Human-centric integrated change management framework for digital transformation in construction
Why this work is in the frame
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Bibliographic record
Abstract
Purpose This study develops a human-centric change management framework to address the gap between building information modelling (BIM) potential and its practical implementation and adoption in the construction industry by focusing on human factors influencing digital transformation success. Design/methodology/approach A multi-phased methodology was employed, combining systematic literature reviews with advanced network analysis techniques. Two literature review rounds extracted key change management activities and human-centric principles. Social network analysis (SNA) was utilised to quantify relationships and significance within the construction industry context, identifying high-centrality nodes in the network. Findings The analysis identified training, organisational competency assessment and resource allocation as the most critical change management activities for successful digital transformation, which emerged as central nodes. The study developed a tailored three-phase framework (Strategic initialisation, Operational transformation and Sustainable integration) that enables construction organisations to implement BIM and digital technologies while maintaining focus on human factors. Practical implications include improved employee engagement, reduced resistance to technological change, enhanced organisational readiness for digital transformation and a structured pathway for construction organisations to move beyond current BIM implementation barriers. The framework provides actionable guidance for construction leaders to balance technological advancement with human-centric values, ultimately supporting sustainable digital transformation in the industry. Originality/value This study offers a novel data-driven approach to digital transformation in construction by quantitatively analysing relationships between change management activities and human-centric principles. The research addresses a critical gap in BIM and digital transformation implementation literature by developing an integrated framework that balances technological advancement with human considerations, helping organisations move beyond current adoption barriers in the AECO industry’s transformative journey.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it