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Mapping The Literature of Family Business Management (FBM): Bibliometric Analysis and Systematic Review

2023· article· en· W4385821849 on OpenAlexaboutno aff
Prashant Pareek, Badrinarayanan Gopalakrishnan

Bibliographic record

VenueMSA- Management Sciences Journal/MSA-Management Sciences Journal · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsnot available
Fundersnot available
KeywordsSystematic reviewBibliometricsManagement scienceComputer scienceEconomicsLibrary sciencePolitical scienceMEDLINE

Abstract

fetched live from OpenAlex

The purpose of this paper is to conduct a Systematic Review and Bibliometric Analysis in the area of Family Business Management. Data was collected from Scopus database, with the help of advance search feature, initially 81 articles were extracted and after filtration finally 43 research articles were considered for this study. MS excel and VOS viewer were used for analysis. This study gave useful insights which can be of scholarly implications in future. After the year 2011 there is a rise in number of research articles published in Scopus database from the domain of Family Business Management, in this study we found that USA, Canada and Spain are the significant contributors of Articles in the domain of FBM. So far most of the articles published in this domain of FBM are based on the key themes of Gender roles, Succession planning, and ownership related issues. This study indicates the future areas of research in the domain of FBM, in future scholars can undertake studies like Effectiveness of various functional areas like Marketing, Finance, HR, Operations and Supply chain in the context of Family owned businesses. Even the usefulness of emerging areas like Fintech, Digital marketing, and HR Analytics can be studied in future.

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)0.020
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics, Science and technology studies, Scholarly communication
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0860.330
Science and technology studies0.0050.001
Scholarly communication0.0070.008
Open science0.0050.002
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 head0.038
GPT teacher head0.281
Teacher spread0.243 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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