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Record W4388827929 · doi:10.1108/ijebr-05-2023-0516

A multi-voiced account of family entrepreneuring research: expanding the agenda of family entrepreneurship

2023· article· en· W4388827929 on OpenAlexaff
Haya Al‐Dajani, Nupur Pavan Bang, Rodrigo Basco, Andrea Calabrò, Jeremy Cheng, Eric Clinton, Joshua J. Daspit, Alfredo De Massis, Allan Discua Cruz, Lucia Garcia‐Lorenzo, William B. Gartner, Olivier Germain, Silvia Gherardi, Jenny Helin, J. Miguel Imas, Sarah Jack, Maura McAdam, Miruna Radu-Lefebvre, Paola Rovelli, Malin Tillmar, Mariateresa Torchia, Karen Verduijn, Friederike Welter

Bibliographic record

VenueInternational Journal of Entrepreneurial Behaviour & Research · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsEntrepreneurshipFamily businessNoveltyLeverage (statistics)OriginalityAgency (philosophy)Context (archaeology)SociologyPublic relationsField (mathematics)Value (mathematics)MarketingKnowledge managementBusinessPolitical scienceSocial scienceQualitative researchPsychologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

Purpose This conceptual, multi-voiced paper aims to collectively explore and theorize family entrepreneuring, which is a research stream dedicated to investigating the emergence and becoming of entrepreneurial phenomena in business families and family firms. Design/methodology/approach Because of the novelty of this research stream, the authors asked 20 scholars in entrepreneurship and family business to reflect on topics, methods and issues that should be addressed to move this field forward. Findings Authors highlight key challenges and point to new research directions for understanding family entrepreneuring in relation to issues such as agency, processualism and context. Originality/value This study offers a compilation of multiple perspectives and leverage recent developments in the fields of entrepreneurship and family business to advance research on family entrepreneuring.

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)0.007
metaresearch head score (Gemma)0.002
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 score0.358
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0030.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.278
GPT teacher head0.436
Teacher spread0.158 · 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

Citations20
Published2023
Admission routes1
Has abstractyes

Explore more

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