The Relation between Strategy, CEO Selection, and Firm Performance
Bibliographic record
Abstract
ABSTRACT We examine whether a firm's strategic priorities influence its selection of a new CEO and what conditions enable such an appointment to add value to the firm. More specifically, this study investigates the value‐adding effect when prospector firms (i.e., those pursuing a prospector‐type strategy) select a CEO with high social capital. We argue that uncertainty, driven by a firm's strategy, will determine the decision to select a CEO with high social capital; such CEOs can use their networks to mitigate the uncertainty and thus can be valuable to the firm. However, prior research indicates that CEOs with high social capital can engage in behavior detrimental to firm value. To mitigate the potential for this to occur, we assess whether corporate governance can play a role in prospector firms who appoint CEOs with high social capital. Drawing on archival data of CEO successions over a 14‐year period, we find that prospector firms have greater incentives to appoint CEOs with high social capital. We also find that prospector firms who appoint a CEO with high social capital improve their performance. Furthermore, the value‐adding effect of this selection choice is stronger in prospector firms with good corporate governance.
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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 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".