What Makes Better Boards? A Closer Look at Diversity and Ownership
Bibliographic record
Abstract
This study investigates the joint effect of corporate ownership and board of directors' diversity configurations on the success of strategic merger and acquisition (M&A) decisions. Board diversity is defined as the extent to which its demographic diversity as measured by the culture, nationality, gender and experience of its directors complements its statutory diversity. A theoretical framework linking ownership, board diversity and M&A strategic decision making is proposed and tested. Based on a sample of 289 M&A decisions undertaken by Canadian firms over the period 2000–2007, demographic diversity is found to have a clear and non‐linear effect on M&A performance while statutory diversity is of limited influence. Ownership is found to influence the effect of diversity, making the relation finer and more precise. This has practical implications. First, statutory diversity is not sufficient for well‐performing boards. Also, ownership is an important factor. The most advocated board diversity aimed at insuring the board's independence is not valid across all ownership configurations. From a public policy perspective, results provide support for the principles‐based approach in governance. Governance regimes should encourage the search for a balance between board diversity and the need for cohesion that best serves the firm's purpose and obligations.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".