Organizational competencies for BIM adoption: a cross-field analysis in the built asset industry
Bibliographic record
Abstract
Purpose Numerous studies have examined building information modeling (BIM) adoption in the built asset industry to understand its components, requirements and dynamics of this process. Further research is needed to follow this process, particularly considering emerging practices and technologies. This paper aims to characterize and evaluate the competency profiles underlying the BIM adoption and implementation process across distinct fields within the built asset industry, namely, owners, architecture, engineering, manufacturing, general and specialty contractors. Design/methodology/approach Using a quantitative approach, six fields of activity and 136 competencies were related based on data from 368 organizations. This approach, which includes univariate and bivariate methods (profiles and Chi2-test), enables the delineation and comparison of competency profiles underlying BIM adoption across fields of activity. Findings In all fields of activity, notable progress is observed in management and operational competencies, while administration, research and development (R&D) and implementation are progressing slowly. Sixty-two competencies are correlated significantly with fields of activity. Of note is the expertise of certain competencies in fields of activity (architecture excels in managerial, functional, technological, implementation and R&D competencies; engineering in administrative and normative competencies). Conversely, some fields of activity notably fall behind in most competencies, such as general and specialty contractors. Originality/value By examining the competencies development, this research identifies the key areas in which organizations in various fields of activity should invest to improve their BIM adoption. These findings can guide competency enhancement efforts in the built asset industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".