DIGITALIZATION, BANKING COMPETITION, AND CREDIT RISK AS ANTECEDENTS OF BANK STABILITY MEDIATED BY PROFITABILITY
Bibliographic record
Abstract
This study aims to analyze the effects of digitalization, banking competition, and credit risk on bank stability, with profitability as a mediating variable in conventional banking in Indonesia. This study employs a quantitative research design using hypothesis testing to examine the relationships between independent and dependent variables. The sample is selected using purposive sampling, consisting of conventional banks, including state-owned banks, private banks, regional development banks, and foreign banks. The observation period covers quarterly data from the first quarter of 2019 to the second quarter of 2025. Using panel data, a total of 2,366 observations are obtained. The analytical method applied is panel regression. The results indicate that digitalization does not have a direct effect on bank stability, but it has a positive effect on profitability. Profitability is found to have a positive effect on bank stability and mediates the relationship between digitalization and stability. Banking competition positively affects bank stability but does not significantly influence profitability. Profitability does not mediate the relationship between competition and stability. Credit risk does not directly affect bank stability and does not significantly influence overall profitability. However, when examined through individual profitability measures, credit risk shows a positive effect on profitability. Furthermore, profitability does not mediate the relationship between credit risk and bank stability when measured in aggregate, but partial mediation is observed through specific profitability indicators. The findings suggest that digitalization should be positioned as a strategic tool to enhance profitability, which in turn strengthens bank stability. Banking competition needs to be effectively managed to maintain systemic stability, while credit risk remains a critical instrument in supporting profitability and ensuring long-term sustainability in the banking industry. Digitalization, Banking Competition, Profitability, Credit Risk, Bank Stability
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".