Bibliographic record
Abstract
In today's dynamic business landscape, the audit profession encounters numerous obstacles, particularly in adapting to the necessity of computer-assisted audits due to the immense volume of data requiring scrutiny. Despite the emergence of different digital auditing tools, there is a gap in research regarding the level of adoption, and its effects on the quality of audit services especially in the context of developing countries. This study seeks to investigate the impact of digitalization of audit tasks on the quality of audit services of accounting firms in Nigeria. The study, which has its foundation on the Technology Acceptance Model (TAM) integrated with the Technology, Organization, and Environment (TOE) framework, adopted the survey research design. The population of study was made up practitioners of accounting firms in Abuja and Lagos, Nigeria. A self-designed questionnaire was used as a tool for data collection for the study. Findings of the study indicate that automation of audit tasks enhances the quality of audit services suggesting that adopting IT infrastructures leads to more reliable audit procedures, improved efficiency and accuracy, as well as mitigating audit risks. Results also revealed that Big Four auditors are significantly ahead in the adoption of digital technologies compared to the non-Big Four auditors, confirming the dominance of larger accounting firms in application of emerging technologies in performing audit tasks.
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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 | 0.001 | 0.001 |
| 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 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".