Leader Dismissal or Continuity, President Longevity, Geographic Orientation of Owners and Team Performance: Insights from French Men’s Football, 1994–2016
Bibliographic record
Abstract
We investigated the impacts of president longevity and the geographic orientation of owners on team performance and on the effectiveness of dismissing the leader. In addition, we considered their impacts on the effectiveness of not dismissing the leader while the same organisation fires them at another time for a similar performance. We also tested the impact of dismissing the leader or not on performance. We explored the aforementioned risk-taking relationships in the first tier of French men’s football over the 1994–2016 period (n = 4918 observations). To do so, we used a counterfactual based on the evolution of the team position over the last three games leading to the leader change and estimate linear regression models with fixed team effects. Our findings show that performance improves either after a leader dismissal or not in the same situation, and both president longevity and the geographic orientation of owners impact the effectiveness of dismissing the leader or not. In particular, global- and local-oriented ownerships have a positive impact on the effectiveness of the decision to dismiss the leader or not compared to national-oriented ownership. Practical implications stem from the research, e.g., how organisations with national-oriented ownership can overcome their competitive disadvantage.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".