The Relationship Between Implementing Knowledge Management Practices in on-the-Job Training and Developing Professional Skills of Oil Industry Employees
Bibliographic record
Abstract
In the technology-based organizations that are active in oil industry, the enjoyment of the employees of the specialized knowledge and skills plays a determinative role in their vocational performance. Inter alia the various prerequisites for such specialized skills in the employees, the present study has dealt with the evaluation of the knowledge management effect. The current study paper is an applied research in terms of its objectives and it has been carried out based on descriptive-survey method. The study population included the operational workers of Naft-e-Shomal Excavation Company that reached to a total of 2000 individuals out of whom 322 individuals were selected as the study sample volume based on convenience method. The data collection tool was a standard questionnaire the reliability of which was evaluated based on Cronbach’s alpha method and a value equal to 0.86 was obtained. The data analyses have been undertaken assisted by path analysis and t-test statistical examinations through taking advantage of LISREL software and SPSS software. The results indicated that all four practices of knowledge management (knowledge creation, knowledge storage, knowledge application and knowledge sharing) exert positive and significant effects on the employees’ development of specialized skills. Also, it was figured out that the company is in a good status in terms of both of the abovementioned factors.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".