CSR, monitoring cost and firm performance during COVID-19: balancing organizational legitimacy and agency cost
Bibliographic record
Abstract
Purpose COVID-19 induced uncertainty in the firms’ business transactions, financial markets and product-market competition, causing a severe organizational legitimacy crisis. Using the organizational legitimacy perspective and agency theory, this paper aims to study the relationship between prior corporate social responsibility (CSR) activities, monitoring cost (MC) and firm performance. Design/methodology/approach This study uses a quarterly panel (16,924 firm-quarter observations from 61 countries for CSR and 53,345 firm-quarter observations from 55 countries for MC) for 14 quarters from January 2018 to June 2021. This study uses panel fixed-effect regression models to estimate the effect of CSR activities and MC (measured as audit fees) on firm performance during the COVID-19 period. Findings This study finds a U-shaped relationship between CSR and firm performance. This relationship is strengthened during COVID-19. In contrast, this study finds an inverted U-shaped relationship between firm MC and firm performance. However, this relationship is weakened during the pandemic. Originality/value This study contributes to theory and practice on maintaining organizational legitimacy and reducing agency costs during the pandemic. This study shows that firms’ prior legitimacy-gaining practices, such as CSR activities and MC, provide an opportunity to increase firm value. To balance agency costs and legitimacy benefits, firm managers also need to identify the optimal level of CSR activities and MC.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".