Bibliographic record
Abstract
The objective of this study has been to compare motivation, intention, fear of failure and self-efficacy in starting business ventures in Saudi Arabia. Inductive content analysis is used to identify the similarities and differences between two cites and groups of people in Saudi Arabia; both male and female entrepreneurs and those who have already established a business. Furthermore, the paper draws on data gathered from eight interviews in the cities of Riyadh and Jeddah. Four of these interviews were conducted with male entrepreneurs and the remaining four were with female entrepreneurs. A description and analysis of each entrepreneur was developed individually including short observations on the interviewees; noting movements and body language. We found the motivation for being one’s own boss is greater in Riyadh than in Jeddah (p=0.024) whereas self-efficacy is greater in Jeddah yet this difference is not significant. Regarding intention, no significant difference was found between region and gender. Finally, both regions consider fear of failure to be predictable. The study presents important contributions to theorists and practitioners in entrepreneurial activities in Saudi Arabia. Our research has contributed to the study in entrepreneurial motivation, intention, role of management, and role of culture of male and female entrepreneurs in Saudi Arabia hence providing more knowledge and information towards the behavior of Saudi entrepreneurs.
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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 | 0.002 |
| 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 | 0.002 | 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".