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Record W4414019570 · doi:10.1108/ecam-02-2025-0312

Human-centric integrated change management framework for digital transformation in construction

2025· article· en· W4414019570 on OpenAlex
Ali Bidhendi, Mani Poshdar, Mostafa Babaeian Jelodar, Farzad Pour Rahimian, Vicente A. González

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEngineering Construction & Architectural Management · 2025
Typearticle
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTransformation (genetics)Digital transformationProcess managementChange management (ITSM)BusinessComputer scienceKnowledge managementEngineeringOperations managementWorld Wide WebChemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.537
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.214
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it