The Effect of Accountability and Time Budgets on Auditors' Testing Strategies*
Bibliographic record
Abstract
Abstract This study investigates the joint effects of accountability and time budgets on auditors' testing strategies. The task studied, substantive analytical procedures, requires auditors to identify and test hypotheses when investigating the cause of unexpected fluctuations. Thus, auditors must determine the number of tests to conduct (i.e., extent), the number of potential hypotheses to directly test (i.e., breadth), the number of tests for each hypothesis (i.e., depth) and the number of potential error or non‐error hypotheses to test (i.e., focus). Testing strategies, which we define as choices made with respect to extent, focus, depth, and breadth of testing, have significant practical and theoretical implications. For example, reducing the breadth of testing may result in failure to test the correct hypothesis, potentially impairing audit effectiveness. In this study, auditors inherited five potential causes of an unexpected increase in the gross margin of a client. As in practice, their task was to conduct tests to investigate and identify the actual cause of the fluctuation. Auditors were randomly assigned to one of four conditions created by fully crossing accountability and time budgets. The results indicate that accountability leads to an increase in the extent and breadth of testing but does not affect the depth of testing. Further, accountability leads to an increase in the testing of errors but results in a decrease in the testing of non‐errors. The focus on breadth and error testing is consistent with the notion that accountability, to a superior with unspecified preferences, promotes more cautious behavior. The results also show that a time budget decreases the extent and depth of testing but does not affect the breadth of testing. There was no evidence that the two factors interactively affected testing strategies or performance. Finally, increased breadth of testing was the mechanism that led to better performance as measured by the identification of the actual cause of the unexpected fluctuation.
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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 | 0.043 | 0.389 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".