Structural equation modeling on the drivers of digital technology adoption in the construction industry

Digital technology has become a driving force for many industries in the Industry 4.0 era, but its adoption in the construction industry remains limited. Previous studies have predominantly offered micro-level, fragmented insights into adoption drivers, lacking a quantitative industry-wide perspective. This study aims to identify and categorize the drivers of digital technology adoption in the construction industry (DTACI) and to quantify their driving effects at a macro level.

A total of 25 digital technology adoption drivers were rigorously defined through a systematic literature review and semi-structured interviews with construction experts. The significance of these drivers was evaluated by 145 construction professionals, and the data were further analyzed using exploratory factor analysis (EFA) and partial least squares structural equation modeling (PLS-SEM).

The results classified the drivers of DTACI into six categories: policy environment, human resources, technical characteristics, organizational readiness, digitalization level, and market pressure. Policy environment category demonstrated the strongest driving effect, while market pressure category had the weakest influence. Categories with a middle effect underscored the combined importance of internal drivers alongside external environment in the process of DTACI.

This study provides macro-level insights into the drivers of DTACI and presents a systematic categorization framework for these drivers, thereby bridging the gaps in existing research and offering strategic guidance for construction stakeholders and policymakers worldwide to accelerate DTACI.