Strengthening Formal Credit Access and Performance through Financial Literacy and Credit Terms in Micro, Small and Medium Businesses
Bibliographic record
Abstract
This study aims to test financial literacy and credit conditions in determining formal credit access to determine the performance of MSMEs. This research includes the type of associative research that is accompanied by hypothesis testing. This research was conducted on MSMEs of as many as 324 creative industry players in four cities in East Java (Mojokerto, Pasuruan, Gresik, and Sidoarjo) with a sample size of 100 actors who had accessed formal credit using the stratified random sampling method for data collection. The results of Smart PLS analysis show that financial literacy and credit terms directly and significantly affect access to formal credit and MSME performance; formal credit access directly and significantly affects MSME performance. Likewise, financial literacy and credit terms indirectly affect the performance of MSMEs. These results mean that financial literacy and credit terms have a strategic role in explaining why access to formal credit is growing and is attracting MSMEs to strengthen capital to improve performance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".