First-Generation, Second-Generation and Third-Generation Family Business: A Manova Comparison
Bibliographic record
Abstract
This research builds on an earlier study by Sonfield and Lussier (2002), in order to expand our comprehension of similarities and differences between first-generation family firms (1GFFs), second-generation family firms (2GFFs), and third-generation family firms (3GFFs).A lengthy literature review is presented, as are 12 testable hypotheses.The sample consists of 159 surveyed family firms in New York and Massachusetts, a much larger sample than was used in Sonfield and Lussier (2002).Rather than using one-way ANOVA as in the previous study, the much stronger MANOVA test is used. The results of the analysis supportonly 3 of the 12 hypotheses.A statistically significant difference is found among generations in the creation of specific succession plans, with 1GFFs planning less than the other generations.Also, no difference exists among generations regarding the influence of the firm's founder.The final hypothesis, that hypotheses 1 through 11 together create a model that reveals significant differences between generations, is accepted, and a limitation of this finding is discussed. The implications and suggested areas for future research are discussed.(AKP)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".