Sustainability in Franchised Food Business
Bibliographic record
Abstract
Abstract: Sustainability in the franchised food business sector is a matter of adopting green practices, sourcing materials ethically, and promoting community engagement to ensure that the core business is profitable and makes a positive impact in the local community. This research tested the sustainability practices implemented by franchised food businesses in a highly urbanized city in Central Visayas during the first quarter of 2025. The study focused on critical areas of sustainability. Namely, reducing water usage, minimizing food waste, using sustainable packaging, and introducing healthy menu options. A descriptive-comparative research study was used, and data were collected using a validated self-constructed questionnaire from 50 respondents, including franchise owners and employees. Overall, the level of sustainability practices was found to be high. Food waste management and water conservation scored the best, while rainwater collection scored the least practiced. Differences were found when the results were grouped according to variables: number of employees, type of business, ownership, capitalization, and number of years in the business. The study found that although many franchised food businesses already implement sustainable practices, they require more effort in areas like showing nutrition information and encouraging customers to use eco-friendly packaging. These findings can help franchise owners, policymakers, and advocates make better decisions and promote environmental and social responsibility in the food business. Keywords: Sustainability, Franchise, Food 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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".