The Dynamics of Financial Innovation and Bank Performance: Evidence from the Tunisian Banking Sector Using a Mixed-Methods Approach
Bibliographic record
Abstract
This study investigates the interactive link between bank performance and financial innovation in Tunisian banking using a mixed-methods research framework that combines econometric approaches and institutional factors. The empirical analysis uses a panel data of 11 commercial banks from the period of 2000–2024 and employs an Autoregressive distributed lag (ARDL) model to estimate short- and long-run impacts of innovation on return on equity (ROE). A composite indicator of Fintech investment, digital service adoption, and innovation productivity characterizes financial innovation. Governance factors like the presence of risk management departments and executive compensation are taken into account. The results reveal a robust positive impact of financial innovation on bank performance in the long run, especially in more concentrated market settings. Risk management supports performance, while inefficient executive compensation is negatively associated with profitability. These findings are confirmed by robustness tests with HAC standard errors. This research contributes to the literature by situating financial innovation in the context of an emerging North African market and produces practitioner-relevant information for policymakers and bank executives interested in ensuring that performance results are consistent with innovation strategy.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".