The effect of emotional intelligence, and fostering creativity on entrepreneurship business administration: mediating role of innovation and intrinsic motivation
Bibliographic record
Abstract
The study focuses on the intricate nature of emotional intelligence (EMI), Fostering creativity (FC), intrinsic motivation (IM), innovation (IN) and entrepreneurship (E) under the context of corporate management in a country. Based on a sample of 359 actively engaged respondents and using Partial Least Squares Structural Equation Modeling, the study reveals significant positive associations: emotional intelligence includes entrepreneurship, Fostering creativity contains entrepreneurship, innovation, emotional intelligence appears intrinsic motivation, in Fostering creativity appear intrinsic motivation, emotional intelligence includes innovation, in Fostering creativity is innovation, intrinsic motivation includes entrepreneurship, in innovation is entrepreneurship. The results demonstrate a chain mediation: emotional intelligence influences entrepreneurship, intrinsic motivation is involved, and innovation serves as the intermediary. In the same way, it is fertile in creativity to begin entrepreneurship, whereas intrinsic motivation and innovation are mediating factors considering this fact. Accordingly, these findings have a huge impact on the development of effective public health policies and the nature of ecosystems research in future. For organizations in Jordan, a result based on these results will help in the development of a constructive atmosphere which will be inclined towards entrepreneurs by working on emotional intelligence, creativity, intrinsic motivation and innovation. The research puts in place a theoretical framework which is complemented by the granular perspective of these psychological and entrepreneurial factors.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".