THE EFFECT OF AUDIT OPINION, FINANCIAL DISTRESS, AUDIT DELAY, CHANGE OF MANAGEMENT ON AUDITOR SWITCHING
Bibliographic record
Abstract
There are several factors responsible for shaping the decision of changing auditors besides mandatory regulation. In this paper, the author contributes to the existing body of literature by analyzing the impact of change in management, audit opinion, audit delays and financial distress on the decision of switching auditors. The analysis is carried out in the context of the metal firms listed on the Indonesian stock exchange. Data has been collected against a period of eight years from 2011 to 2018. The sample consists of 88 public manufacturing companies listed on the Indonesian Stock Exchange (IDX). Using logistic regression, the paper highlights the following key findings based on the analysis are as follows. First, audit opinion does not influence auditor switching. Second, financial distress has a negative and significant effect on auditor switching. Third, management change has a positive and significant effect on auditor switching. Finally, audit delays have significant effects on auditor switching. The policy implications and the direction for future research is also provided in the paper
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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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".