Do Corporate Social Responsibility and Political Connections Matter to Financial Performance and Financial Stability in the Banking Sector? Evidence from Indonesia
Bibliographic record
Abstract
This study aims to determine the effect of Corporate Social Responsibility and political connections on financial performance and financial stability in the banking sector in Indonesia. Corporate Social Responsibility is widely seen as a form of the company's commitment to society, which can encourage sustainability. Meanwhile, political connections are seen as capable of maintaining the financial stability of banking companies, especially in countries with high levels of corruption and weak laws. The sample in this study were 26 banking companies listed on the Indonesia Stock Exchange for the period 2017-2020. The method used in sampling is purposive sampling method, with secondary data in the form of financial statements and company annual reports during the study period. This study uses a combined least squares regression analysis technique. The results showed that Corporate Social Responsibility had a positive effect on financial performance and had no effect on financial stability, while political connections had a negative effect on both financial performance and financial stability. This shows that banks that have political connections do not make people more trusting. Thus, the company's image in society becomes more important than political connections.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".