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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".