Firm Complexity and the Accuracy of Auditors’ Going Concern Opinions in Emerging Markets: Does Auditor Work Stress Matter?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines the direct and indirect effects of firm complexity on the accuracy of auditors’ going concern opinion (GCAO), and whether and how auditors’ work stress (AWS) can serve as a mediating variable in such a relationship. We analyzed a sample of 705 firm-year observations from 105 non-financial firms listed on the Egyptian Stock Exchange between 2017 and 2023. Binary logistic regression, OLS regression, and path analysis were employed to test the study hypotheses. The results suggested that firm complexity is negatively associated with GCAO accuracy but positively associated with AWS. Furthermore, a negative relationship was observed between AWS and GCAO accuracy. Finally, the analysis revealed that AWS mediates the relationship between firm complexity and GCAO accuracy. The findings remained robust across various sensitivity tests. Policymakers, audit firms, and investors can benefit from the findings, which emphasize the necessity of AWS mitigation techniques to improve GCAO accuracy and ultimately contribute to transparent financial reporting. This study provides unique evidence from a developing country on how firm complexity can indirectly impact the quality of auditors’ judgments.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it