Impact of Corporate Governance on Value Creation and Corporate Productivity: Evidence from Tunisian Context
Bibliographic record
Abstract
The governance practices and the value of the company have ancient origins but still relevant. Faced with economic, social and environmental upheavals, the Tunisian company is subject to contradictory injunctions and must redefine its role in society. Today there is a general consensus that governance has a significant impact on the value of the company. The areas on which governance can act to create value are many and varied and there can be no question of looking at them exhaustively. The purpose of this paper is to examine the impact of governance mechanisms on the value creation of Tunisian firms, particularly measured by total factor productivity. Based on a sample of 28 corporations listed on Tunisian stock market during the period 2008-2014, our study employs regression analysis to test the impact of governance mechanisms on firm value as measured by productivity. As predicted by the theory, empirical data show (i) that debts have a negative and significant effect on productivity such as measured by the ratio of total debt to total assets. (ii) the presence of institutional investors on boards of directors also increases the value of the company. (iii) However, contrary to previous studies, the duality that combines the functions of the Chairman of the Board and the Chief Executive Officer has no effect on the value creation of Tunisian companies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".