Auditors’ Perceptions of the Triggers and Obstacles of Continuous Auditing and Its Impact on Auditor Independence: Insights from Egypt
Bibliographic record
Abstract
Our study explores auditors’ perceptions of the triggers and hurdles of implementing continuous auditing (CA) in Egypt. It also explores auditors’ perceptions of the impact of CA on their independence. A survey of ninety-five auditors working in Big Four and non-Big Four firms was conducted to gather data. Descriptive statistics and the Friedman test were used to test our hypotheses. In addition, using the Mann–Whitney U test, we delve deeper into auditors’ perceptions to examine differences across audit firm types. The results reveal that addressing the increasing demand of stakeholders for real-time reporting and enhancing the quality of financial reporting significantly affect auditors’ perceptions of the triggers for adopting CA. In addition, the lack of standards related to CA and the high cost of implementation significantly affect auditors’ perceptions of the obstacles to implementing CA. The lack of clear guidelines regarding the work required in CA and auditing data that the auditors have previously corrected during the CA process is perceived by auditors as among the most significant factors that can impair their independence. The significance of this study stems from the fact that it is one of the few studies to explore continuous auditing practices in developing countries. To the best of our knowledge, this study is one of the first to investigate how CA affects auditor independence in developing countries.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 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".