How quickly do CEOs become obsolete? Industry dynamism, CEO tenure, and company performance
Bibliographic record
Abstract
Abstract Scholars have characterized CEO tenures as life cycles in which executives learn rapidly during their initial time in office, but then grow stale as they lose touch with the external environment. We argue, however, that the opportunities for adaptive learning are limited because (1) a CEO assumes office with a relatively fixed paradigm that changes little thereafter; (2) inertia limits the speed at which an organization can align itself with a new CEO's paradigm; and (3) for any within‐paradigm learning to occur, the external environment must be stable enough so that the cause–effect relationships that CEOs glean today remain relevant tomorrow. In a longitudinal study of 98 CEOs in the relatively stable branded foods industry and 228 CEOs in the highly dynamic computer industry, we found results that strongly supported our hypotheses. In the stable food industry, firm‐level performance improved steadily with tenure, with downturns occurring only among the few CEOs who served more than 10–15 years. In contrast, in the dynamic computer industry, CEOs were at their best when they started their jobs, and firm performance declined steadily across their tenures, presumably as their paradigms grew obsolete more quickly than they could learn. Copyright © 2006 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 | 0.005 | 0.033 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".