Motivation Application: The Key to Stimulating Work Productivity in Jordanian Private Universities
Bibliographic record
Abstract
Motivation program was found to be the most commonly applied mechanism among firms, providing employees with multiple financial or non-financial rewards. It aims at raising the employees’ interest, attracting and retaining talented employees, rewarding employees based on the value they create and encourages them to work hard to achieve the goals set by organizations. The study has assessed the way motivational practices are applied as a mechanism for improving work productivity, and to establish the difference caused by gender in the application of motivation practices in Jordanian private universities. Such motivation tools have been exaimened, training, work conditions, rewards, promotion, and employee benefits. Quantitative approach has been applied in this study and data was obtained through a questionnaire survey. A total of 320 respondents were selected as a study sample including; professors, associate professors, assistant professors, senior lecturers, lecturer, and assistant lecturer. Additionally, 253 completed questionnaires were analyzed as a final sample using descriptive analysis and independent t-sample test performed by SPSS. Two hypotheses were developed based on literature review. The results indicate that respondents were not motivated by motivational practices applied by private universities. There was a positive relationship between motivation tools (training, financial rewards, promotion, working conditions, and employee benefits) and work productivity, and there was no significant difference caused by gender in the way motivation practices were applied.This study contributs to support the literature that’s not much available on the level of application of motivation practices to the academic staff in arab private universities particuallarly in Jordanian private universities. It recommends universities management to set academic staff salary based on the cost of living, labor market conditions and performance to retain talented staff and to avoid high labor turnover. Also, management should take into account the promotion practice as a motivator that may attract and retain talented academic staff. Future studies may investigate more motivation practices in the same industry or comparing Jordanian private universities with other private universities in the Middle East area based on these variables.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".