Internal Governance Mechanisms and Commercial Performance of Tunisian Financial Institutions
Bibliographic record
Abstract
Purpose: Financial institutions are always in touch with customers, therefore, many support for commercial performance needs to be addressed in order to be more competitive. Then, this research is an attempt to present some aspects of this performance. Indeed, the performance of companies represents a very varied field of study. And, through its mechanisms, governance is a tool for improving performance. The purpose of this article is to inspect the effects of internal governance mechanisms on the commercial performance of Tunisian financial institutions. Result: Data econometrics is used to study a sample of 34 financial institutions. We find that the results dealing with the impact of governance on commercial performance are mixed. Some mechanisms have a positive effect while others have a negative effect. Only the variables “effectiveness of the board, confidence, organizational culture and importanceof the existence of an auditing” has positively and significantly affected the commercial performance of Tunisian financial institutions. On the other hand, the variable “incentive system of managers by the remuneration” negatively affects this performance. Originality/value: Theoretically, the commercial performance of Tunisian financial institutions is an extension and a supplement to the literature on financial institutions. This article is, therefore, a source of ideas and information on the Tunisian financial sector. And, empirically, this study is an attempt to assess the existing association between commercial performance and internal governance mechanisms in the Tunisian financial sector.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".