Toward an Integrative Model of Effective Fob Succession
Bibliographic record
Abstract
Succession in family owned businesses (FOBs) isinvestigated in order to discover a more complete model of what it takes for asuccession to succeed, determine trends, develop consensus findings, and fillgaps in the conceptual and empirical knowledge, and suggest areas for futureresearch. A literature review was conducted of over 40 articles and seven books aboutFOB succession written in the last 30 years. The examination of an incumbent asa predictor of successful succession revealed three major themes: therelationship between incumbent and successor, the incumbent's motivation, andthe incumbent's personality and needs. Several elements that lead to successful succession in FOBs were determinedto be: the characteristics of the successor and incumbent, the nurturing anddevelopment of successor(s), family relationships, the passing of power, sharedvision, succession planning, and the presence of a board of directors withstrong outsiders as members. Other important factors that can be controlledwere overlooked in previous studies, such as ground rules, selection,nurturing, and family councils. These factors can play important roles insuccession success. (JSD)
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".