Winners and losers: understanding a mutually beneficial third-party food delivery business model for restaurants and customers
Bibliographic record
Abstract
Purpose The purpose of this study was to understand the factors that attract and deter restaurants and customers from entering into a relationship with third-party food delivery apps; another goal was to determine a framework for food delivery service that can benefit all stakeholders, including the end consumer. Design/methodology/approach Using a grounded theory approach, 16 semi-structured interviews were conducted with restaurant operators and customers in Canada to determine these factors. Findings Restaurants and customers are attracted to food delivery apps because of the convenient network they provide and the ability to either earn or save money. The food delivery app business model is threatened by the high costs to participate. Restaurants and consumers found the financial terms confusing and service quality low. Practical implications Food delivery app companies need to ensure that all stakeholders are benefiting from using food delivery apps to keep them committed long-term. Food delivery app companies are recommended to keep their rates transparent and fair and develop better packaging and technology to improve the food and service quality. Restaurant operators should collaborate to negotiate lower commission fees. Originality/value This is the first known qualitative study that combines the viewpoints of restaurant operators and customers as an insider perspective. As a result, it provides an understanding of the difficulties each stakeholder group faces in the food delivery app ecosystem and provides a suggested framework for food delivery app ecosystems to be viable long-term.
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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".