Islamic banking contribution in sustainable socioeconomic development in Indonesia
Bibliographic record
Abstract
Purpose As a prominent actor in terms of achieving sustainable socioeconomic development, especially in rural areas, Islamic banks are urged to pursue their main objective. It is required to set the objective accordingly from time-to-time to continuously make a positive contribution to the sustainable socioeconomic development. Hence, the integration of the external factors such as government’s economic target (macro) into Islamic banking’s objectives (micro) is needed. Design/methodology/approach This research attempts to identify factors that might prevent the sustainable economic development activities within the micro–macro circular causal model established by Tawhidi String Relation (TSR) methodology. Findings The research clearly found that the existing Islamic banking’s business and directions had an uncorrelated connection with Indonesia’s economic objective. Nevertheless, the Islamic banking’s Musharakah and Mudharabah contract for Usaha Mikro Kecil Menengah [(UMKM) (Micro, Small Medium Enterprises)] was showing the positive correlation to their financial performance indicator. Hence, Islamic banking is strongly suggested to be more focused on these two types of partnership financing contract to UMKM. Furthermore, its value and volume is needed to be expanded to build Indonesia’s sustainable socioeconomic foundation. Then the positive gross domestic product (GDP) growth will be achieved. Originality/value The existing research covering the sustainability index is mainly only based on the macro perspective, while in this research, the integration between the micro and macro perspectives between government objectives and Islamic banking objectives is needed. This interaction and integration between the two are in line with the concept of the TSR methodology.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".