An exploratory study of outsourcing of foodservice operations in Canadian hotels.
Bibliographic record
Abstract
An exploratory study of outsourcing of foodservice operations in Canadian hotelsOutsourcing in hotels has received sporadic attention in the academic literature but remains a key strategic decision, particularly as it pertains to food and beverage operations.The purpose of this paper was to investigate the outsourcing practices of food and beverage operations of selected Canadian independent hotels.Specifically, it attempted to determine: the various outsourcing practices and the hotels' reasoning behind such decisions, the hotels' perceived benefits from outsourcing their food and beverage services, and the factors the hotels considered when contemplating the outsourcing of their food and beverage services.Most of the findings from the interviews with hotel operators were generally consistent with respect to these reasons.However, mixed responses were reported with respect to the factors and drawbacks the respondents believed should be considered before outsourcing.Overall, respondents suggested that decisions were made with respect to financial considerations, ability to focus on core competencies, and strategic intent.They also suggested that the results of the outsourcing decisions were mixed.Overall, it was concluded that the independent hotels were found to outsource their food and beverage service for the many of the same reasons hotel chains do.Challenges with regard to the outsourcing decision are discussed and recommendations are made with respect to continued challenges around the outsourcing question as well as directions that future research could take.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".