A game theory perspective to power acquisition during CEO transition period
Bibliographic record
Abstract
Purpose This study examines how CEOs-elect navigate power dynamics with incumbent CEOs during leadership transitions, focusing on their strategic choices – cooperate, defect or disengage – based on perceptions of the incumbent’s behavior. Design/methodology/approach Using the game theory framework and insights from 22 interviews with executives from large Canadian organizations, we analyze CEOs-elect’s decision-making from nomination to ascension. Findings CEOs-elect cooperate when they anticipate the incumbent to cooperate and defect when they anticipate defection. When faced with uncertainty or signs of disengagement from the incumbent, CEOs-elect strategically choose to disengage, adopting a “No Play” strategy to preserve board trust and organizational stability. Research limitations/implications Findings are based on large Canadian organizations, which may limit applicability to smaller firms, family businesses or different cultural contexts. Future research should examine CEO transitions across diverse organizational and cultural settings. Practical implications Boards should recognize proactively manage power struggles during transitions, ensuring support for CEOs-elect and promoting cooperation with incumbents. Understanding perceived incumbent strategies can improve transition planning, minimize conflicts and improve organizational outcomes. Originality/value This research introduces “No Play” as a novel strategic option in CEO transitions, contributing to game theory and power dynamics literature. It also bridges gaps in understanding by linking strategic choices of CEOs-elect to perceptions of incumbent behavior and stakeholder trust.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".