Business model shift in independent restaurant operation: the COVID-19 impacts
Bibliographic record
Abstract
Restaurant operators adapted their business practices and concepts during the COVID-19. The aim of this research is to identify the transformation of key components of independent restaurant business models. We used a reference framework comprising three key variables—Process, People and Profit—used to distinguish the influence of fast failure concept and the necessary adaptation of the traditional revenue model. The impact of this crisis on their business models and the decisions on which some practices are grounded have allowed us to review traditional processes and assess their relevance. This research is based on 10 interviews with restaurant operators located in various regions of Québec (Canada). The results show that, in this period of insecurity shared by customers and restaurant operators, processes were the components most affected by this upheaval. These processes were characterized by sharing of responsibilities among team members, speed of deployment of tests, and the importance of three elements: partners with multidisciplinary skills, stakeholders, and financial performance centered on profit margins instead of the usual volume aspect. Organic adaptation centered on experimentation and mutual adjustment of resources are what characterized an emergent form of business model in the restaurant industry.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".