Stepping into Ill-Fitting Shoes: Local Status Contrasts and Acquisitiveness of New CEOs
Bibliographic record
Abstract
This paper asks what motivates new chief executive officers (CEOs) to engage in an acquisition spree despite the considerable risk it entails to themselves and their firms. Building on status theory and performance feedback theory, we theorized that status distance between new CEOs and their predecessors explains the empire-building behavior of new CEOs early in their tenure. Because of the uncertainty surrounding a CEO’s quality early in the individual’s tenure, status serves as a signal of quality for the new CEO. Hence, CEOs had to rely on status signals to maintain or close the status gap between them and their predecessors. Drawing on performance feedback theory, we theorized that new CEOs’ status contrast relative to their predecessor influences their acquisitive behavior. Our examination of the acquisition behavior of 429 new CEOs of S&P 500 firms in the United States revealed that relatively low-status CEOs engaged in risk-taking to improve their status, but relatively high-status new CEOs engaged in risk-taking to maintain their lead. It also revealed that new CEOs changed their risk-taking behavior when direct evidence of their quality or that of their predecessors deviated from the underlying quality expectations indicated by their relative status position.
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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 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".