Contribution of Islamic Commercial Bank Financing to East Java Economic Growth in the Era of Branchless Banking
Bibliographic record
Abstract
This study aims to determine and analyze the contribution of Islamic Commercial Bank’s (BUS) financing to the economic growth of East Java Province in the era of branchless banking. Three types of financing channeled by the BUS namely working capital financing, investment and consumption are used as the independent variables tested each effect on the dependent variable which is economic growth in East Java with a proxy of GDRP in the period of the quarter-I 2010 to quarter-I 2020. Ordinary Least Square (OLS) with dummy variable of branchless banking (0 = before the implementation of the branchless banking program (before November 2014) and 1 = after the implementation of the branchless banking program (after November 2014) is used as data analysis technique with the results of the study show that only consumer financing that have a positive and significant impact on economic growth of East Java. Whereas, the productive financing known to have positive impact but not significant toward the economic growth of East Java. Meanwhile, the branchless banking program known to give the positive and significant difference impact on economic growth in East Java compared to economic growth prior to the enactment of it. The results of this study beneficial for both the BUS and the regulator as an evaluation of the level of inclusiveness of the BUS’s financing to economic growth and the implementation of branchless banking in Islamic bank.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".