Strategic Planning Processes for Profitability and Sustainability of Small Restaurants in Alberta
Bibliographic record
Abstract
Some small restaurant managers struggle to develop and implement strategic planning processes to achieve profitability and sustainability. As customer attendance declines, these managers face economic challenges and reduced daily sales, often stemming from inadequate strategic planning that could support sustained profitability and long-term success. Grounded in resource-based view theory and dynamic capability theory, the purpose of this qualitative multiple case study was to explore the successful strategic planning processes that small restaurant business managers in Alberta, Canada, use to achieve profitability and sustainability beyond 5 years. The participants were six small restaurant managers who successfully implemented strategic planning processes to achieve profitability and sustainability beyond 5 years. Data were collected using semistructured interviews and a review of employee training, staffing schedules, workflow, inventory, and customer service documents. Through thematic analysis, five themes were identified: (a) operational efficiency, (b) management and supervision, (c) inventory management, (d) staffing and training, and (e) financial planning. A key recommendation is for restaurant managers to standardize workflow systems, utilize inventory management software, optimize resource allocation, and improve employee cross-training. The implications for positive social change include the potential for small restaurant managers to contribute to local economic growth by creating jobs, supporting other businesses, and fostering cultural diversity and inclusion through inclusive work environments and sustainable practices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 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".