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Record W3153250705 · doi:10.1057/s41267-021-00425-2

Family firm internationalization: Past research and an agenda for the future

2021· article· en· W3153250705 on OpenAlexafffund
Jean-Luc Arrègle, Francesco Chirico, Liena Kano, Sumit K. Kundu, Antonio Majocchi, William S. Schulze

Bibliographic record

VenueJournal of International Business Studies · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsInternationalizationScholarshipFamily businessConceptual frameworkInternational businessField (mathematics)StructuringConceptual modelScope (computer science)DisciplineEmpirical researchBusinessPublic relationsPolitical scienceSociologyMarketingInternational tradeSocial scienceEpistemology

Abstract

fetched live from OpenAlex

Abstract Although the study of family firm internationalization has generated considerable scholarly attention, existing research has offered varied and at times incompatible findings on how family ownership and management shape internationalization. To improve our understanding of family firm internationalization, we systematically review 220 conceptual and empirical studies published over the past three decades, structuring our comprehensive overview of this field according to seven core international business (IB) themes. We assess the literature and propose directions for future research by developing an integrative framework of family firm internationalization that links IB theory with conceptual perspectives used in the reviewed body of work. We propose a research agenda that advocates a cross-disciplinary, multi-theoretic, and cross-level approach to studying family firm internationalization. We conclude that family firm internationalization research has the potential to contribute valuable insights to IB scholarship by increasing attention to conceptual and methodological issues, including micro-level affective motivations, background social institutions, temporal perspectives, and multi-level analyses.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
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 head0.105
GPT teacher head0.376
Teacher spread0.271 · 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 designNot applicable
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

Citations22
Published2021
Admission routes2
Has abstractyes

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