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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".