When Experts Become Liabilities: Domain Experts on Boards and Organizational Failure
Bibliographic record
Abstract
How does the presence of domain experts on a corporate board—directors whose primary professional experience is within the focal firm’s industry—affect organizational outcomes? We argue that under conditions of significant decision uncertainty, a higher proportion of domain experts on a board may detract from effective decision making and thus increase the probability of organizational failure. Building on exploratory interviews with board members and CEOs, we derive hypotheses from this argument in the context of local banks in the United States. We predict that the greater the level of decision uncertainty—due to rapid asset growth or operation in less predictable markets—the stronger the relationship between the proportion of banking expert directors and the probability of bank failure. Longitudinal analyses of 1,307 banks between 1996 and 2012 support this prediction, even after accounting for both the overall level of professional diversity among directors and banks’ different propensities to have an expert-heavy board. We discuss implications for the key dimensions of board composition, the conditions under which the professional background of directors is more or less consequential, and the mechanisms whereby board composition affects organizational outcomes.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".