Exploring the determinants of smart construction technology adoption intention: empirical evidence from Chinese construction companies

The construction industry faces enduring inefficiencies and safety risks, underscoring the urgent need for digital transformation. However, the adoption of Smart Construction Technologies (SCTs) remains limited due to the complex interplay of technological and contextual barriers. This study focuses on adoption intention (AI) to identify key factors influencing construction companies’ willingness to adopt SCTs, drawing on an integrated framework that combines the Technology Acceptance Model (TAM), Task-Technology Fit (TTF) theory, and the Perceived Value (PV) perspective. A survey of 295 construction professionals in China was analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that perceived usefulness (PU), perceived ease of use (PEOU), TTF, PV, and social influence (SI) positively influence AI, while perceived risk (PR) has a negative effect. PV partially mediates the effects of PU and SI on AI, and project complexity (PC) strengthens the positive relationship between TTF and AI. The hypothesized direct link between PEOU and PV is not supported. Unlike prior single-theory studies, this study integrates multiple technology adoption frameworks and incorporates both intrinsic psychological constructs and external contextual factors. The model highlights PV as a bridging mechanism, advancing understanding of technology adoption and providing empirical insights for digital transformation.