Determinants of artificial intelligence adoption in the Indian construction industry using the UTAUT model

Abstract

Artificial Intelligence (AI) has been adopted in relatively few areas of construction industry, despite having the ability to make it more productive, efficient, and better informed. In this research, four additional constructs, namely, Awareness, Cost, Trust and Perceived Risk have been applied to the original Unified Theory of Acceptance and Use of Technology (UTAUT) to explore the determinants of AI adoption in the Indian construction industry. This study utilized a quantitative design, surveying 121 construction professionals via an online platform. Analysis of the collected data was carried out in a structured sequence. This process began with frequency analysis, followed by reliability testing, calculation of descriptive statistics, and assessment of normality. Correlation analysis was first conducted to explore relationships between variables, followed by multiple linear regression to test their respective predictive effects. The findings suggest that Performance Expectancy, Social Influence, and Awareness have a significant impact on Behavioral Intention to adopt AI. Conversely, there are no notable impacts of Effort Expectancy, Cost, Trust, and Perceived Risk. Moreover, the effects of Facilitating Conditions and Behavioral Intention to predict actual Use Behavior are significant, and organizational and technical support appear to become a decisive factor of the implementation. The results indicate that the perceived performance benefits and social dynamics have a greater influence on AI adoption intentions as opposed to effort, cost, or risk concerns. The study builds on the UTAUT concept by introducing more constructs and offering practical values on how to enable successful AI adoption in construction industry.