An Investigation of the Link between Major Shareholders’ Behavior and Corporate Governance Performance before and after the COVID-19 Pandemic: A Case Study of the Companies Listed on the Iranian Stock Market
Bibliographic record
Abstract
One of the basic functions of establishing corporate governance (CG) in companies is improving performance and increasing value for shareholders. Expanding the company’s value will ultimately increase the shareholders’ wealth. Therefore, it is natural for shareholders to seek to improve their performance and increase the company’s value. If CG mechanisms cannot perform this function in companies, they do not have the necessary efficiency and effectiveness and, therefore, cannot improve the efficiency of companies. This article investigated the connection between the power of major shareholders and the modality of CG of companies listed on the Iranian capital market before and after the COVID-19 pandemic. The statistical sample of the research included 120 companies listed on the Tehran Stock Exchange for the selected period from 2011 to 2021. The results showed that the concentration of ownership is harmful to adopting corporate governance (GCG) practices. In particular, the high level of voter ownership concentration weakens the corporate governance system (CGS). The results of this study, which was conducted using panel analysis, revealed that the concentration of ownership impairs the quality of CGS, and major shareholders cannot challenge the power of the main shareholder; it alsonegatively affected the quality of business boards, both during and before the COVID-19 pandemic. The competitiveness and voting rights of the major shareholders negatively affected the quality of board composition before and after the COVID-19 pandemic. The concentration of voter ownership also negatively affected the quality of CGS, both during and before COVID-19, and the competitiveness and voting rights of major shareholders before COVID-19. This concentration positively affected the quality of CGS after the COVID-19 pandemic.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".