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Record W1990858008 · doi:10.1108/00400910410569597

Qualities of an effective successor: the role of education and training

2004· article· en· W1990858008 on OpenAlexaffabout
A. Bakr Ibrahim, Khaled Soufani, Panikkos Poutziouris, J. Lam

Bibliographic record

VenueEducation + Training · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsConcordia UniversityCanadian Imperial Bank of Commerce (Canada)
Fundersnot available
KeywordsSuccessor cardinalResource (disambiguation)Human resourcesTraining and developmentProcess (computing)Family businessSelection (genetic algorithm)BusinessHuman resource managementTraining (meteorology)MarketingPsychologyManagementPublic relationsKnowledge managementPolitical scienceEconomicsComputer science

Abstract

fetched live from OpenAlex

Small family firms represent the predominant organizational form in Canada. They are perceived to be crucial to the development and growth of the Canadian economy. Despite this, scant attention is given to the study of human resource management practices in the specialist family business literature. A key human resource issue in family firms, which has been documented as a potential source of problems, is succession, selection and training. The objective of this research is to explore the qualities that are considered critical to an effective family business successor and discuss the crucial role that education and training could have in enhancing the qualities and skills of a successor. Results suggest that three factors are critical to an effective human resource strategy concerning the selection process of a successor. These include the successor's capacity to lead, his/her managerial skills and competencies, and the willingness and commitment of the successor to take over the family business and to assume a leadership role.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.003
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.020
GPT teacher head0.275
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations70
Published2004
Admission routes2
Has abstractyes

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