Improving the Resilience of SMEs: Succession Planning for Canadian Family-Run Small Businesses
Bibliographic record
Abstract
Canadian Family-Run Small Businesses, the most prevalent business type in the private sector economy, are increasingly undergoing succession planning, with the support of advisors. Yet, there is limited available research of their distinct characteristics, needs, behaviours, and actions as they navigate this critical business process. \nThis paper seeks to increase knowledge of and suggest improvements to succession planning for Canadian family-run small businesses and their advisors. Alongside a literature review, the paper provides three mini case studies of family-run small businesses in Ontario, Canada who are currently or have undergone succession planning within the last five years. It also incorporates the perspective of accredited advisors who provide succession planning services to Canadian family-run small businesses. \nThe paper unlocks three key areas of findings. First, the paper identifies that ownership, employee loyalty, family dynamics, and reliance on the business for income are distinct characteristics of Canadian family-run small businesses which influence their succession planning. Second, a proposed step-by-step process is shared for planning which starts when the successors are young, leverages sensemaking as a key planning tool, and recognizes the role of advisors in enabling stakeholders to reconcile economic and non-economic goals and complete the succession. Third, the paper stresses further efforts to understand the role of gender and advisory bias in the process as well as the emergence of interest amongst exiting Canadian-family run small business owners in employee ownership as a viable model.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| 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".