Conflict-induced forced CEO turnover and firm performance
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine operational and stock performance changes around forced CEO turnovers caused by conflicts between corporate boards and CEOs over the strategic direction of the firm. In addition, the authors investigate whether changes in performance can be explained by board, CEO, or firm characteristics. Design/methodology/approach The authors apply propensity score matching to choose matching firms that do not forced CEO turnover but have similar characteristics with the sample firms. The authors compare their operating and stock performances. The authors apply both univariate analysis and multivariate regression analyses. Findings The authors find that the CEO turnovers caused by conflicts between corporate boards and CEOs over the strategic direction of the firms tend to be preceded by significant declines in a firm’s operating and stock performance and that corporate performance improves after turnovers. In addition, the authors find that an increase in long-term incentives and firm size and a decrease in turnover improve firm performance. Originality/value While the existing corporate governance literature emphasizes oversight as the main role of the board of directors and identifies the CEO as the leader who sets the strategic direction of the firm, in cases of conflict-induced forced CEO turnover, it is the board that sets the strategic direction. This paper is the first to provide evidence regarding the implications of conflict-induced forced CEO turnovers.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".