Human resource challenges in Canada’s hospitality and tourism industry
Bibliographic record
Abstract
Purpose This paper aims to explore the challenges encountered by the hospitality and tourism industry in managing the labour challenges it faces presently and will face in the coming years. Although there are several issues at play, there are actions that industry members can take both internally and by advocating externally for change. Design/methodology/approach This paper draws on insights from three industry members and two academics to explore key areas in which action can be taken to address labour demand challenges in the hospitality and tourism workforce. The identified action items combine these various types of expertise to provide a holistic frame of action. Findings The Canadian hospitality and tourism industry is facing an ever-increasing labour demand shortage. Industry members can confront this on multiple fronts, from front-line employee satisfaction to more regional and national advocacy efforts. A combination of activities is recommended. Practical implications Hospitality and tourism industry members can take numerous actions from this analysis, including developing stronger organization cultures that align with employee needs, exerting effort in balancing wage gap issues and maintaining pressure on government partners to provide support for establishing hospitality and tourism, so that it is viewed as a valuable career path. Originality/value This paper increases knowledge in the hospitality and tourism field by combining the current human resource management theory with observations from industry experts on the needs that exist now and are predicted in the coming years.
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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 | 0.003 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".