How is Audit Quality Perceived by Big 5 and Local Auditors in China? A Preliminary Investigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study seeks to determine what attributes are perceived by various groups of players in the Chinese auditing market as being important to audit quality. Attributes of audit quality were incorporated into a survey instrument which was administered to auditors from a local CPA (Certified Public Accountant) firm and a Big 5 firm operating in Shanghai, as well as subjects belonging to another group—regulators. Results indicate that the attributes perceived as having the most positive effect on audit quality correspond to a very high degree with those identified in the prior (mostly US‐based) literature. Attributes thought to be most detrimental to audit quality, on the other hand, were developed from consultations with key players, and were unique to the Chinese context. Wide differences were observed between auditors and regulators (e.g., regulators and Big 5 auditors), but not among the independent practitioners (local CPAs and Big 5 auditors). In addition to heightening the current understanding of what attributes of audit quality are important to audit firms operating in emerging markets such as China, these findings are expected to hold significant implications for strategy formulation by public accounting firms in China, both local as well as international.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it