The impact of entrepreneurship factors on organization behavior and learning at Jordan Orange Telecommunication firm
Notice bibliographique
Résumé
The purpose of this study was to evaluate the impact of important entrepreneurship variables, notably self-concept, work motivation, and risk-taking, on organizational behavior and learning within the setting of Jordan Orange Telecommunication firm. Notably, the research was intended to look at how these factors affect organizational behavior and learning. To evaluate the hypotheses that were proposed in this research, a sample size of 294 people participated in the study, and their data were evaluated and debated. It was shown that an individual's perception of themselves as entrepreneurs, their level of drive at work, and their willingness to take risks all had an impact on developing organizational behavior and organization learning. It became clear that self-concept was the most important component, and that it included aspects such as self-confidence, self-perception, and self-definition. Closely following behind was the concept of work motivation, which included concepts such as self-motivation, extrinsic motivation, the setting of goals, and concentration. Risk-taking was the third key factor, and within this category were subcategories such as risk tolerance and risk perception, as well as expectations surrounding the consequences. The findings showed the possibility for improving organizational behavior and fostering innovation by assisting employees in comprehending the roles they play in their jobs, boosting job motivation, and encouraging a predilection for risk-taking. This can be accomplished by providing employees with training and development opportunities. The study adds fresh insights to a field that merits additional exploration, particularly considering the connection between workload, compensation, and the structural demands that are placed on organizations. The fact that the research concentrated on Jordan Orange Telecommunication company makes it both innovative and of major importance. This is because it can improve both the business's operations as well as the overall quality of the company's goods and services.
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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 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| 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 ».