AI-based audit acceptance and auditors’ technology readiness
Bibliographic record
Abstract
This study investigates auditors' willingness to adopt AI-based audit tools using the AI Device Use Acceptance (AIDUA) model, focusing on the factors influencing acceptance and the moderating role of technology readiness. Data were collected from 153 certified external auditors in Jordan, representing a 30% response rate. The findings reveal that social influence and hedonic motivation positively impact performance expectancy, while anthropomorphism influences effort expectancy. Emotions significantly affect auditors' willingness to adopt AI-based audits, moderated by their technology readiness. This study contributes to the literature by utilizing the AIDUA framework to understand AI acceptance in auditing, offering insights into the unique aspects of AI technologies. The results highlight the importance of understanding auditors' perceptions and readiness, providing valuable implications for practitioners and policymakers to develop strategies for effective AI integration in auditing. Future research should explore these dynamics in diverse cultural contexts and over extended periods to enhance the generalizability of the findings.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".