Outsourcing to Online Food Delivery Services: Perspective of F&B Business Owners
Bibliographic record
Abstract
Purpose - There are two main purposes for this study. The first aims to provide a comprehensive review on the past literature review on outsourcing in the foodservice business sector by identifying the commonalities and filling in the gaps. The second purpose is to contribute to the limited research works that cover this field of study. Design/methodology/approach - This study adopted a qualitative research method with data collected through physical interviews with F&B business owners based on their knowledge, attitudes, perspectives, and needs on outsourcing to online food delivery services. Findings - In most research works, motivators, trends, risk, benefits, and relationships are discussed. The research on business owner's perspective in relation to outsourcing food delivery service is found to be scarce. The findings in this research work suggests that there are three main driven factors to the business ownersâ behavioural intention to outsource food delivery service to third-party online food delivery service provider. These factors are listed as increase of revenue, wider customer reach and expand customer base. The findings propose that business owners should consider and pay close attention to the changes in the consumer preference as it still remains dynamic, if they want to remain competitive in the foodservice industry. On top of that, this research work also provides insights and recommendations to third-party online food delivery service providers. This is one of the few research works that study the determining factors for restaurant businesses to outsource to online food delivery service providers. Practical implications - The continuous and drastic changes in the restaurant business reflects the worthiness for further researches considering the booming potential in the foodservice industry where online delivery service is taking up a big part of the market share. In order to reduce cost and maximise profit, many restaurants are asserting effort in outsourcing to online food delivery service providers. It may be difficult for business restaurant owners to explore the opportunities or challenges associated with the outsourcing to online food delivery service in the existing studies and literature. This study identifies the areas that are less explored in the literature, in the views of restaurant business owners. Originality/value - This study is a first attempt to organise the outsourcing literature with the view of restaurant business owners using non-statistical and in-depth analysis in exploring the contents of the studies in a new approach.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".