Female entrepreneurship and access to finance in Nigeria through a gendered lens
Bibliographic record
Abstract
The purpose of this research project is to critically explore and articulate the experiences of female entrepreneurs in Nigeria while accessing finance for business growth through a gendered lens. Despite the significant growth in the level of female entrepreneurship across the world - such as is seen in the US, Australia, Canada, New Zealand and Sub-Saharan African countries like Nigeria - female entrepreneurs continue to experience unique challenges specifically in the way they access finance. In Nigeria particularly, male dominance over females caused by the patriarchal nature of the society presents additional obstacles to the entrepreneurial process of women entrepreneurs. A feminist analytical approach serves as a blueprint to begin to uncover these differences and their implications. Women are expected by society, because of their ascribed gender, to perform domestic related roles and these beliefs are supported by traditional and cultural norms. This gives rise to potential conflict between a woman's business growth aspiration and the expected traditional role she has in the family. As women entrepreneurs face the tasks of juggling family responsibilities, it results in them being less likely to amass sufficient credit history to access external finance to grow their businesses or enough accumulated personal funds to invest into growing bigger enterprises. This research, ontologically and epistemologically, adopts social constructionism, backed by an interpretivist philosophy. This approach allows for a deeper understanding on the lived experiences of Nigerian entrepreneurs in accessing finance by contrasting male and female entrepreneurial experiences. The analysis uses data from 30 business owners, who were interviewed drawing on semi-structured interview guidelines online via Zoom, an online video conferencing platform. The findings highlight the influence socialization has on the entrepreneurial processes of females and males and how this creates nuances that influence their ability to access finance. This research indicates that structural gender inequalities exist in business financing in Nigeria due to the collateral requirement of accessing commercial bank funding. This in turn limits the size of women owned businesses and have implications for the kinds of sectors in which they operate, which are more service oriented sectors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| 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".