption through user-centered design, digital skills development, organizational support, and targeted policy interventions.