Evaluating the Alignment of Organizational and Project Contexts for BIM Adoption: A Case Study of a Large Owner Organization
Bibliographic record
Abstract
Building information modeling (BIM) has been presented as a potential solution to current facilities management problems related to information exchange during handover, and facilities information management during operations. However, implementing BIM in an owner organization is a complex challenge that necessitates reconfiguration of work practices and internal structures to fully realize the benefits. Owners are often unsure about how or whether they should go through the challenges related to implementation. Although previous studies have documented the potential benefits of BIM adoption for owners, such as improvements in work order processing, very little research has specifically looked at the transition to BIM and the scale of the effort required for large and diverse owner organizations. This paper presents the results of a long-term embedded case study analysis of a large owner-operator institutional organization that investigated the alignment of facility management (FM) practices across organizational and project contexts. The research objective was to examine current organizational practices in order to understand the potential, as well as the challenges, of transitioning from a paper-based to a model-based approach in handover and operations. We describe the current state of handover, information management and facility management practices and developed a framework to characterize the alignment between organizational constructs, available technology, project artifacts and owner requirements. This investigation of the current state of practice enables us to understand the gap between available and required information, processes and technology, and to better understand the enormous challenges owners face when considering the transition to BIM.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".