Family ties and emotions: a missing piece in the knowledge transfer puzzle
Bibliographic record
Abstract
Purpose The paper aims to develop a model of knowledge transfer that considers kinship ties and emotions in family‐based firms. Design/methodology/approach There exist several models, which show how information flows among individuals and within organizations. One school of thought is known as Cultural‐Historical Activity Theory (CHAT), which was initially formulated by Lev Vygotsky, the Founder of the school. However, when analyzing CHAT within the family business context, the model no longer holds true. This paper examines knowledge‐transfer mechanisms through the lens of family firms. Findings Family traditions, ties, and emotions, which are not considered in the original learning framework, affect knowledge transfer, commitment, and the motivation of family members. Research limitations/implications Based on CHAT and subsequently on other social networks theories, a more appropriate next generation learning model is developed which explains how intergenerational knowledge transfer takes place within family firms. Practical implications This paper improves the understanding of how family members' shared knowledge (i.e. traditions) may become sources of competitive advantages for the family firm (i.e. long‐term survival). Originality/value This paper is among the first known to examine knowledge‐transfer mechanisms specifically for family‐based businesses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".