BIM Critical-Success Factors in the Design Phase and Risk Management: Exploring Knowledge and Maturity Mediating Effect
Bibliographic record
Abstract
The effective use of building information modeling (BIM) in the design phase generates countless benefits that contribute to risk management (RM). However, a better understanding of the relationship between the critical-success factors (CSFs) in the design phase has not yet been addressed. This study aims to investigate the influence of BIM CSFs in the design phase in the RM process, exploring the mediating effect played by BIM knowledge, RM knowledge, and BIM maturity. The research design applies the partial least-squares structural equation modeling technique, and the variables were collected by a survey with a sample of 195 respondents from different countries. The results pointed out that earlier and accurate three-dimensional (3D) visualization of the design was the top-ranked recognized design factor in the use of BIM. The findings also indicated that BIM design CSFs have a positive impact on the RM process. Furthermore, there is a positive and significant indirect effect of BIM knowledge, RM knowledge, and BIM maturity through the path of BIM Design CSF on RM.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".