Auditing Standard Change and Auditors' Everyday Practice: A Field Study
Bibliographic record
Abstract
The analysis of inertial phenomena in business context has been developed with reference to management and financial accounting (Hopwood, 1990; Hopwood and Miller, 1994). In the auditing field, however, only a few studies have attempted the inertial phenomenon (Salterio, 1996; Salterio & Koonce, 1997; O'Reilly, et al., 2004; Messier, et al., 2012). An opportunity to investigate more in depth the topic of auditing inertia is offered by the recent change of the auditing principles in Italy. Stemming from the aforementioned consideration, this paper aims to analyze the phenomenon of auditing inertia making reference to a case of change in the auditing standards and procedures to which auditors refer to in performing audit works. In order to achieve this aim, a field study methodology was adopted. The main findings are the following. First, it emerges that the reactions of the audit firms to the analyzed change of auditing standards are different. Second, the analyzed cases have shown that single auditors have undergone passively through the process of change. Third, the collected evidences also suggest some of the items that contribute to the adoption of an inertial behavior. Fourth, it emerges that the aim of the EU project, the harmonization and adaptability-flexibility of auditing procedures, is only partially achieved as sometimes occurs a substantial harmonization while in other cases it is only formal.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".