Drivers and Practices for Technology Adoption in Construction
Construction continues to face persistent productivity, safety, and coordination challenges, yet the last five years have seen rapid growth in digital solutions such as building information modelling (BIM), artificial intelligence (AI), digital twins, immersive training tools, and blockchain-enabled...
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| Main Authors: | , , , , , |
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| Format: | Proceeding |
| Language: | en |
| Published: |
Asian Scholars Network
2026
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/51299/1/Drivers%20and%20Practices%20for%20Tech%20Adoption%20in%20Construction%202026.pdf http://ir.unimas.my/id/eprint/51299/ https://asianscholarsnetwork.com/?s=KLISEE2025 |
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| Summary: | Construction continues to face persistent productivity, safety, and coordination challenges, yet the last five years have seen rapid growth in digital solutions such as building information modelling (BIM), artificial intelligence (AI), digital twins, immersive training tools, and blockchain-enabled information sharing. This paper synthesises recent journal evidence on (i) how construction organisations adopt technology, (ii) the main drivers that accelerate adoption, and (iii) policy levers that can improve diffusion across fragmented supply chains. Drawing on technology–organisation–environment (TOE) and related adoption perspectives, the discussion explains why adoption in construction is rarely a single purchasing decision and is instead a staged change programme that requires governance, capability building, and integration with project delivery processes. Key drivers include cost and schedule certainty, competitive pressure and client requirements, safety performance, sustainability and carbon reporting, and the need for trustworthy, near real-time data flows across stakeholders. The paper argues that policies are most effective when they combine demand-side measures (public procurement requirements and standardised information deliverables), supply-side measures (skills development, incentives, and demonstrator projects), and ecosystem measures (interoperability standards, data governance, and platform coordination). |
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