Implications of the Joint Provision of CSR Assurance and Financial Audit for Auditors' Assessment of Going‐Concern Risk
Bibliographic record
Abstract
ABSTRACT We examine whether the joint provision of corporate social responsibility (CSR) assurance services and financial audit by the same audit firm influences auditors' assessment of going‐concern risk. We predict that the provision of CSR assurance and financial audit by the same audit firm creates CSR‐related knowledge spillovers from the CSR assurance team to the financial audit engagement team, which helps in the auditor's assessment of going‐concern risk. Using more than 28,000 firm‐year observations from 55 countries, we document that, relative to audit firms that provide only the financial audit, audit firms that provide both CSR assurance and financial audit for the same client (i) issue more frequent going‐concern opinions and have lower Type II going‐concern errors, (ii) have clients that book larger environmental and litigation provisions, (iii) report earnings that are more persistent and value‐relevant and are less likely to book income‐decreasing earnings restatements, and (iv) do not charge higher audit fees or total fees. Our results are important especially because of firms' increasing exposure to CSR risks and the growing number of countries that require assurance of CSR reports.
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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 | 0.029 | 0.154 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".