Managerial decision-making during the COVID-19 pandemic and its impact on the sustainability initiatives of Canadian foodservice businesses
Bibliographic record
Abstract
COVID-19 had a major impact on the Canadian foodservice sector. Like most countries, the pandemic in Canada resulted in various periods of lockdown. The pandemic placed great strain on many establishments and had a major impact on the pre-COVID-19 sustainability initiatives of the Canadian foodservice sector. The purpose of this study was to observe managerial decision-making in Canadian foodservice businesses during lockdown and reopening, focusing on the impact of those decisions on pre-COVID-19 sustainability initiatives. We linked the outcomes to the theory of decision-making by objection during times of crises. This study used semi-structured interviews over a two-month period in mid-2020 with three Canadian foodservice establishments. Our results showed that decision-making impacted the environmental sustainability initiatives in foodservice establishments by imposing a throwaway culture for food and personal protective equipment. The pandemic also impacted social and economic initiatives, created higher operation costs, a complexity of government intervention and the managing of mental health. This study showed that the COVID-19 pandemic provided an opportunity to develop theories of managerial decisions during crises and disasters that are natural, versus human-based crises, with pandemics situated between those two concepts. Future research could investigate the impact of decision-making on other initiatives within foodservice businesses.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 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".