Corporate social responsibility and accounting fraud: international evidence
Bibliographic record
Abstract
Purpose This study aims to trace the impact of corporate social responsibility (CSR), and its social, environmental and governance dimensions, on accounting fraud. Design/methodology/approach ASSET4 data from 793 French, Canadian, British and German companies were collected over a 12-year period, from 2010–2021, and assumptions were examined using a logistic regression model. The adjusted model of Beneish (1999) was used to measure accounting fraud. Findings The results reveal that strong CSR significantly lowers the likelihood of corporate fraud, with each CSR dimension showing a negative and significant relationship with fraudulent behavior. The governance dimension consistently shows the strongest impact in reducing fraud. In addition, the COVID-19 pandemic notably decreased fraud in some countries, such as the UK, highlighting CSR’s enhanced preventive role during crises. However, this moderating effect varied across countries. Further robustness analyses affirmed the stability and applicability of these results across various model specifications. Practical implications This study highlights the vital role of CSR, especially its governance dimension, in reducing accounting fraud, offering key insights for investors, auditors and regulators. It underscores the need to integrate ethical principles into governance, particularly in times of crisis like the COVID-19 pandemic. Future research could examine how national institutional contexts influence CSR’s effectiveness in preventing fraud. Originality/value The results of this study provide valuable information on the importance of strengthening engagement in CSR activities to prevent companies from engaging in fraudulent activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".