Explaining Performance Differences between Family Firms with Family and Nonfamily CEOs: It's the Nature of the Tie to the Family that Counts!
Bibliographic record
Abstract
Drawing on regulatory focus theory, we advance a microtheory for Naldi, Cennamo, Corbetta, and Gómez–Mejía's findings suggesting that family ties as well as the career aspirations that derive from them trigger relatively higher prevention and relatively lower promotion goal orientations of family when compared with nonfamily chief executive officers (CEOs). Our conceptualization offers an alternative theory for why family firms with family CEOs outperform those with nonfamily CEOs in contexts such as industrial districts where conservation strategies are more valuable, but underperform in contexts such as publicly listed firms where market–driven strategies are more valuable. Our commentary highlights the need for future research to examine variance in the self–regulatory mindsets of family and nonfamily CEOs, and to link these differences to firm strategies and performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".