Ready for departure: Factors to adopt large language model (LLM)-based artificial intelligence (AI) technology in the architecture, engineering and construction (AEC) industry

• Performance, effort, social influence & service reliability positively affect LLM-AI adoption intention. • Social influence emerges as the strongest predictor of LLM-AI technology adoption in construction. • Facilitating conditions negatively impact adoption, highlighting need for systematic training. • Experience moderates technology acceptance factors more significantly than age in AEC industry. The architecture, engineering, and construction (AEC) industry is being transformed by Large Language Model (LLM)-based Artificial Intelligence (AI) technologies like ChatGPT. This study analyses factors influencing the intention to use LLM-based AI technologies among construction professionals using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. The model incorporates performance expectancy, effort expectancy, social influence, facilitating conditions, and service reliability. Findings indicate that performance expectancy, effort expectancy, social influence, and service reliability positively influence the intention to use LLM-based AI technology, while facilitating conditions have a negative impact. Performance expectancy reflects users' expectations that the technology will enhance efficiency and performance. Effort expectancy points to the importance of an intuitive interface in lowering adoption barriers. Social influence is the most significant factor, highlighting the role of peer support and recommendations. Service reliability is crucial for user trust and continuity. Conversely, the negative impact of facilitating conditions underscores the need for adequate resources and support for successful adoption. Comprehensive education and training programs are essential for effective technology utilization. This study validates the extended UTAUT model in the construction context and provides insights for enhancing LLM-based AI technology adoption. Future research should explore these factors across different sectors and cultural contexts to develop robust adoption strategies.