Social capital of non-resident family members and small business financing
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the impact of social capital of non-resident family members on small business debt financing. Recent literature in entrepreneurship suggests that small businesses can borrow social capital to improve their access to debt financing. Design/methodology/approach Micro-entrepreneurs from India were interviewed regarding their ability to raise capital from family members as well as their relationship with banks and politicians. Findings The survey indicates that small business entrepreneurs are able to borrow social capital from non-resident Indians. Results also suggest that these small businesses are more likely to be connected to banks and politicians facilitated by their non-resident family members, which not only improves micro-entrepreneurs’ access to debt financing but also reduces their cost of borrowing. Research limitations/implications This is a co-relational study that investigates the association between social capital of non-resident family members and small business debt financing. There is not necessarily a causal relationship between the two. The findings of this study may only be generalized to firms similar to those that were included in this research. Originality/value This study contributes to the literature on the factors that improve the access to small business debt financing. The findings may be useful for financial managers, investors, financial management consultants, entrepreneurs, and other stakeholders.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".