Entrepreneurship as a career choice for Emirati women: a social cognitive perspective
Bibliographic record
Abstract
Purpose The purpose of this study is to utilize social cognitive theory to investigate how social comparison orientations, individual cognitive, and environmental factors influence females' decisions to pursue self-employment in the United Arab Emirates In doing so, the authors explore how the entrepreneurial self-efficacy of Emirati women also influences individuals towards entrepreneurship. Design/methodology/approach Using a survey instrument administered in both English and Arabic, data were collected from one hundred and three (103) employed Emirati women and eighty-four (84) self-employed Emirati women who were taking part in workshops conducted by the Dubai Chamber of Commerce. Findings The results from the study suggest that the social environment is a contributing factor toward self-employment, with higher levels of social comparison orientation increasing the likelihood of Emirati women being self-employed. Consistent with prior research, the authors also find that internal cognitive factors also play a significant role, with Emirati women possessing higher levels of entrepreneurial self-efficacy and having a higher likelihood of being self-employed. Originality/value This is one of the few studies aimed at exploring the role of social comparison orientation as a factor in motivating females towards entrepreneurship in the Middle East and North African (MENA) region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".