How much do network support and managerial skills affect women’s entrepreneurial success? The overlooked role of country economic development
Bibliographic record
Abstract
The success of women-owned businesses with regard to the stages of economic development of countries is under-examined on a global basis. This study explores the relationship between country economic and political contexts and assesses the importance of entrepreneurs’ networks and managerial skills on women’s entrepreneurial success. The research uses data from 22 countries chosen from multi-dimensional country context constructs (i.e., select economic and political factors) and measures both family and external moral and financial support and managerial skills. The results show that stock (managerial skill) and flow (family and non-family support) differentially influence women’s entrepreneurial success in countries at varying levels of competitive development. In particular, the results confirm the positive influence of managerial skills and family moral and financial support on women’s entrepreneurial success (based on annual income) in countries at a higher level of competitive development and confirm their negative influence in countries at a lower level of competitive growth. Moreover, the results reveal influences of non-family financial support (positive for highly competitive countries) on income but not non-family moral support. Public policy implications are discussed.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".