Building family business identity through transgenerational narratives
Bibliographic record
Abstract
Purpose The purpose of this paper is to understand how family businesses (FBs) build their collective identity through transgenerational narratives. The authors examine the processes through which organizational meanings are socially constructed through narratives about individuals who are closely linked to the organizations (and their family). Design/methodology/approach Based on qualitative research, the authors study a 180-year old Spanish Pharmaceutical FB. Using longitudinal data, the authors analyze the narratives of six family members and two non-family executives. The authors use open-ended questions to allow interviewees to elaborate their own stories, following previous studies using extended narratives that leave the stage to the narrator. Findings Findings based on the stories of the eight interviewees (voice) suggest that the FB identity was initiated by the founder’s way to grow the business (fictionality). In turn the family shaped the identity of the FB, being reshaped by the stories arising from next generations’ entry into the business (reflexivity). While the FB identity reflects that of the owners, this identity is enduring but dynamic (temporality), not only shaped by the business family behind, but also conditioned by the environment. Originality/value The authors contribute to the growing literature adopting a narrative method to study phenomena in FBs. Thanks to the richness of the empirical material, a narrative method is particularly suited – and novel – for understanding collective identity, a crucial organizational resource that is closely linked to leadership in the FB.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".