Digital Transformation's Moderating Role on Financing and Capital Quality Impacts for Sustainable Islamic Rural Banking in Indonesia
Bibliographic record
Abstract
This investigation explores the moderating role of digital transformation on the impact of financing and capital quality on the sustainability of business practices within Islamic rural banks in Indonesia.Data were collected from the financial and annual reports of 165 Islamic rural banks across the nation, with a focused sample of 30 banks in the West Java region, spanning the years 2016 to 2021.The analysis, conducted through EViews version 10, employed multiple linear regression analysis on panel data to ascertain the relationships in question.It was found that non-performing financing (NPF) exerts a significant adverse effect on the sustainability of these banks' operations, whereas a positive influence is observed in the case of the capital adequacy ratio (CAR).Furthermore, digital transformation was identified as a critical moderating factor, enhancing the negative impact of NPF and bolstering the positive impact of CAR on business sustainability.The findings suggest that Islamic rural banks in Indonesia embarking on digital transformation initiatives must prioritize information transparency, financial stability, and the cultivation of innovative capabilities.Additionally, the selection of digital transformation strategies should be tailored to the banks' unique characteristics, including property rights, operational scale, and growth potential.This study contributes to the literature by providing empirical evidence of the significant role digital transformation plays in influencing the relationship between financial health indicators and sustainability in the context of Islamic rural banking.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".