Does experience matter? CEO successions by former CEOs
Bibliographic record
Abstract
Purpose This paper aims to investigate an interesting yet mostly ignored distinction within external CEO successions: outside successors who have previous CEO experience and those who do not. It examines stock market reaction, compensation and firm performance prior and post‐succession. Design/methodology/approach The authors used an event study, “Patell Z‐statistic” and “Rank Z‐statistic” to test cumulative abnormal return before and after the successions. They also used probit and OLS regressions to examine firm performance and CEO compensation prior and post‐succession. Findings The authors find that the stock market reacts positively to the hiring of an outsider who is an exCEO. Compared with firms that hire non‐exCEOs, firms that hire exCEOs had higher debt ratios and greater bankruptcy chances pre‐succession, but post‐succession, these firms still have worse financial performances. Non‐exCEOs come from better performing firms than exCEOs. There is no consistently significant difference in compensation between an exCEO and a non‐exCEO, though the compensation for both increases significantly from that of the predecessors and that of their previous positions. Research limitations/implications Future research could focus on the cost‐benefit tradeoff of hiring an exCEO. It would be interesting to examine the role of the board of directors in assessing this cost‐benefit tradeoff and determining the optimal choice for the firm. An important aspect that has not been sufficiently examined in the literature is the CEO fit. Hiring an exCEO may not always be the right choice for the firm. Another area for future research could examine how the post‐succession performance is affected by exCEO tenure in previous CEO position(s) and whether the exCEO worked in several industries or in the same industry. Practical implications This paper also has implications for the board of directors. There seems to be a negative transfer of human capital when it comes to hiring exCEOs. The human capital theory suggests that job‐specific experience positively relates to job performance. According to Hamori and Koyuncu, prior CEO experience may “lead to the formation of knowledge corridors and decision‐making templates that make it difficult for individuals to take in inconsistent information or take actions that are different from past ones in a changed context. This, in turn, undermines performance”. Boards of directors should put more effort into considering inside relay successions and should be cautious when hiring an outsider who has prior CEO experience. A best‐of‐both‐worlds scenario may be for boards to hire exCEOs into top executive positions, such as COO and/or president, so as to give them a chance to be groomed for the top position and familiarize themselves with the firm while still benefiting from their prior CEO experience. Originality/value There is very little research on the distinction between outside CEOs with previous CEO experience and those with no such experience. This paper tries to shed some light on this important issue in corporate governance in order to explain why boards of directors would hire an outsider with or without previous CEO experience.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 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".