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Record W2734666671 · doi:10.5430/ijba.v8n5p11

Finance in Family Business Studies: A Systematic Literature Review

2017· article· en· W2734666671 on OpenAlexvenueno aff
Carmen Gallucci, Rosalia Santulli, Michela De Rosa

Bibliographic record

VenueInternational Journal of Business Administration · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsnot available
Fundersnot available
KeywordsSocioemotional selectivity theoryFamily businessPerspective (graphical)SchematicValue (mathematics)BusinessFinanceEconomicsMarketingPublic relationsPolitical scienceComputer sciencePsychology

Abstract

fetched live from OpenAlex

The aim of this paper is to examine how the family business literature and financial issues interact. It is carried out on 448 articles methodically selected from 24 management journals and 102 finance journals. After discussing the periodical development of literature, the study identifies the key research topics in the both fields and by crossing the results it identifies specific interaction trends. The classical financial theory cannot be applied to family businesses. The outcome of this research discloses that socioemotional wealth could contribute to determine a new perspective under which to examine the interplay between the family and the business. The schematic overview on the state of art let us reflect on the gaps that could be bridged, through a coherent advancing of financial studies in family business. The understanding that classical financial theory cannot be applied to family businesses and the discovery of peculiar family business dynamics can assure the continuity of the firm by defying the growth of firm’s value in term of the emotional components beyond the financial considerations. This review shows to researchers a wider scenario of the family business by leading to many challenges and gives an essential support to scholars in advancing a beneficial research.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.007
Open science0.0010.000
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 head0.038
GPT teacher head0.322
Teacher spread0.284 · 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.

Study designSystematic review
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

Citations6
Published2017
Admission routes1
Has abstractyes

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