Moderating the role of top management commitment in usage of computer-assisted auditing techniques
Bibliographic record
Abstract
The importance of computer-assisted auditing techniques (CAATs) is widely acknowledged by auditors. However, the current usage of CAATs is not as broad as expected. In this work, the technology–organization–environment framework is used to establish and analyze the organizational factors affecting the post-adoption usage of CAATs. This study also determines whether or not the use of CAATs enhances the audit process. Top management commitment is introduced as a variable that moderates audit firms’ use of CAATs and audit performance. The data used in this work were obtained from auditors of audit firms in Jordan. Analysis results reveal that CAAT usage is affected by the cost–benefit of technology, firm size, readiness and competitive pressure. By contrast, technology compatibility and the complexity of the accounting information systems of clients do not appear to influence CAAT usage. Top management directly influences audit performance and is thus crucial in dictating how auditors utilize CAATs. However, it does not exert a moderating effect (top management × audit firm’s use of CAATs) between audit firms’ use of CAATs and audit performance. Moreover, the use of CAATs improves the overall audit process of audit firms.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".