Does industry expertise at engagement partner and audit firm level matter in emerging market? Evidence from Indonesia
Bibliographic record
Abstract
This study investigates the association of industry specialization at the engagement partner level and audit firm level with aggressive earnings management and modified audit opinion. The study employs a sample of 570 firm-year observations of manufacturing industries on the Indonesia Stock Exchange from 2014 to 2018 using a binary logistic regression model. First, this study finds no evidence of a relationship between industry specialization at the engagement partner level and audit firm level with aggressive discretionary accruals. Furthermore, the author finds evidence of a positive association between industry specialization at the audit firm level and aggressive real earnings management due to high audit quality. Finally, the study finds evidence that industry specialization at audit firm level is likely to issue modified audit opinion. This study contributes to the study of industry specialization at the engagement partner level and audit firm level, which is rarely performed in Indonesia. Policy makers and capital market players might learn some lessons from the audit quality of external auditors with industry specialists as the gatekeeper of the capital market. Moreover, this study has provided a valuable perspective to practitioners, researchers, and policy makers in other emerging markets regarding the quality of industry specialization at the partner and audit firm level.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".