L'échec des successions des fondateurs d'entreprise : avoir les défauts de ses qualités
Bibliographic record
Abstract
Résumé Many business owners spend a great deal of time thinking about their succession. And yet, the evidence clearly shows that ensuring a family succession is a very difficult challenge that often ends in failure. This article, which is based on coaching experiences spread over a period of 15 years and involving 11 company founders, provides us with insight into the human factors at work within entrepreneurial families. We observe that the success of company founders is closely linked to the aggressive approach they take to managing their businesses. This aggressiveness is related to three attributes possessed by these founders: a strong focus (they are constantly thinking about their company); independent thinking (they are independently minded); and a strong vitality (they have a lot of energy). However, while this aggressive approach can be an advantage for the founder’s business, it can work against members of the founder’s family – that is, the spouse or future successors. Our study shows that the spouse of a company founder often plays a decisive role in the success or failure of a succession, because the spouse must focus his or her attention on the family and act as a buffer against the aggressiveness of the founder so that their children can develop the skills and aggressiveness necessary to become successors who will be successful in business.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".