Turnover by Non-CEO Executives in Top Management Teams and Escalation of Commitment
Bibliographic record
Abstract
This article investigates the relationship between the decision-making bias known as escalation of commitment and the turnover of non-CEO executives in top management teams. The phenomenon of escalation of commitment is observed when decision makers persist with business investments that have a low likelihood of success. Theoretical explanations for the association between executive turnover and escalation include self-justification and reputation protection. Top managers may conceal prior errors, escalate commitment to earlier decisions, and exit the organization before the outcome of decisions is observed. Successor managers do not have a commitment to earlier decisions and have the capability to stop investments that are discovered to be failing. Empirical analysis utilizing a sample of over 1600 U.S. firms confirms that departures by non-CEO executives from top management teams are associated with an increased likelihood of new reporting of discontinued operations and extraordinary items by firms and a reduction in the firms’ performances relative to their industry. These effects reflect de-escalation activities and are amplified in the years concurrent with and following a joint departure of multiple management team members. Prior empirical studies on escalation and de-escalation behavior focused on CEO turnover. The contribution of this article is its documenting of the key role of non-CEO managers and team turnover in the context of escalation.
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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 | 0.003 | 0.022 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".