The impact of bank financing and internal financing sources on women’s motivation for e-entrepreneurship
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the impact of bank financing and internal financing sources on women’s motivation for e-entrepreneurship. Design/methodology/approach Female owners of e-businesses in India were surveyed regarding their perceptions of bank financing, internal financing sources and their motivations for e-entrepreneurship. Findings The findings of this study show that bank financing and internal financing sources positively impact women’s motivation for e-entrepreneurship in India. The results show that family status, education, easy access to new business information and location positively impact women’s motivation for e-entrepreneurship in India. The findings also show that bank financing has a higher impact on women’s motivation for e-entrepreneurship compared with internal financing sources. Research limitations/implications This is a co-relational study that investigated the relationship between bank financing and women’s motivation for e-entrepreneurship and the relationship between internal financing sources and women’s motivation for e-entrepreneurship. There is not necessarily a causal relationship between the two. The findings of this study may only be generalized to individuals similar to those that were included in this research. Originality/value This study contributes to the literature on the impact of bank financing and internal financing sources on women’s motivation for e-entrepreneurship. The findings may be useful for investment advisors, the Indian Government and entrepreneurship consultants.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".