The antecedents of audit quality: The input-process-output factors
Bibliographic record
Abstract
This paper is intended to explore the manifestation of attributes in reflecting audit quality as set by International Auditing and Assurance Standard Board (IAASB). This research focuses on the attributes of Input-Process-Output factors for engagement (auditor) and firm (public accounting firm) level. The input factors considered are: values, ethics, and attitude, knowledge, skill and experience. The process factors considered are: audit process, and quality assurance. The output factor considered is the audit report. The data gathered from 250 senior auditors who work in 100 public accounting firms in Indonesia. The data analysis and hypotheses testing were processed using Second Order Confirmatory Factor Analysis - Structural Equation Model (SEM) - SmartPLS 3.0. The results of the study confirmed that input, process, and output factors manifest the audit quality. All attributes of values, ethics and attitude for engagement and firm level, positively manifest the audit quality. All attributes of knowledge, experience and time, for engagement and firm level, positively manifest the audit quality. All attributes of audit process and quality assurance, for engagement and form level, positively manifest the audit quality. All attributes of output (audit report), for engagement and firm level, positively manifest the audit quality.
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.
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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".