Bibliographic record
Abstract
ABSTRACT As financial statements continue to contain more estimates and audit programs rely more on enquiry-based evidence, there is an increasing need to understand whether the process of enquiry can provide more reliable evidence. Prior research in other disciplines indicates enquiry process improvement is a first step in the direction of improving the quality of evidence obtained from management enquiry. This study explores which type of decision aid—a simple cognitive planning theory-adapted instruction or a detailed checklist developed from auditing field research—is more likely to improve junior auditors' planning for collecting enquiry-based evidence about a specific accounting issue. An experiment was conducted using 154 participants with an average of 12 months of auditing experience. The results show participants receiving the theory-based instruction, in contrast to those receiving no decision aids, planned to pose a greater number of questions and inquire of a larger and more diverse set of client personnel in areas relevant to the accounting issue. Further, these participants planned to corroborate evidence obtained from management enquiry with an increased number of audit procedures other than enquiry. The practice-based checklist resulted in similar overall effects, albeit at the “cost” of having to deal with a more complex aid and with less focus on corroboration by means other than enquiry. This study suggests that both decision aids can lead junior auditors to improve their planned approach to enquiry, and if these plans are executed appropriately, they could lead to more reliable audit evidence generated from enquiry of management. Data Availability: Contact the author.
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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 | 0.268 | 0.262 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".