MSME’s Financing Decisions and the Roles of Financial Literacy and Microbusiness Demographics
Bibliographic record
Abstract
Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in the Indonesian economy, contributing 60.5% to the GDP, employing 96.9% of the workforce, and accounting for 15.69% of exports. However, MSMEs still face capital problems. Many of them struggle to access financial institutions due to their limited financial literacy. This research aims to identify the level of financial literacy among microbusinesses. The survey involved 60 respondents in the Cibinong District of Bogor Regency and analyzed how financial literacy levels and demographic factors influence microbusinesses’ decisions regarding the use of financing products from micro financial institutions (MFIs). The analytical method employed in this study is logistic regression. The research results indicate that financial attitudes, education, and income have a significant positive impact on financing decision from micro financial institutions, while financial knowledge and behavior have a significant negative impact. Keywords: MSME, MFI, financial literacy, demography, micro businesses ABSTRAK Usaha Mikro, Kecil dan Menengah (UMKM) memiliki peran penting dalam perekonomian Indonesia dengan kontribusi terhadap PDB 60,5%, penyerapan tenaga kerja 96,9%, dan ekspor 15,69%. Namun, UMKM masih menghadapi masalah permodalan. Banyak UMKM masih belum mampu mengakses lembaga keuangan karena literasi keuangannya yang rendah. Penelitian ini bertujuan mengidentifikasi tingkat literasi keuangan usaha mikro, dengan menyebarkan kuesioner kepada 60 responden di Kecamatan Cibinong, Kabupaten Bogor dan menganalisis pengaruh tingkat literasi serta faktor demografi terhadap keputusan usaha mikro dalam menggunakan produk pembiayaan di lembaga keuangan mikro (LKM). Metode analisis yang digunakan adalah regresi logistik. Hasil penelitian menunjukkan bahwa sikap keuangan, pendidikan, dan pendapatan berpengaruh signifikan positif terhadap pengambilan keputusan menggunakan produk pembiayaan lembaga keuangan mikro, sementara pengetahuan dan perilaku keuangan berpengaruh signifikan negatif. Kata kunci: UMKM, LKM, literasi keuangan, demografi, bisnis mikro
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".