Does the use of honorific appellations in audit reports connote higher financial misstatement risk? Evidence from China
Bibliographic record
Abstract
Purpose From the sociolinguistic perspective, the purpose of this paper is to examine whether the honorific and actual-name appellations that Chinese auditors use to address clients in audit reports connote differential financial misstatement risk. Specifically, the authors hypothesize that auditors’ use of honorifics signals their inferior social status relative to their clients, thereby leading to compromised auditor independence, lower audit quality, and higher financial misstatement risk. Design/methodology/approach The authors use a sample of manually coded appellation data from audit reports of Chinese public firms between 2003 and 2012 to conduct the research. Findings The authors find significantly greater financial misstatements, both in terms of likelihoods and magnitudes, for companies addressed by honorifics than for those addressed by actual names. Moreover, compared to auditors’ consistent honorific usage, discretionary honorific usage has a stronger positive association with misstatements. The authors further show that the positive association between honorific usage and client misstatement risk weakens when the audit firm is a Top 10 accounting firms in China, is an industry specialist, is formed as a partnership, or resides in a more concentrated audit market. Originality/value This study contributes to the sociolinguistics literature in accounting and provides evidence supporting the reform proposed by the International Auditing and Assurance Standards Board to enhance the usefulness of audit reporting.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".