Designing the Sweet Spot: The Next Food-as-a-Service Approach for Independent Hospitality Sector
Bibliographic record
Abstract
This research explores how emerging technologies can support Toronto’s Quick Service Food Establishment (QSFE) SME (Small and Medium Enterprises) in adopting circular economy practices to tackle key sustainability challenges such as excessive food waste, slow digital adoption, and shifting regulatory landscapes. Focusing on the intersection of technology, business resilience and urban food systems development, the study explores how digital tools can help small food businesses transition toward more regenerative models while remaining economically viable. A mixedmethods approach combining design thinking and strategic foresight grounded in Dator’s Four Futures framework was used to conduct user research, stakeholder analysis, and systems mapping to identify root causes and future opportunities. The research proposes a Food-as-a-Service (FaaS) model that leverages shared infrastructure, AI-enabled local production, and blockchain-driven transparency to reduce waste, engage consumers, and lower operational costs. A backcasting framework charts a realistic pathway toward 2035, aligning technological shifts with policy evolution and behavioural change. By connecting speculative futures to grounded design interventions, this research demonstrates how foresight-driven strategies can inform actionable, scalable solutions for circular food systems, starting in Toronto and extending to other urban environments navigating similar transitions. Keywords: Circular Economy, Smart Food System, Strategic Foresight, Innovation Business Model, Digital Transformation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".