Changing Behaviours and Its Theories to Achieve the Desire for Entrepreneurship in Future Generations in the UAE and Gulf Region
Bibliographic record
Abstract
The Gulf region has been emerging as a prosperous hub of entrepreneurship and commercial innovation and is, at present, portraying continuous development. The success of this region is due to several different factors such as the size of the region, youth generation; digital economy; and its progressive access to technology. This study is a literature review and adopts a five-stage process to recruit studies that align with the aim of this study. The data was collected from journal articles, blogs, government websites, and articles from Google Scholar, Proquest, EBSCO, and EconLit. A total of 31 journal articles were reviewed and analyzed. This paper (1) identified variations in entrepreneurial activities, attitudes and perceptions, and aspirations among UAE youth; (2) explored factors defining the nature and level of UAE entrepreneurial work; and (3) presented the potential of entrepreneurship through education in the UAE; (4) need to strengthen technological transfers to entrepreneurial approach and networking opportunities. Initiatives taken by the UAE government in supporting entrepreneurial development were also presented and emphasized. Entrepreneurs are observed as a core aspect that encourages creativity and innovation, generates employment opportunities, and brings prosperity to society. This paper emphatically focuses on entrepreneurship research by presenting policy implications to improve UAE’s entrepreneurship in the country’s economy.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".