Effect of FinTech Adoption, Green Finance and Green Innovation on Sustainability Performance of Nepalese Financial Institutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study is aimed to investigate the impact of FinTech adoption on the sustainability performance of financial institutions of Nepal focusing the mediating role of green finance and green innovation. Employing a descriptive and causal comparative research design, the research systematically describes the characteristics of FinTech adoption among customers of financial institutions of the selected area and explores potential cause-and-effect relationships between FinTech adoption and sustainability performance. Data were collected from 180 respondents through a structured questionnaire distributed via an online survey platform, utilizing a 5-point Likert scale. Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM) were used to test the measurement model and examine the relationships between the constructs. The study confirmed that FinTech adoption positively impacts sustainability performance and green finance, although it negatively influences green innovation. Mediation analysis revealed that neither green finance nor green innovation significantly mediate the relationship between FinTech adoption and sustainability performance. The findings underscore the necessity for financial institutions to integrate both technological advancements and sustainable practices comprehensively. This study contributes to the academic literature by providing empirical evidence from Nepal and offers practical insights for policymakers and financial institutions aiming to enhance sustainability through fintech. By addressing critical research gaps, this study advances our understanding of how FinTech can be effectively aligned with sustainable financial practices in Nepal, ensuring long-term environmental, social, and economic benefits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it