Factors influencing the success of women-owned SMEs in Africa
Bibliographic record
Abstract
This dissertation investigates strategies for the success and growth of women-owned SMEs in Africa, focusing on the challenges, opportunities, and impacts. The essence of this project is to highlight the critical role of women entrepreneurs in driving economic growth across the continent, despite facing significant barriers such as limited access to finance, regulatory hurdles, and socio-cultural constraints. The Nigerian populace can testify to witnessing firsthand, the profound impact that women have on both personal relationships and the broader economy. Recently, a study opined that Nigeria’s female entrepreneurs in SMEs contribute to 50% of the nation's GDP and significantly influence the employment rate, with about 23 million women running micro-businesses (See Uddoh, 2023). Africa boasts the highest proportion of women entrepreneurs globally, with OECD research indicating that over a quarter of businesses are started or run by women, compared to just 5.7% in Europe (figures from the European Investment Bank). Despite their high levels of entrepreneurial activity, African women often lack recognition and support.The motivation for this study stems from a desire to enhance economic empowerment through women’s entrepreneurship. This study aims to get a renewed perspective on this subject by analysing quantitative data from sources like the World Bank, IFC, and UNIDO, as this research aims to uncover key factors influencing the performance of women-owned SMEs. The goal is to provide actionable insights and policy recommendations to foster a supportive entrepreneurial ecosystem, thereby contributing to sustainable economic growth in Africa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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 teacher head, 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".