Interim CEO Successions: Implications for CEO Successor Selection and Subsequent Firm Performance
Bibliographic record
Abstract
Prior research highlights that interim CEO successions are disruptive for firms. Yet boards appoint interim CEOs in order to focus on finding the right person to take over permanently, suggesting they may offer benefits to CEO selection. Considering these two views, this paper compares the firm performance of CEOs appointed following an interim period with that of CEOs appointed in a direct succession. We argue that, owing to the disruption caused by the interim appointment, firm performance under a CEO appointed following an interim period will be poorer than under a CEO appointed in a direct succession. However, we posit that, in circumstances in which obtaining and processing the information relevant to CEO selection is more difficult, the interim period may be particularly helpful in selecting the most fitting successor and ultimately weakening the associated performance penalty. Using a sample of CEO successions that occurred in S&P 1500 firms between the years 2002 and 2016, we find general support for our hypotheses. This paper, thus, demonstrates that the subsequent performance consequences of appointing an interim CEO depends on the difficulty of selecting the firm’s next permanent CEO. Funding: We gratefully acknowledge financial support from the Spanish FEDER/Ministerio de Ciencia e Innovacion – Agencia Estatal de Investigacion [Grant PID2021-123450OB-I00].
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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 | 0.041 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".