The Determinants of Using Bank Loans by Women Entrepreneurs: Comparative Study Between Middle East and Western Europe Countries
Bibliographic record
Abstract
The intention of this research is to provide more evidence related to women entrepreneurs by selecting an unexplored sample coming from two different regions: The Middle East and the Western Europe countries. It aims to explore the profile of women entrepreneurs and to further investigate the impact of their socio-demographic profile and their individual subjective perception on their preference for bank loans. The sample was composed of 47 and 46 women entrepreneurs operating in small and medium (SMEs) business activities, respectively in 10 Middle East and 10 Western Europe countries. Results show that the majority of women entrepreneurs in both regions are between 26 and 31 years old, they have a high education level, they have already received a training support and accumulate an important professional experience. The majority of women entrepreneurs also avoid using bank loans. However, women entrepreneurs in the Middle East have more conflict between their entrepreneurial activity and their family duties. Regressions results show that socio-demographic variables of women entrepreneurs, such as the age, the education level, the training support and the professional experience affect positively and significantly the use of bank loans in both regions. Other variables related to family issues such as the marital status, the family size and the family responsibilities provide a negative and significant impact. The subjective perception variables of women entrepreneurs such as the risk tolerance, the self-confidence and the confidence in banks affect negatively on the use of bank loans.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".