Impact of corporate donations to combat Covid-19 on the market value of donor companies: an analysis from the perspective of legitimacy theory
Bibliographic record
Abstract
Purpose: The objective of the study is to verify the impact of corporate donations aimed at combating Covid-19 on the market value of the involved companies. Methodology: We employ the event study methodology, examining the returns of company shares on the dates when corporate donations were announced. Results: Results indicate an increase in the standard deviation of daily returns of Itaú, Vale and JBS shares compared to Ibovespa. For Itaú, three statistically significant abnormal returns occurred during the event window, with two being negative and only one positive, thus the negatives prevailed. In the case of Vale, there was one significant abnormal return of each kind prior to the event, and one positive return on the event day. For JBS, five statistically significant abnormal returns were observed during the event window, with three being positive and two negatives. Therefore, the hypothesis that corporate donations during the Covid-19 pandemic positively impact the market value of donors was partially confirmed for Vale and JBS, but not for Itaú. Contributions of the Study: The study concludes that acts of corporate social responsibility (CSR) positively impact the market value of the practicing companies. Additionally, it suggests that the impact on market value varies among companies based on the nature of the threat to their legitimacy, with those facing threats originated from individual responsibility showing a positive impact.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".