Adoption of AI in construction design: Insights from UTAUT2 and TOE frameworks

• Behavioral intention drives AI adoption in construction design. • Facilitating conditions significantly enhance the adoption of AI. • Competitive pressure has a direct and indirect influence on AI usage. • Hedonic motivation is the strongest predictor of AI adoption. • Age, gender, and experience moderate AI adoption pathways. Artificial intelligence has revolutionized the construction industry by improving design speed, accuracy, and cost efficiency. This study used the unified theory of acceptance and use of technology 2 combined with the technology, organization, and environment framework to quantitatively examine the variables influencing the adoption of artificial intelligence by design engineers. Partial least squares structural equation modeling was conducted to gain insights from the data, namely structured survey data collected from 268 design engineers with validity and reliability. The results revealed that engineers’ behavioral intention to use artificial intelligence (β=0.313, p-value<0.01), facilitating conditions such as available resources and knowledge (β=0.279, p-value<0.01), and the compatibility of artificial intelligence tools with existing design practices (β=0.155, p-value<0.01) significantly influenced adoption. Notably, hedonic motivation was the most influential factor in behavioral intention (β=0.232, p-value<0.01). The study also explored the direct and mediating effects of other variables on adoption and the moderating effects within the research model. These findings offer practical implications for organizations and policymakers to develop strategies that enhance the adoption of artificial intelligence by addressing individual, environmental, technological, and organizational factors, thereby promoting innovation in construction design processes.