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Record W4379212092 · doi:10.1108/ijebr-12-2021-1056

How family firms can avoid the trap of strong social ties and still achieve innovation: critical roles of market orientation and transgenerational intent

2023· article· en· W4379212092 on OpenAlexaff
Mumin Dayan, Poh Yen Ng, Dirk De Clercq

Bibliographic record

VenueInternational Journal of Entrepreneurial Behaviour & Research · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsBrock University
Fundersnot available
KeywordsTransgenerational epigeneticsOriginalityLeverage (statistics)Interpersonal tiesEntrepreneurial orientationBusinessMarket orientationFamily tiesFamily businessSample (material)Empirical evidenceMarketingEntrepreneurshipPsychologySocial psychologyCreativityFinance

Abstract

fetched live from OpenAlex

Purpose To extend family business research, this article proposes and tests a curvilinear relationship between social ties and family firm innovation, with the firm's market orientation and transgenerational intent as moderators. Design/methodology/approach Representatives from a sample of 150 family firms in the United Arab Emirates completed self-administered questionnaires. Regression analyses on the collected data test the conceptual model and proposed hypotheses. Findings The empirical study reveals an inverted U-shaped relationship, such that a high market orientation mitigates the diminishing returns of social ties on enhancing family firm innovation. Similarly, at high levels of transgenerational intent, family firm innovation increases due to social ties, instead of exhibiting diminishing returns. Originality/value These results help explain contradictory outcomes previously attributed to social ties and offer clear guidelines for how family firms can leverage these ties more effectively to enhance their own innovation.

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)0.010
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.000
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 head0.078
GPT teacher head0.353
Teacher spread0.275 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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Same venueInternational Journal of Entrepreneurial Behaviour & ResearchSame topicFamily Business Performance and SuccessionFrench-language works237,207