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Record W1964883958 · doi:10.3917/riges.361.0041

L'échec des successions des fondateurs d'entreprise : avoir les défauts de ses qualités

2011· article· en· W1964883958 on OpenAlexaffvenue
Carol Bélanger

Bibliographic record

VenueGestion · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsSpouseVitalityFamily businessCoachingEntrepreneurshipSuccession planningManagementPsychologyBusinessSociologyPolitical sciencePublic relationsMarketingEconomicsPhilosophyLawTheology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.079
GPT teacher head0.266
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2011
Admission routes2
Has abstractyes

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