Just Diverse Among Themselves: How Does Negative Performance Feedback Affect Boards’ Expertise vs. Ascriptive Diversity?
Why this work is in the frame
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Bibliographic record
Abstract
We investigate how negative performance feedback affects board diversity, which is instrumental in shaping a firm’s strategic change. When a firm underperforms compared with its aspiration, its board is motivated to promptly address the underperformance. The board needs to not only help search for strategic alternatives but also quickly build consensus around its strategic reorientation. These two motivations lead the board to value two dimensions of diversity among its members differently. On the one hand, to understand the problem of underperformance and find a solution, the board is motivated to seek new expertise, avoiding redundancy in the pool of expertise already represented in the boardroom. This results in a higher level of diversity in director expertise. On the other hand, the urgent need to build consensus prompts the board to value trust and solidarity and to avoid potential conflict among directors. Because people perceive others with similar ascriptive backgrounds as trustworthy, changes in the board of an underperforming firm are likely to yield a lower level of diversity in its members’ ascriptive backgrounds. These changes in board are affected by the committee chairs of the board whose power and influence are significant in the boardroom. Analyses of the boards of 733 U.S. listed manufacturing firms show that when a firm underperforms compared with its aspirations, it increases the board expertise diversity, but decreases the board ascriptive diversity. When chairs on the board are gender or racial minorities, the negative association between underperformance and the board ascriptive diversity is weakened. Supplemental Material: The e-companion is available at https://doi.org/10.1287/orsc.2022.1595 .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it