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Record W2100180614 · doi:10.1287/orsc.1110.0728

Family Firm Governance, Strategic Conformity, and Performance: Institutional vs. Strategic Perspectives

2012· article· en· W2100180614 on OpenAlexaff
Danny Miller, Isabelle Le Breton‐Miller, Richard H. Lester

Bibliographic record

VenueOrganization Science · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsUniversity of AlbertaHEC Montréal
Fundersnot available
KeywordsConformitySocioemotional selectivity theoryCorporate governanceMicrofoundationsSituational ethicsBusinessInstitutional theoryEconomicsIndustrial organizationMicroeconomicsPsychologySocial psychologyManagementFinance

Abstract

fetched live from OpenAlex

A fundamental schism divides the family firm and strategy literatures, on the one hand, and the institutional literature, on the other, regarding both the situational prevalence and the utility of conforming behavior. The first two schools, respectively, view strategic differentiation as especially common among family firms and an important source of competitive advantage. By contrast, the reasoning of institutionalists would suggest that family firms will be subject to unusually powerful motivations to conform, in part because of their pursuit of socioemotional wealth objectives. Unfortunately, the relationships between conformity and family firm governance—and, in fact, governance in general—have not been amply studied. This analysis of Fortune 1000 firms finds considerable support for the institutional perspective: family involvement is related to greater, not lesser, conformity in many aspects of strategy. Although strategic conformity related to superior returns on assets, it did not enhance firm market valuations.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.233
Teacher spread0.205 · 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

Citations381
Published2012
Admission routes1
Has abstractyes

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