Fostering poverty reduction through ultra-microfinance interventions for agricultural MSES in Indonesia: The role of business size and gender
Bibliographic record
Abstract
Studies on the role of gender, family businesses, and non-family businesses in examining financial management practices, particularly in developing countries, have been largely overlooked. This research, based on the collaboration between Positive Accounting Theory (PAT) and Agency Theory, aims to explore the intervention of microfinance in micro and small agricultural enterprises (MSEs). Using a quantitative approach, the study employs the Structural Equation Model Partial Least Square (SEM-PLS) method with a sample size of 656 respondents, comprising ultra-micro and small business actors in the agricultural sector in Indonesia. The findings indicate that for micro-sized businesses, entrepreneurial success mediates the relationship between microfinance and subjective wellbeing, while in small-sized businesses, poverty reduction and entrepreneurial success fully mediate the relationship between ultra-microfinance and subjective wellbeing. Ultra-microfinance assists business owners in enhancing their financial resource capacity more easily. On the other hand, microfinance not only provides financial access but also offers training and mentorship programs, helping business owners achieve long-term success. The study also reveals that gender does not mediate the relationship between microfinance and subjective wellbeing. Gender inequality in accessing resources, differences in decision-making participation, and social norms that still limit women's roles in economic activities within the agricultural sector are contributing factors. The implication of this study is to provide insights for decision-makers in the agricultural MSME sector to enhance subjective wellbeing. It is hoped that poverty alleviation programs through microfinance initiatives, such as PNPM, can be optimally absorbed and have a positive impact on empowering business owners to reduce structural poverty, especially in the key sector of agricultural businesses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".