When will boards influence strategy? inclination × power = strategic change
Bibliographic record
Abstract
Abstract While boards of directors are usually recognized as having the potential to affect strategic change in organizations, there is considerable debate as to whether such potential is typically realized. We seek to reconcile the debate on whether boards are typically passive vs. active players in the strategy realm by developing a model that specifies when boards are likely to influence organizational strategy and whether such an influence is likely to impel vs. impede change. Specifically, we develop arguments as to when certain demographic and processual features of boards imply a greater inclination for strategic change, when these features imply a greater preference for the status quo, and how differences in such inclinations will influence strategic change. We then also propose that a board's inclination for strategic change interacts with a board's power to affect change, generating a multiplicative effect on strategic change. These ideas are tested using survey and archival data from a national sample of over 3000 hospitals. The supportive findings suggest that strategic change is significantly affected by board demography and board processes, and that these governance effects manifest themselves most strongly in situations where boards are more powerful. We discuss these findings in terms of their relevance for theories of demography, agency, and power. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.038 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".