Technology and Evidence in Non-Big 4 Assurance Engagements: Insights from the COVID-19 Pandemic
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
SUMMARY We interviewed 30 assurance professionals in the United States regarding how and to what extent non-Big 4 firms incorporated technologies into assurance engagements during the COVID-19 pandemic. Informed by technology acceptance models, our findings show that the pandemic played an accelerator role, prompting an open attitude toward experimenting with technologies in assurance engagements. This experimentation increased perceptions of the usefulness of technology in engagement efficiency, given easier and faster evidence gathering. However, the readiness and security of clients’ systems remain barriers in evidence gathering. Assurance professionals perceive technology as useful in producing better quality evidence evaluation, with usage stymied by challenges related to source data integrity, naive use of tools, and distrust of outputs limiting the extent of change in evidence evaluation. Our study indicates more modest technology gains in evidence evaluation than in evidence gathering during the pandemic due to barriers with higher stakes, often tied to assurance conclusions.
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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 | 0.007 | 0.028 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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