Influence factors analysis of online auditing performance assessment
Bibliographic record
Abstract
Purpose Consistent with the requirements of online auditing performance assessments, the purpose of this paper is to propose an influence factors analysis method using analytic hierarchy process (AHP) and grey incidence analysis (GIA) to analyze the importance degree of influence factors on online auditing performance quantitatively. Design/methodology/approach A grey incidence model is developed to analyze the influence factors of online auditing performance based on the characteristics of online auditing. Then, the AHP is used to compute the weights of each assessment criterion of online auditing, and the performance of online auditing are computed. Finally, representing the performance assessment results computed by AHP and values of each assessment criterion as two sequences, GIA is used to analyze the importance degree of influence factors of online auditing performance quantitatively. Findings The main, secondary and minor influence factors of performance assessment of the online auditing project are identified. For online auditing projects, costs incurred are not the main influence factors of performance. Online auditing projects with higher benefits, higher quality and better design are the really effective ones. Besides, there is no direct relationship between the value of the weight of each criterion and the value of the degree of grey incidence. Practical implications The results of this study provide useful decision information to implement online auditing projects. Originality/value An effective method for analyzing the importance degree of influence factors of online auditing performance quantitatively is provided in this study.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".