The Role of Auditor Narcissism in Auditor‐Client Negotiations: Evidence from China
Bibliographic record
Abstract
ABSTRACT This paper reports the results of three studies (archival, experimental, and qualitative) designed to examine the effects of auditor narcissism on auditor‐client negotiations in China. We contend that narcissistic characteristics fuel auditors' competitiveness and embolden them to stand firm in negotiations, potentially lengthening the negotiation process but leading to more conservative negotiation outcomes. As predicted, our archival results suggest that auditor narcissism is positively associated with audit delay and negatively associated with clients' absolute and positive discretionary accruals. Our experimental results document that narcissistic auditors are more likely to be involved in negotiations that reach an impasse or take longer to resolve and that narcissistic auditors negotiate reported asset values that reflect less aggressive reporting choices. Our qualitative results from field interviews with practicing audit partners corroborate our archival and experimental findings. Overall, the data collected using three different research methods yield consistent results in support of our theory. Our findings shed light on factors that influence audit efficiency and quality in China. We discuss the key cultural and contextual differences between China and the West as well as the implications of these differences for future research.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".