Fintech Adoption and Banks’ Non-Financial Performance: Do Circular Economy Practices Matter?
Bibliographic record
Abstract
This study draws insights from practice-based view theory (PBV) to investigate the impact of fintech adoption (FA) on the non-financial performance (NFP) of banking institutions in developing countries, considering the mediating role of circular economy practices (CEPs). A structured questionnaire was distributed to collect primary data from banks’ staff in Iraq, Egypt, Oman, and Jordan using a convenience sampling method with a sample size of 397. Subsequently, the structural equation model was utilized to test the research hypotheses of the proposed conceptual model. The study’s findings revealed that FA positively and significantly impacts CEPs and banks’ NFP (customer satisfaction, internal processes, and learning and growth perspectives). Moreover, CEPs mediate the relationship between FA and banks’ NFP in a positive and significant way. Given the dearth of the literature, this is the first study to fill the research gaps by investigating the impact of FA on the NFP of banking institutions in developing countries, considering CEPs as a mediator, and yielding critical theoretical and practical implications. The study’s findings provide banks’ managers with valuable insights about how to enhance their NFP through FA and CEPs during and after crises and support policymakers and regulators in developing a legislative framework that guides banks to invest in CE models and provides reward systems to encourage them.
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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 | 0.006 | 0.020 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".