Functional analysis of hyperautomation in construction for advancing efficiency and sustainability through process optimization and technological integration

The construction industry continues to struggle with inefficiencies, high resource wastage, and persistent safety risks, which hinder progress toward sustainability and productivity goals. This study aims to investigate the functional impacts of hyperautomation an integration of AI, IoT, RPA, and machine learning on efficiency, sustainability, resource optimization, precision, scalability, and worker safety in construction projects. A structured questionnaire was developed from prior literature and expert insights, using a 5-point Likert scale to capture perceptions across six critical factors. Data were collected from 211 construction professionals, representing engineers, managers, safety officers, and architects. The responses were analysed using structural equation modelling (SEM), supported by reliability tests (Cronbach’s alpha, CR, AVE), discriminant validity checks (HTMT, Fornell–Larcker, cross-loadings), and multicollinearity diagnostics (VIF). Results indicate that streamlined processes and enhanced efficiency exert the strongest influence on hyperautomation adoption, followed by optimized resource management and sustainability goals, while precision, scalability, and worker safety also demonstrate significant but lesser effects. These findings extend theoretical understanding of digital transformation in construction by empirically validating hyperautomation’s multidimensional contributions and highlight practical pathways for improving sustainability, productivity, and safety outcomes. The novelty of this study lies in its comprehensive framework and empirical validation across multiple performance dimensions, offering actionable insights for both practitioners and policymakers to accelerate hyperautomation adoption in construction.