The influence of some fintech service on the performance of Islamic bank in Jordan
Bibliographic record
Abstract
Islamic banking across the developing countries may undoubtedly become more prevalent thanks to financial technology, making it more competitive in the financial sector. The current study's goal is to extensively address fintech on Islamic banks and to examine the effect of fintech Services upon Islamic banking financial performance around Jordan. The investigation utilized a quantitative-descriptive survey approach. The study utilized annual data (panel data) which were acquired via financial institutions figures of the annual statements from the Jordan's Islamic Bank (JIB) registered with Amman Stock Exchange from 2017 to 2021. This investigation found Fintech services, such as internet banking, mobile banking, crowdfunding, and automated teller machines had a substantial effect on JIB's financial performance. The positive beta value suggests that there were favorable relationships among JIB's financial success from 2017 to 2021 on Fintech services. Finally, the study recommends that JIB increase its efforts to inform the public concerning Islamic banking services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".