Directors’ Perceptions of Board Effectiveness and Internal Operations
Bibliographic record
Abstract
We contribute to the growing literature on the effectiveness of corporate boards by examining the effect of two insights that have been largely unexplored in prior studies that use public data. First, since boards’ responsibilities are wide-ranging, more holistic performance measures may better capture the full range of their duties than specific public actions and outcomes (e.g., disclosure of risk management processes, financial restatements, acquisition returns, CEO turnover). And second, because corporate boards share many characteristics of other types of teams, their effectiveness is likely to be influenced by their internal operations. To examine the performance effects of these insights, we use data from 577 directors of U.S. public firms that responded to a survey we conducted in 2015–2016 and qualitative data from interviews of 75 directors. Our study establishes a strong relation between director perceptions of board performance effectiveness and internal board operations. Further, by highlighting the critical role of internal operations, identifying areas of relative strength and weakness in boards’ effectiveness in various activities, and probing director perceptions of their primary responsibilities, we are able to offer concrete suggestions for future research on board effectiveness. This paper was accepted by Shiva Rajgopal, accounting.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".