The Role of Knowledge Management on Competitive Advantage in Jordan Manufacturing Companies from Employees Perspectives
Bibliographic record
Abstract
The study aimed to investigate the role of knowledge management on competitive advantage in Jordan manufacturing companies from employee’s perspective. The research used the descriptive analytical methodology. In addition a self administrated questionnaire was developed according to research hypothesis and objectives for the purpose of achieve the study objectives. The research sample consisted of. 255 subjects. The self administrated questionnaires were distributed over 0 research sample, 240 questionnaire were collected, therefore the research sample is 240.. All gathered data were checked and coded then analyzed by using the social Packaging statistical System (SPSS). The study concluded that there is a relationship between knowledge management and competitive advantage in Jordanian industrial companies from the point of view of administrative employee perspectives. In addition the data also concluded that here is a relationship between knowledge generation and competitive advantage. Also there is a relationship between knowledge storage and competitive advantage and there is a relationship between knowledge sharing and competitive advantage in Jordanian industrial companies. The study revealed hat there is a relationship between knowledge application and competitive advantage.... The study recommended that Jordan manufacturing companies have to encourage knowledge management use and to notify their employees with the motives behind such use for obtaining their support.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".