SOCIO-ECONOMIC CHALLENGES HINDERING WOMEN ENTREPRENEURS’ BUSINESS SUSTAINABILITY IN GAUTENG PROVINCE
Bibliographic record
Abstract
Background and objective: This study explored the socio-economic challenges women entrepreneurs face in the Gauteng province of South Africa. It further examines the specific challenges these women encounter in running their businesses. Study design: A qualitative research approach was employed and draws insights from indepth interviews with forty (40) women entrepreneurs operating in various sectors of the Gauteng SMME sector. The study used semi-structured interviews to collect data from the participants. Through these interviews, participants shared their experiences, perspectives and challenges hindering their business success in the region. The data was analysed manually using a thematic analytical technique. Results: The findings identified several key challenges faced by women entrepreneurs. These were: (i) psychological issues, (ii) managerial issues, (iii) economic issues, (iv) sociocultural issues and (v) policy issues. These challenges are often exacerbated by gender-related disparities, which have substantial implications for business sustainability and growth potential. In addition to identifying these challenges, the study explores their impacts on business sustainability, which include the inability to achieve success, limited expansion, reduced confidence, a lack of strategic planning, and closure of the business, among others. Practical implications: Through the lens of social capital theory, it becomes evident that financial support, access to mentorship, and networking opportunities are vital components needed by women entrepreneurs to achieve business sustainability in Gauteng. Conclusion and summary: This study concludes that the facilitation of gender-inclusive policies, mentorship programmes, and financial support structures is essential in nurturing a business ecosystem conducive for the success of women 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 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".