Are There Gender Differences in Sustainable Entrepreneurship Indicators Amongst SMEs in South Africa? Application of MANOVA
Bibliographic record
Abstract
In addition to the contributions and relevance of entrepreneurship activities to economic growth and development of countries, various factors have equally been advanced as responsible for the success stories of entrepreneurship sustainability worldwide. However, the influence of success factors on gender ownership of entrepreneurship activities is a relatively new aspect in the field of research that has not gained much academic attention in the literature especially in South Africa. This debate is so important in the face of the various agitations for equal participation of women and the inconclusive debate that women are better managers of business enterprises. In this article, using the Multivariate Analysis of Variance (MANOVA) technique, we examined the extent to which sustainable entrepreneurship indicators (finance, social and environmental) account for any disparity in gender ownership and management of business enterprises in South Africa. A stratified sampling method was adopted for the survey. Our analytical technique (MANOVA) created a new summary dependent variable, which is a linear combination of each of our original dependent variables. Confidence intervals of 95% and margins of error (3%) were used to validate the results. Findings indicate that the only difference that exists as per gender ownership disparity is around financial resources. There is therefore a need to realign programmes and policies to reduce this gendered inequality.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".