Executive Deviance as a Sociopolitical Force in Dismissals
Bibliographic record
Abstract
The seminal model of CEO dismissals utilizes four sociopolitical forces that operate together in determining whether a CEO will be dismissed. Missing from the sociopolitical forces of CEO dismissals is executive deviance. We propose the inclusion of executive deviance as the fifth sociopolitical force in CEO dismissals, which is not limited to the deviant actions of the executive, but also the executive’s subordinates. Within the comprehensive CEO dismissal framework, the effect of executive deviance on head coach dismissals in the National Football League (NFL) from the 2000–2001 to 2015–2016 season is examined using four levels of executive deviance: (a) deviance committed directly by the executive, (b) minor workplace deviance by employees, (c) serious workplace deviance by employees, and (d) off-duty deviance by employees. Logistic regression results indicate all four levels of executive deviance increase the likelihood of executive dismissal and have more substantial effects than organizational performance. We encourage researchers to include executive deviance within their comprehensive, ceteris paribus models of CEO dismissals, empirically test the effects of executive deviance in various industries, and revisit past models of executive dismissals to mitigate potentially erroneous statistical results from confounding variables.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".