THE POTENTIAL OF KNOWLEDGE MANAGEMENT ON CONSTRUCTION SITES
Bibliographic record
Abstract
Knowledge management is the process of creating, sharing, using, and managing knowledge, one of the most valuable organizational resources. This approach is well-known in Austria's industrial sector, but applied only in major construction companies. It is mainly used to share knowledge between different departments, but it is not commonly found on construction sites. During the construction phase a variety of separate firms build a temporary multidisciplinary organization, to produce investment goods. To show the potential of knowledge management on building sites in Austria, 78 interviews were conducted. Construction sites of different types (new construction and refurbishment of buildings) were taken into account in order to guarantee a representative outcome. The highest cost-benefit ratio for knowledge management can be seen in knowledge intensive processes. The execution phase is characterized by craftwork which often includes many routine steps. But the survey shows that almost a quarter of the daily business is about knowledge intensive processes while the amount doesn't correlate with the working experience. Furthermore, on construction sites with many trades the lack of information and knowledge transfer is the cause of nearly a quarter of the problems faced. The findings indicate the need for knowledge management on construction sites and the potential grows with the number of trades. The teambuilding process can be seen as the most important step for an efficient knowledge management during the execution phase.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".