Family, business and power: illustrating three extreme cases
Bibliographic record
Abstract
Purpose The purpose of this paper is to illustrate how small family business (SFB) leader‐founders exhibit a dominant logic of action over less dominant prevailing ones. The authors investigate three logics of action: family, power and economic. Design/methodology/approach An exploratory qualitative research is conducted based on case studies. The authors use Cisneros and Genin's conceptual model, to identify, through an iterative sampling frame, three extreme SFB cases where in the first the leader exhibits a dominant family logic, in the second, a dominant power logic and in the third, a dominant economic logic. Findings The authors illustrate the characteristics of the SFB leaders when they exhibit a dominant logic of action and also present some of the implications of SFB leaders’ dominant logics of action on the SFB and the family and non‐family members. Research limitations/implications The three extreme case studies provide an important building block for future studies based on larger samples of SFBs. However, the authors’ results cannot be generalised due to the exploratory nature of the study. Practical implications The paper highlights the importance, for practitioners and researchers alike, of being able to diagnose when SFB leaders use a dominant logic of action. The paper also accentuates the need for a greater awareness of logics of action in training programmes for SFB leaders and for consultants who work with those leaders. Originality/value The concept of logics of action has never been previously empirically applied to large, medium or small family businesses. The paper highlights the relevance of identifying dominant logics of actions in SFB leaders.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".