The impact of computer assisted auditing techniques in the audit process: an assessment of performance and effort expectancy
Bibliographic record
Abstract
The rapid advancement of technology has had a significant impact on a wide range of industries, including the auditing industry. It is now obvious that employing Computer Assisted Auditing Techniques (CAATs) is a possible tactic for enhancing the effectiveness and efficiency of the audit process. This study evaluates how CAATs affect auditors' expectations for performance and effort in Jordan. Through a comprehensive survey of Jordanian auditors, this research provides insights into the factors that drive CAATs adoption. Utilizing structural equation modeling, the study confirms that both Effort Expectancy and Performance Expectancy positively influence CAATs adoption. These relationships are supported by robust path coefficients and low P-values, indicating statistical significance. The results of this study should clarify the possible advantages of including CAATs in the audit process and point out any difficulties auditors could encounter. Companies and professionals may choose wisely whether to embrace and use CAATs by comprehending Performance Expectancy and Effort Expectancy.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".