The Effect of Technology Readiness on Adopting Artificial Intelligence in Accounting and Auditing in Vietnam
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research article focuses on investigating the impact of technology readiness (TR) on the adoption of artificial intelligence (AD) by accountants and auditors, utilizing intermediary factors, such as perceived usefulness (PU) and perceived ease-of-use (PEOU), within companies in Vietnam. Based on 143 survey responses, the results demonstrate a positive relationship between TR and AI adoption among professionals in the accounting and auditing industry. Additionally, the analysis reveals that the intermediary factors PU and PEOU positively influence AI adoption. TR consistently relates with PU and PEOU in applying artificial intelligence in accounting and auditing. The result of the experiment study is that technology readiness positively impacts the AI adoption of accountants and auditors from companies in Vietnam. Hence, perceived usefulness and ease of use mediate the relationship between technology readiness and the adoption of AI technologies by workers in the accounting and auditing industry. This study contributes not only academically by enriching scientific knowledge on AI adoption but also holds practical significance by suggesting training and development policies from a business perspective in the future.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it