Successor CEO Functional and Educational Background
Bibliographic record
Abstract
This study seeks to examine if boards consider CEO educational and functional background when choosing a new CEO. It also examines which factors determine whether the board of directors will seek an incoming CEO with a different educational and/or functional background from that of the current CEO. Using a sample of 832 successions between 1992 and 2009, we found that the outgoing CEO and the firm characteristics influence the selection of the successors’ functional backgrounds. Firms are more likely to hire new CEOs with functional backgrounds similar to the outgoing CEO. Research-oriented firms hire CEOs with the functional background that would permit them to understand the firm’s research processes. Firms with poor prior operating performance tend to hire successors with a financial/accounting background. We also find that firms are more likely to change the functional background of the successor relative to the predecessor when there has been poor prior performance and the firm has higher institutional investor ownership. This study suggests new avenues of research for the direction of causality between CEO personality and background on one hand and corporate growth and managerial decision making on the other hand. It offers insights into how boards can use firm characteristics and outgoing CEO characteristics to motivate the incoming CEO characteristics.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".