Does Corporate Social Responsibility Affect the Timeliness of Audited Financial Information? Evidence from “100 Best Corporate Citizens”
Bibliographic record
Abstract
Companies are under immense pressure to integrate activities that will improve society and the environment with their business objectives. Such integration is likely to introduce complexity into the firms’ activities and impact the timeliness of the financial statements. Audit report lag is significant to investors as it directly impacts investor decision-making and investment fortunes. This study examines the association between corporate social responsibility (CSR) and audit report lag. We measure CSR activities using a composite variable representing a firm’s inclusion on or exclusion from the annual list of “100 Best Corporate Citizens.” In the robust regression analyses with a sample of 3661 firm-year observations from 2011 to 2016, we found a positive and significant association between CSR activities and audit report lag after controlling for extraneous variables potentially influencing audit report lag. Furthermore, the additional results with the six CSR components in the list confirm our finding that, except for governance, all the other components, such as environment, climate change, human rights, employee relations, and philanthropy, have a positive and significant association with audit report lag. Our findings suggest that CSR activities introduce audit complexities and risks that compel auditors to assess a high risk of material misstatements, translating into more audit effort and longer times to complete audits.
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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 | 0.009 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".