Determinants of the Commercial Bank’s Efficiency in ASEAN
Bibliographic record
Abstract
The purpose of this study is to analyze the influence of capitalization, bank size, bank age, and loan to asset ratio (LAR) to bank efficiency in ASEAN-5 countries (Singapore, Indonesia, Thailand, Malaysia, and the Philippines). Net interest margin (NIM) and non-net interest income (Non-NIM) were used as control variables. There was a total of 58 banks used as a sample using a purposive sampling technique. There were two stages of the analytical method used: data envelopment analysis (DEA) approach – to provide estimates of bank efficiency, and multiple regression linear – consists of the statistical F-test and t-test, coefficient of determination (R2) test and the classic assumption test. The results show that capitalization and bank age affect bank efficiency negatively, while bank size and LAR affect bank efficiency positively. The banks are suggested to consider optimizing their capital to continue to operate efficiently, increase their assets to be more efficient, the older banks are expected to be able to adjust to technological developments, and the banks are also expected to increase the amount of credit by monitoring its quality to be efficient.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".