Determining Factors Influencing BIM Adoption: A Competency-Driven Approach
Bibliographic record
Abstract
Building information modeling (BIM) adoption has received increasing attention over the past two decades due to its potential to revolutionize the built asset industry. Several different characteristics, facilitators, and drivers of BIM adoption have been identified, which has led to the development of BIM adoption frameworks and guidelines. Many of these elements and frameworks have focused on specific organizational-, project-, or industry-level characteristics. Few have taken a competency-based approach to BIM adoption. Thus, this paper aims to empirically evaluate and validate the factors influencing BIM adoption within organizations operating in the built asset industry as understood through the evolution of their BIM competency profiles. The research consists of a mixed-method data collection on a sample set of 368 organizations participating in the Initiative Québécoise pour la Construction 4.0 (IQC 4.0). The research method of analysis is based on a bivariate approach for testing the correlation between the variables concerning the organization’s internal characteristics and their BIM competency development level. The findings show that the factors, which include field of activity, BIM experience, size, financial capacity, and geographical location, are correlated to an organization’s BIM competency development level; in parallel, an organization’s perceived maturity is strongly correlated. The results from this research can be potentially useful to properly guide and structure governmental initiatives supporting competency development for digital transformation.
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.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".