Using Cultural Mindsets to Reduce Cross‐National Auditor Judgment Differences
Bibliographic record
Abstract
ABSTRACT In a globalized audit environment, regulators and researchers have expressed concerns about inconsistent audit quality across nations, with a particular emphasis on Chinese audit quality. Prior research suggests Chinese audit quality may be lower than U.S. audit quality due to a weaker institutional environment (e.g., lower litigation and inspection risk) or cultural value differences (e.g., greater deference to authority). In this study, we propose that lower Chinese audit quality could also be due to Chinese auditors' different cognitive processing styles (i.e., cultural mindsets). We find U.S. auditors are more likely to engage in an analytic mindset approach, focusing on a subset of disconfirming information, whereas Chinese auditors are more likely to take a holistic mindset approach, focusing on a balanced set of confirming and disconfirming information. As a result, Chinese auditors make less skeptical judgments compared to U.S. auditors. We then propose an intervention in which we explicitly instruct auditors to consider using both a holistic and an analytic mindset approach when evaluating evidence. We find this intervention minimizes differences between Chinese and U.S. auditors' judgments by shifting Chinese auditors' attention more towards disconfirming evidence, improving their professional skepticism, while not causing U.S. auditors to become less skeptical. Our study contributes to the auditing literature by identifying cultural mindset differences as a causal mechanism underlying lower professional skepticism levels among Chinese auditors compared to U.S. auditors and providing standard setters and firms with a potential solution that can be adapted to improve Chinese auditors' professional skepticism and reduce cross‐national auditor judgment differences.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".