Malaysian Practitioner’s Perception on Knowledge Management in Construction Consulting Companies
Bibliographic record
Abstract
Construction consulting companies of today are a part of what has been called the knowledge society. The reason behind this name is the increasing importance of intangible assets as the major source of wealth creation in construction industry. As such, Knowledge Management (KM) has become a critical concern for construction consulting companies to improve project performance. The purpose of this paper is to analyse Malaysian practitioner’s perception on KM in the construction consulting companies. The finding of this study was based on an analysis of transcribed data from semi-structured interviews conducted on Malaysian practitioners in the construction consulting companies to identify pattern and themes accordingly. The findings of the study indicate that all participants have expressed the significant contributions of KM to their success as a professional construction consultant in Malaysia. These participants shared a common characteristic of being professional in construction industry such as they agreed that manage database (data record) and filing system is perceived as important and also the professionals must build extensive social and business networks both locally and overseas that were relevant and have impact on their business success. The professionals have a personal desire to learn and they preferred to learn informally from a variety of people that they can access usually through discussion, seminar/conference and media. Learning from personal experience and experiences from others through learn on job and mentoring were a common characteristics among all the professionals and all of them strongly prescribed that such transfer of learning’s will enable one to shortcut learning and improve their knowledge. However, both internal and external environmental factors have equal impact on the transfer of knowledge in construction consulting companies. Internally, personal interest has the biggest impact followed by culture, commitment from management, incentive or reward for the staff and openness or willingness (more on trust) to share and listen. Externally, business factor/competition has the biggest impact on transfer of knowledge in construction consulting companies.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".