Quantitative analysis of the impact of electronic banking on the financial performance of rural banks in Indonesia
Bibliographic record
Abstract
The rapid growth of electronic transactions has transformed the landscape of many industries, especially in the banking sector. This digitalization growth has brought about the need to understand the impact of the adoption of electronic banking in rural banks on the financial performance of rural banks in Indonesia. The aim of this research is to analyze the extent to which the adoption of e-banking influences consumer electronic trust (e-trust) and financial performance. The research method used is quantitative, involving data collection through questionnaires. Respondents in this study are managers of rural banks in West Java, Indonesia. The sample size used is 200 participants. Data collection took place over two months from May to June 2023. Data analysis was performed using Structural Equation Modeling (SEM) through SmartPLS 4 software. The research results indicate that the adoption of e-banking has a positive and significant impact on consumer e-trust and financial performance. Consumer e-trust has also been proven to mediate the relationship between e-banking adoption and financial performance. These findings provide profound insights into how e-banking plays a key role in building consumer e-trust and enhancing the financial performance of banking institutions. This research makes a significant contribution in the context of developing e-banking strategies in the banking sector. The implications of these findings can guide policies, strategies, and innovations in the banking sector to optimize the benefits of electronic banking, build consumer e-trust, and enhance financial performance in this digital era.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".