Strategic Knowledge Management for Institutional Effectiveness in Construction Procurement: The Role of Stakeholder Inter-Communication
Bibliographic record
Abstract
In the construction industry, knowledge management and knowledge sharing face significant challenges, primarily due to the involvement of diverse stakeholders and the transient nature of work clusters. While technical issues often receive attention, the informal transfer of knowledge from previous projects remains a critical gap, as the preservation of intellectual capital is essential for organizational effectiveness. The dynamic nature of construction projects, with their numerous stakeholders, further complicates the development of efficient knowledge-sharing practices. As a result, construction organizations often struggle to retain valuable insights from past projects, leading to inefficiencies and a lack of continuity in decision-making. To address these challenges, various knowledge management strategies are being explored. This research paper specifically examines knowledge management within construction procurement in Morocco's public sector, presenting a comprehensive and integrated framework validated by industry experts. The proposed framework is designed to empower procurement teams to effectively leverage organizational knowledge, thereby promoting more efficient and informed decision-making and enhancing overall institutional effectiveness. The framework also emphasizes the role of effective inter-communication among stakeholders, which is crucial for fostering a culture of knowledge sharing and ensuring that all parties can contribute to the continuous improvement of procurement practices.
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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 | 0.032 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".