The relation between governance and growth in small and midsize family farms: proposal for a new transition model
Bibliographic record
Abstract
Purpose This study explores the interplay between governance and growth in privately held, family-run small and midsize enterprises. Design/methodology/approach We used an abductive, exploratory and longitudinal methodology, using data from two semi-structured interviews and an online questionnaire completed by 17 owner-managers of family farms in Quebec, Canada. Findings The study highlights the crucial role of corporate governance in facilitating the growth of family farms. The findings indicate that implementing formal governance mechanisms early in a firm’s lifecycle can significantly facilitate growth. Three distinct phases of corporate governance were identified, with governance either concurrently or sequentially related to growth, depending on the phase. Farms progressed to a new lifecycle phase when gaps arose between their growth trajectory and their human and organizational capacities or due to changes in ownership or generational turnover. Practical implications The findings indicate that, to foster growth, farmers should proactively establish formal governance mechanisms from the onset, tailored to their specific growth objectives. Recommended practices include financial, strategic and investment planning; delegating operational responsibilities and the creation of governance bodies, such as family councils or boards. Originality/value This study advances our understanding of corporate governance lifecycles by confirming the dynamic relationship between growth and governance in private, family-run enterprises, including during the early stages of development.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".