Attracting and retaining quality human resources for Niagara's hospitality industry
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify the best approaches management should embrace to successfully attract and retain high quality human resource talent within the Niagara region's hospitality industry. Design/methodology/approach A selected cross‐section of relevant and recent publications are reviewed. The key findings from a mini survey involving 14 senior hospitality managers in the Niagara region are shared. Findings This paper suggests that the hospitality managers should: understand the basics related to good human resource management practices; know the “fair market value” for each position; foster relationships with colleges and universities to tap into student labor; encourage mature workers to apply for part‐time work; and cultivate a good relationship with seasonal employees and educate them on the rewards of a career within the hospitality industry. Through the industry survey, it was discovered that hospitality managers within the Niagara region are already executing some of these strategies. However, it was concluded that a stronger working relationship with the seasonal employees is required in the region. Originality/value Two academics with hotel general manager experience in five countries join hands with the president for three four‐diamond hotels to write this paper. Given the background of the authors, it is expected that the viewpoints would be welcomed by hospitality managers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".