Determinants of Female Entrepreneurial Success: The Influence of Social, Financial, and Institutional Support
Bibliographic record
Abstract
Purpose: This research aims to identify the variables that impact both the financial and non-financial performances of Afghan women-owned businesses.Method: This study utilized a descriptive, cross-sectional design with a quantitative approach, employing a Structural Equation Model (SEM) to assess the influence of push and pull factors on the success of female entrepreneurs. A purposive sample of 308 women-led Micro and Small Enterprises (MSEs) in Kandahar, Afghanistan, was surveyed using a structured questionnaire.Result: The study identified key determinants influencing both the financial and non-financial performance of women entrepreneurs. Significant factors include familial support and motivation, access to financial resources, availability of training and professional development opportunities, and support from governmental and non-governmental organizations. Conversely, self-independence, self-efficacy, and access to professional networks did not exhibit a statistically significant positive impact on business performance.Practical Implications for Economic Growth and Development: This article outlines key strategies for enhancing the business environment and success of female entrepreneurs. The findings provide a basis for policymakers to design supportive frameworks that foster the growth and sustainability of women-led enterprises. By identifying critical success factors, the study contributes to the empowerment of female entrepreneurs and their transformative role in driving innovation, job creation, economic development, and poverty alleviation.Originality/Value: Although there is ample research on female entrepreneurs, a notable gap exists in studies that explicitly examine the factors influencing their financial and non-financial success, particularly in war-torn areas such as Kandahar. This paper explores significant topics and advocates for further investigation in this field.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".