The Corporate Restructuring Imperative: Performance, Strategy, and CEO Dismissal in the Shareholder Value Era
Bibliographic record
Abstract
This paper proposes that CEO dismissal is a form of penalty that CEOs incur for their record of deviation from historically prevailing norms of appropriate behavior during performance downturns. To verify this argument, I examine CEO dismissal and post-dismissal strategic change in large U.S. companies between 1984 and 2007, when the field in which these firms were embedded was characterized by the prominence of the norms of corporate restructuring for shareholders. My findings are three-fold. First, the effect of performance declines on CEO dismissal was intensified by the extent to which CEOs held a record of deviation from the norms of restructuring—i.e., that of increasing assets, employees, and unrelated diversification. Second, new CEOs were more inclined to engage in restructuring when they took office following the dismissal of predecessors with a record of deviation. Finally, those patterns of CEO dismissal and post-dismissal restructuring became more evident over time as the norms developed. Meanwhile, the findings did not apply prior to the shareholder value era. Consequently, this paper suggests that institutional norms act as interpretive frameworks that enable a board of directors to make sense of performance problems and evaluate managerial competence to resolve those problems. Implications for the literatures on institutional theory and upper echelon research are discussed.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".