Key Factors Affecting Construction Organizations' Acceptance of BIM: A Comparative Study
Bibliographic record
Abstract
Governments and clients expect contractors to utilise BIM for construction and maintenance purposes at higher level of details. However, the process of BIM implementation is not as quick as it was expected and looks some contractors have not used BIM at all, or do not use it for some of projects. This justifies an urgent study on the process of BIM adoption to identify drivers and key factors influencing the contractorsäó» decision. Many studies focus on exploring BIM advances, its applications and individual matters of BIM acceptance. However, less effort has been made to investigate the impacts of organizationsäó» intention considering BIM performance value and their support for BIM implementation. Therefore, this study aims to identify factors that affect the BIM adoption process at the organization level regarding perceived needs, organizational support and ease of operation. Quantitative and qualitative information are collected through survey and face-to-face interview in Chinese and Australian construction organizations. Structural equation modelling analysis is used to quantify the relationships between influential factors and organizationäó»s intention towards BIM utilization. Analysis results indicate that äóÖBIM Awarenessäó», äóÖPerceived Needsäó», äóÖOrganizational Supportäó», and äóÖDown Timeäó» are four critical factors influencing BIM acceptance in Chinese and Australian construction organizations. Moreover, this study provides an insight of BIM adoption challenges in Chinese and Australian construction industries.
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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".