When Experts Become Liabilities: Domain Experts on Boards and Organizational Failure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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