Empowering Small Enterprises by Driving Value and Flow Through Systemic Strategic Planning: An exploratory study into the strategic planning of Small Enterprises in the Canadian Provinces of Ontario and Quebec
Bibliographic record
Abstract
The journey takes us at the heart of strategic planning in small enterprises (SEs). We explore the role of strategy and business models in SEs and how the two are adopted by SE managers, the level of involvement of their employees, and the outcomes on the enterprise. \n \nIn the increasingly fast-paced and highly competitive business landscape systemic strategic planning provides small enterprises (SEs) with valuable opportunities to understand and thoroughly examine their enterprises’ internal and external environments, and identify ways to enhance the likelihood of steering the company towards a preferred future. \nIn this in situ research initiative, which focuses on enterprises in the Provinces of Ontario and Quebec, we seek primarily to gather a better understanding of small enterprise managers’ views on strategic planning. Secondly, we explore tools, techniques, and practices used. Thirdly, we explore the growing trend of business modeling and how it is welcomed by SEs. \nOur research team has uncovered a limited use or absence of systemic strategic planning amongst SEs. As a result, many SE managers have a preconceived notion that strategic planning is not for them and thus, at a cost, turn their attention to what is inherently most intuitive to them: operational planning. \nSE managers often focus on an isolated aspect of the process, such as sales or financial performance, rather than the integrated process, and thus fail to close the loop. \nOur research findings have identified a combination of key factors that impact the adoption of strategic planning. To that affect, several recommendations are proposed to the stackeholders as means of increasing the systemic adoption of strategic planning in SEs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".