Board Independence and Corporate Misconduct: A Cross-National Meta-Analysis
Bibliographic record
Abstract
Although increased board independence is a commonly offered solution to curbing corporate misconduct, scholars have expressed skepticism about its effectiveness, and empirical evidence is mixed. We argue that the relationship between board independence and corporate misconduct is likely nuanced—and may vary by the type of independence (e.g., independence on the whole board or on the audit committee) and by national context. We conducted a meta-analysis of 135 studies spanning more than 20 countries. We find that the board independence–corporate misconduct relationship (a) is generally negative, (b) varies based on the implementation form that independence takes on (i.e., independence of the whole board, on the audit committee, or between the roles of CEO and board chair), and (c) is more strongly negative in countries with less corruption. We advance corporate governance theory and research by demonstrating that the popular governance practice of increasing board independence must both account for the manner in which independence is implemented and consider the powerful influence of firms’ broader societal context to clearly understand its effect. Further, based on our review of the literature, we uncover opportunities for the advancement of corporate governance and corporate misconduct research.
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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 | 0.043 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.034 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".