Technology and Evidence in Non-Big 4 Assurance Engagements: Insights from the COVID-19 Pandemic
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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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 | 0.054 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".