An Empirical Assessment of a Model of Effective Audit Judgment in an E-Commerce Scenario
Bibliographic record
Abstract
This global survey of 203 B2B E-Commerce auditors examined a model of E-Commerce audit effectiveness using confirmatory factor analysis and structural regression analysis. The findings support the theorized relationship of information technology audit expertise and information and communication technology expertise on E-Commerce audit judgment while the theorized system change management impact was indirect via information technology audit expertise. The results of this empirical study furthers understanding of the importance of auditor expertise in systems and network change management which has been an under researched area in E-Commerce auditing. The highly technology-centric nature of E-Commerce requires various expertise areas for the E-Commerce auditor to develop a higher level of audit judgment expertise. The most significant contribution made by this study to the accounting literature lies in the empirical validation of the E-Commerce audit judgment expertise model which uses global sampling of forty six countries with 203 auditor respondents. Further, this study provides measurement scales for future empirical studies to not only validate these scales on independent samples but also to extend the theory developed and tested in this paper. It is hoped that the results of this study can provide a sound theoretical and operational basis for research focused on differentiating the efficacy of varying E-Commerce audit judgment expertise configurations and for future accounting studies that determine the paths of audit expertise system design and redesign for our fast changing technological milieu.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".