The Moderating Effect of the COVID-19 Pandemic on the Relation between Corporate Governance and Firm Performance
Bibliographic record
Abstract
The present study aims to investigate the association between corporate governance mechanisms and financial performance among companies listed on the Tehran Stock Exchange (TSE). We also want to know if the COVID-19 global crisis moderates the relationship between them. The study sample consists of 1098 observations and 183 companies listed on the TSE from 2016 to 2021; furthermore, the statistical method used to test the hypotheses is panel data with random effects. In line with our expectations, the results show that the coronavirus pandemic worsened Iranian corporate performance. In support of agency theory, we figure out that board independence, board meeting frequency, and board financial expertise are correlated positively with firm value. In favor of resource dependency theory, this study finds robust evidence that audit committee size and independence have a positive effect on corporate performance. Most importantly, the positive linkage between board independence, board financial expertise, size, and independence of audit committee with firm performance was reversed during the COVID-19 pandemic, although the positive role of board meeting frequency in corporate profitability remained stable even during the COVID-19 outbreak. Furthermore, the outcomes indicate that CEO duality affects firms negatively, and this devastating effect became even stronger with the COVID-19 pandemic. Finally, we find that firms involved in mergers and acquisitions (M&A) managed to increase shareholders’ wealth using competitive advantage even during the 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".