ArticleScientific reports2025
Functional analysis of hyperautomation in construction for advancing efficiency and sustainability through process optimization and technological integration.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Evaluation of construction progress of smart highway: a Bayesian network model.Scientific reports · 2026Article
- A strategic roadmap for construction automation in indonesian mass housing projects.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.