The impact of artificial intelligence applications on the performance of accountants and audit firms in Saudi Arabia
Bibliographic record
Abstract
The purpose of this study is to investigate the impact of using artificial intelligence applications on the performance of accountants and audit firms. The final sample for this study comprises 38 audit firms. This study uses a survey-based methodology in the context of Saudi Arabia. The results of the multiple regression revealed that the audit firms using artificial intelligence applications perceive them as useful instruments that increase the performance of accountants and audit firms. They can reduce the cost, effort, and time of the audit process, achieve a competitive advantage for the audit firms, help auditors better determine materiality, achieve a competitive advantage, improve the performance of the audit team, carry out the continuous audit process better than the traditional audit, enable auditors to select audit samples with high efficiency, improve the quality of control procedures on electronic transactions and files used by the client, contribute to the management of operations and tasks with more sophisticated and intelligent mechanisms, increase the efficiency and effectiveness of the audit process and the efficiency and effectiveness of planning and supervising the audit process, reduce uncertainty and audit risk. The results reported by this study can be valuable for the accounting and auditing professions, audit firms, and standards and auditing regulators to deeply understand the extent to which artificial intelligence applications influence the performance of accountants and audit firms.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".