Mapping the global regulatory terrain in digital banking: a longitudinal study across countries
Bibliographic record
Abstract
Purpose This study aims to comprehensively understand the regulatory landscape and digital transformation (DT) within the banking sector, anchored in the theory of national innovation systems. Design/methodology/approach Using insights from a comprehensive literature review, an innovative framework is introduced to categorize regulators and digital banking attributes across 88 countries. The study uses k-means clustering to analyze the digital banking and regulatory status of 88 countries, tracing their evolution over two distinct timeframes. Findings The cross-country analysis spanning 2014 and 2022 reveals compelling trends in regulatory rankings and digital banking across diverse nations. These findings shed light on the dynamic interplay between regulatory environments and technological innovation. Originality/value This research contributes to knowledge by establishing a robust framework for understanding regulator dynamics in digital banking across a wide spectrum of countries. It offers valuable insights for academia, practitioners and policymakers by elucidating the complex relationship between the regulatory landscape and DT, shaping discourse and implications in this field, and informing strategic decision-making and policy formulation in the global financial landscape.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".