Motivation Application: The Key to Stimulating Work Productivity in Jordanian Private Universities
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».