Determinants of financial performance and Islamic social reporting: Evidence from Indonesian Islamic banks
Bibliographic record
Abstract
This research examined the connections of efficiency, regulatory and good corporate governance (GCG) variables on the achievement or performance of financial aspects and social reporting from Indonesia’s Shariah banks generally. Data and samples collected in this research include 34 Shariah banks, which are administered in the central bank, Bank of Indonesia for the year of 2009 until 2022. The financial achievement was determined with return on assets, and social reporting was determined with dummy 1 if the banks issue Islamic social reporting, and 0 otherwise. Regulation variables were measured with nonperforming financing (NPF), capital adequacy ratio (CAR), financial to deposit ratio (FDR), and net operating margin (NOM). Corporate governance variables were measured with firm age, board education, and board meeting. Efficiency variable was assessed with operating costs to revenue. The results show that regulation variables significantly impact financial achievement and social reporting, except NPF and CAR have nothing significant influence on Islamic social reporting. Efficiency variables significantly impact Islamic social reporting and financial achievement. Corporate governance variables significantly influence financial achievement and Islamic social reporting. Meanwhile firm age and board meetings have no remarkable influence on financial achievement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".