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Succession and Nonsuccession Concerns of Family Firms and Agency Relationship with Nonfamily Managers

2003· article· en· W2002603455 on OpenAlexaffabout
Jess H. Chua, James J. Chrisman, Pramodita Sharma

Bibliographic record

VenueFamily Business Review · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsWilfrid Laurier UniversityUniversity of Calgary
Fundersnot available
KeywordsAgency (philosophy)Ecological successionBusinessFamily businessFocus (optics)Empirical researchMarketingPublic relationsManagementSociologyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

This article consists of two parts. The first part reports findings from a survey of the issues facing top executives in 272 Canadian family firms. Results show that succession is their No. 1 concern, thus supporting the predominant focus of family business researchers on succession issues. Results also show that concern about relationships with nonfamily managers is a close second in importance. The second part of the article uses Agency Theory to explain why relationships with nonfamily managers are so important. Empirical results show that both the extent and the criticality of a firm's dependence on nonfamily managers are statistically significant determinants of the importance. This study implies that relationships with nonfamily managers is a neglected research topic and points to a new direction for research in family business management.

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)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.118
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.266
Teacher spread0.226 · 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 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

Citations501
Published2003
Admission routes2
Has abstractyes

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