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)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".