Firm Performance during COVID-19 Pandemic: Does Ownership Identity Matter? Evidence from Indonesia
Bibliographic record
Abstract
This study aimed to examine the importance of shareholder identity in improving company performance during shock events such as the COVID-19 pandemic. The outbreak poses threats and opportunities for businesses in various countries including Indonesia. Subsequently, companies must adapt to address the consequences of the economic disruption and lockdown policies imposed by the local government. The study sample comprised companies listed on the Indonesia Stock Exchange (IDX) during the COVID-19 pandemic from 2020 to 2021. Fixed effects model regression was employed to examine the effect of family, government, and institutional ownership on company performance. The results showed that family and institutional ownership positively affected company performance during the pandemic. The mechanisms of direct supervision and control by family members could potentially increase the benefits of their businesses. Furthermore, high institutional ownership makes the role of investors substantial in reducing business risk and increasing company performance. Furthermore, the results revealed that government ownership negatively affected company performance. As owners, the government has different strategic objectives, where companies are more oriented toward better public services than financial gains. Therefore, it is essential to consider the impact of shareholder involvement on company performance, especially during a pandemic because they are treated differently. The research suggests that organizations are responding and adapting to the uncertainties in the business environment they face through a variety of mechanisms, including developing public and corporate governance strategies to prepare for and respond to future emergencies.
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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 | 0.002 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".