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Record W4306870263 · doi:10.1177/02662426221127412

Opening up the black box of family entrepreneurship across generations: A systematic literature review

2022· article· en· W4306870263 on OpenAlexaff
Paolo Capolupo, Lorenzo Ardito, Antonio Messeni Petruzzelli, Alfredo De Massis

Bibliographic record

VenueInternational Small Business Journal Researching Entrepreneurship · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsMount Royal University
Fundersnot available
KeywordsEntrepreneurshipNature versus nurtureDimension (graph theory)Family businessSystematic reviewPsychologySociologySocial psychologyManagementPolitical scienceEconomics

Abstract

fetched live from OpenAlex

What makes some families more entrepreneurial than others? How are they able to nurture entrepreneurship across generations? These are fundamental questions for family business and entrepreneurship research. In particular, the multigenerational dimension of entrepreneurial families (EFs) and the new family logics that emerge as the family grows may lead to different types of entrepreneurial activities. To shed light on these questions, we conduct a systematic literature review of 90 peer-reviewed articles focusing on the characteristics and behaviours of EFs, family members and their business activities. Specifically, we first identify and categorise the family-related factors characterising EFs across generations. Second, we link the identified factors to different types of entrepreneurial activities pursued as the generations advance, distinguishing two dimensions: mode of organising (internal vs. external), and degree of relatedness (related vs. unrelated). Finally, we highlight the main gaps in the literature and provide a future research agenda.

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)0.016
metaresearch head score (Gemma)0.069
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.069
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0200.014
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0020.001
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 head0.066
GPT teacher head0.323
Teacher spread0.257 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations45
Published2022
Admission routes1
Has abstractyes

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