Analyzing the Effect of the Senior Management’s Support on the Relationship between Factors Affecting and Employees’ Performance in the Al-Zawiya University of Libya
Bibliographic record
Abstract
This study aims to test the influence of the senior management’s support as a moderating variable on the relationship between the independent factors (Training, Empowerment, Motivation and Communication) and the dependent variable (Performance of Employees). (SEM-AMOS) is used to test the impact of the moderating variable. Where it is depended on the method of sampling or analysis of what is known as multiple-groups analysis. The paragraphs of the senior management’s support variable are collected and divided into two groups according to the mean of the total paragraphs. In addition, according to the relative weights given to the paragraphs of the questionnaire, using a five- point’s Likert scale: 1= strongly disagree to 5 = strongly agree. The first group consisted of the grades less than the mean and it is considered as the group which is non-supporters of the existence of support. While the second group consisted of the grades higher than the mean and considered as the group which is a supporter of the existence of support. The study found that the model of study in the presence of the support of the senior management’s is appropriate for the second group and inappropriate in light of the lack of support by the senior management’s support for the first group.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".