Abstract
This study investigates the underlying domain challenges contributing to the persistence of unstructured data in the AEC industry. Following a critical realist paradigm, a qualitative study involving semistructured interviews with professionals across various practices in the Irish construction sector were conducted. Thematic analysis revealed five key domain challenges: (1) Knowledge gaps related to fundamental data science concepts, BIM, digital twins, and linked data; (2) Stakeholder dynamics, particularly client influence and misalignment between client needs and designer proposals; (3) The “single source of truth” dilemma arising from ISO 19650 implementation challenges; (4) Process inertia stemming from email dependence, perceived benefits of unstructured data, underdeveloped models, and skills deficiencies; and (5) Contractual and regulatory requirements that fail to incentivize structured data adoption. Mapping these themes to a people, process and technology framework revealed that the majority of these problems are systematic in nature. These findings suggest that the industry needs a multifaceted approach addressing knowledge enhancement, stakeholder alignment, standard simplification, workflow modernization, and regulatory integration to overcome the barriers to structured data implementation. This study identifies valuable insights for policymakers, educators, and industry leaders seeking to facilitate automation in construction information management.
| Original language | English |
|---|---|
| Pages (from-to) | 1728-1748 |
| Number of pages | 21 |
| Journal | Journal of Information Technology in Construction |
| Volume | 30 |
| Issue number | 71 |
| DOIs | |
| Publication status | Published - 11 Nov 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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SDG 9 Industry, Innovation, and Infrastructure
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SDG 12 Responsible Consumption and Production
Keywords
- AEC
- qualitative study
- semi-structured interviews
- thematic analysis
- unstructured data
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