Auditor industry specialization, board governance, and earnings management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate the interaction effect of auditor industry specialization and board governance on earnings management. This study examines whether board independence is more or less effective in constraining earnings management for firms audited by industry specialists than for firms audited by non‐specialists. Design/methodology/approach The US data were collected from the RiskMetrics Directors database and the Compustat database. Regression analysis was used to test the research proposition. Findings It was found that earnings management is more negatively associated with board independence for firms audited by industry specialists than for firms audited by non‐specialists, consistent with the notion that there is a complementary relationship between auditor industry specialization and board governance. The findings suggest a positive interaction effect of auditor industry specialization and board governance on accounting quality. Originality/value This study contributes to the literature by documenting explicit evidence that high quality boards can be more effective through hiring industry specialist auditors. This study also suggests that it may be worth investigating the interaction effect among different corporate governance mechanisms on accounting quality.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it