Bibliographic record
Abstract
This study explores the transformative role of financial technology (fintech) in advancing sustainability, financial inclusion, and customer engagement in Jordan’s banking sector. Utilizing a quantitative descriptive survey design, data were collected from 400 participants—comprising 300 bank customers and 100 banking professionals—through a structured bilingual questionnaire distributed via digital platforms. The study aims to evaluate how fintech innovations align with sustainable finance practices, extend banking access to underserved populations, and influence customer satisfaction. The results reveal strong evidence of fintech’s positive impact across all three domains. Regression analysis confirmed a statistically significant relationship between fintech innovation and the adoption of sustainable finance practices (β = 0.6498, p < 0.001), explaining 42.2% of the variance in sustainability outcomes. Similarly, fintech adoption was found to significantly improve financial inclusion among underserved populations (β = 0.6842, p < 0.001), accounting for 46.85% of the variance in access to services. One-way ANOVA analysis further showed that increased fintech integration significantly enhances customer engagement, with mean satisfaction scores rising progressively with higher fintech usage levels (F = 24.49, p < 0.001). The study underscores that fintech is a critical enabler of ethical banking transformation in Jordan, promoting ESG objectives, reducing financial access disparities, and strengthening customer loyalty. The findings confirm that fintech significantly contributes to sustainable, inclusive, and customer-centric banking practices. These insights support the notion that fintech adoption not only redefines banking operations but also charts a sustainable and socially responsible future for the Jordanian financial sector.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".