The impact of entrepreneurship self-concept, work motivation, and risk taking on human resource department performance and business overall performance at Jordan private universities
Bibliographic record
Abstract
The goal of this study was to find out how entrepreneurship self-concept, work motivation, and risk taking on human resource department performance and business overall performance at Jordan private universities. To test the study's assumptions, 311 samples from the research study were taken, looked at, and argued about. Entrepreneurship, self-concept, work motivation, and risk taking were all found to affect the success of both human resource department performance and business overall performance. The first factor was the self-concept, which can be seen in self-esteem, self-image, and self-identity. The second most important factor is work motivation, which includes intrinsic motivation, extrinsic motivation, goal setting and orientation. Risk taking is the third and final important factor. It includes participants' risk tolerance, their perception of risk, and their outcome expectations. The study found that helping employees to understand their job nature and how they can do it, increasing the motivation on job work, and increasing the employees’ demands for taking risks can improve performance ratio in human resource department, and that having an organizational structure that encourages teamwork and work teams can improve the ability to solve problems and improve work performance, which in turn affects overall productivity. This study adds new information to a new area that needs more research to fully understand how workload, work pay, and organizational structure pressures all affect each other. This study's topic, Jordan private universities, is both new and important because it could help scientific private universities to improve how they run and give better education output.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".