Canadian Production Improvement: A Project Management Improvement Program with Implementing the Knowledge Management Principles
Bibliographic record
Abstract
The production sector has been radically affected by developments in the knowledge of the project management. Therefore, it is important to improve the services production processes in any project. This research aimed to enhance production of cement manufacture by implementing knowledge management principles along with a project management improvement program as mediator. The study was carried out at different cement manufactures in Canada. To enable the study to measure production improvement at cement businesses, the researchers used four knowledge management aspects, first is technology infrastructure, second is human resource, third is knowledge sharing, and fourth is organizational culture along with project management improvement program as a mediator. The researchers employed quantitative research method via using a survey to measure the current study. The questionnaire was distributed randomly among 150 administrative employees of different cement manufacture in Canada. However, the researchers were able to gather 139 completed questionnaires. The study applied hierarchal multiple regression analysis and Sobel test to measure developed research hypotheses. The findings revealed that all knowledge management principles (technology infrastructure, human resource, knowledge sharing, and organizational culture) had direct positive and significant relationship with enhanced production at cement manufactures at Canada. Moreover, the findings showed that all knowledge management principles with mediator factor (project management improvement program) had indirect positive and significant relationship with enhanced production at cement manufactures at Canada.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".