The Role of Consumer Conscientiousness in Marketing Strategies of American and Canadian Vegan Restaurants
Bibliographic record
Abstract
This study examines the similarities and differences in the marketing strategies of vegan restaurants in the U.S. and Canada. Analyzing the websites of 108 vegan restaurants across major cities, the research applies McCarthy & Perreault's 4P framework (Product, Place, Price, Promotion) to assess appeals to ethical consumption and consumer conscientiousness. Findings show a strong emphasis on health, quality, and organic products, with over 70% promoting organic items, aligning with the demand for ethical and eco-friendly choices. Cultural differences emerged: Canadian restaurants emphasize local sourcing, community, and environmental impact, while U.S. establishments focus more on health and wellness messaging. Mentions of animal welfare and spirituality were rare, despite being key to vegan values. While marketing strategies were similar overall, national differences such as health in the U.S. and local sourcing in Canada suggest the need for tailored strategies. The study concludes that ethical and conscientious consumption serves as both a competitive advantage and a core value, and restaurants aligning with these principles can boost consumer loyalty and thrive in the growing market.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".