Investigating the Nexus between Corporate Governance and Firm Performance in India: Evidence from COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has had a dreadful influence on both economic activities and human life, in view of which management has to play a strategic role to focus on effective board leadership in order to optimize firm performance. The present study analyses the role of corporate governance practices in determining firm performance during the pandemic. A total of 151 non-financial companies from 11 diversified industries representing the NIFTY200 index for two years, 2019–2020 (pre-COVID-19) and 2020–2021 (duringCOVID-19), were selected. Paired sample t-tests, panel data regression, and one-way ANOVA were used for the analysis. The findings confirm that there is a significant difference between some corporate governance practices (board size, board independence, board’s female proportion, board attendance, and audit committee size) as well as financial performance (Tobin’s Q) before and during the COVID-19 period. The regression results of the full sample show that only board busyness has a positive and significant impact on ROA and Tobin’s Q. However, after splitting the sample year-wise, board size and audit committee meetings positively affected ROA during COVID-19. On the other hand, board independence had a negative influence. Female directors and audit committee meetings positively affected ROA in the pre-COVID-19 period, while board busyness had a negative influence. The results of one-way ANOVA show a substantial difference in the financial performance among industries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".