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Record W2117121019 · doi:10.5430/ijba.v4n6p120

Knowledge Creation in Family Businesses and Its Importance for Building and Sustaining Competitive Advantage during and after Succession

2013· article· en· W2117121019 on OpenAlexvenueno aff
Mojca Duh, Marina Letonja

Bibliographic record

VenueInternational Journal of Business Administration · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsnot available
Fundersnot available
KeywordsSuccessor cardinalTacit knowledgeCompetitive advantageKnowledge managementBusinessContext (archaeology)Knowledge value chainExplicit knowledgeOrder (exchange)Knowledge creationProcess (computing)Organizational learningQuality (philosophy)MarketingComputer science

Abstract

fetched live from OpenAlex

The family business’s tacit knowledge, embedded in its founder, and its transmission is found to be important for building and sustaining competitive advantage since this type of knowledge is difficult to trade and imitate, scarce, appropriable and specialized. The purpose of our research was to broadening our understanding of family businesses tacit knowledge and its creation during the succession process by applying the concept of knowledge creation through so called SECI process. The case study-based findings showed that founders and successors find mentoring, internal/individual training, and involving in the meetings with business partners as the most used knowledge creating activities. We suggest that tacit knowledge creation during succession should be placed in broader context of organizational knowledge creation in order to raise the total quality of successor’s knowledge and adding new knowledge thus contributing to building family business’s competitive advantage.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.075
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.005
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 head0.011
GPT teacher head0.277
Teacher spread0.266 · 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

Citations13
Published2013
Admission routes1
Has abstractyes

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