Internal Disruption: Examining the Relationship Between Interim CEO Successions and Executive Turnover
Bibliographic record
Abstract
ABSTRACT Research Question/Issue Although research on interim CEO successions has increased, there remains limited knowledge about what internal repercussions interim CEO appointments have on firms, notably on incumbent executives. This study seeks to understand whether and how interim CEO appointments are related to executive turnover, focusing on the role and involvement of the board in the interim succession process. Research Findings/Insights Interim CEO successions are related to higher executive turnover compared to direct CEO successions. This turnover is further augmented either when the preceding CEO was fired or when the interim CEO is also the board chair. We also find that, when the preceding CEO was fired, the executive turnover related to interim CEO successions is related to poorer subsequent firm performance. Theoretical/Academic Implications This research fills an important gap in the interim CEO succession literature, focusing on the internal repercussions of interim CEO appointments, particularly how incumbent executives may be affected and how this may influence firm performance. It reconciles the view that interim CEOs may engage in limited decision‐making, whereas the board uses the interim period to plan for the firm's future. Practitioner/Policy Implications This paper highlights the potential disruptions caused by interim CEO successions and offers some insights into how these may affect executives and firm performance. Moreover, this paper acts as a guide for stakeholders on the nuanced governance decisions around interim CEO appointments.
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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 | 0.007 | 0.031 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".