Factors Affecting Corporate Social Responsibility Web Disclosure: Evidence from the Consumer Sector in Indonesia
Bibliographic record
Abstract
An increasing number of companies disclose information about their financial performance, however, publishing Corporate Social Responsibility information on company’s websites is still limited. This study examines the factors affecting Corporate Social Responsibility (CSR) web disclosure, as well as examine whether firms with high CSR web disclosure are less likely to engage in earnings management. Corporate Social Responsibility may indicate transparent and reliable financial statements. To do so, we investigate factors that affect the extensiveness of CSR web disclosure. In addition, we investigate the impact financial statement quality on CSR web disclosure. Based on a content analysis of the CSR web disclosure of consumer sector of Indonesia listed companies, this research analyzes the content of CSR disclosures with respect to the following four themes, include environmental information, employee information, community involvement information, and products information areas. Companies publish their CSR disclosure on company’s websites only during the year, therefore we could not collect data on its prior year. Using a sample of 94 consumer sector companies listed on the Indonesia Stock Exchange in 2020, our OLS regression results indicate that company size has a significant effect on CSR web disclosure. In contrast, there is no association between public share ownership, board of commissioner size, audit committee size, profitability, and leverage, on CSR web disclosure. We find no evidence of companies with high-level CSR web disclosure publishing more transparent and reliable financial statement than companies with low-level CSR web disclosure.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".