Bad news for announcers, good news for rivals: Are rivals fully seizing transition‐period opportunities following announcers' top management turnovers?
Bibliographic record
Abstract
Abstract Research summary This study analyzes whether and how the disruption of top management turnovers can affect not only turnover firms but also their intra‐industry rivals. It thus adds to the literature on both leader life cycles and competitive dynamics. Using a U.S. sample of 857 CEO turnovers, we find a period of relative stagnation for announcing companies following top management turnovers. We also find that intra‐industry rivals can use this period to their advantage. Semi‐structured interviews with seasoned CEOs, CFOs, and a board member from large publicly listed firms, as well as an extensive news search, support this notion. Intra‐industry rivals gain a competitive advantage that can result in positive abnormal stock returns and accounting performance. The intra‐industry outperformance is greater for forced turnovers. Managerial summary The departure of a company's CEO, forced or not, is usually a disruptive event for a company, as the successor must adapt to the new environment before undertaking any major strategic changes. Rivals can seize an opportunity during the transition period of the announcing company because they remain fully operational. They can thus actively exploit the relative inability of turnover companies to react by, for example, launching sales initiatives or increasing M&A activity. This interpretation is supported by internal and external evidence. Investors on average also recognize this situation, and stock prices react accordingly.
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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 | 0.019 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".