An Evaluation of the Relationship Between Human Resource Practices and Service Quality: An Empirical Investigation in the Canadian Hotel Industry
Bibliographic record
Abstract
Human resource (HR) practices have been recognised as a key function in enhancing organisational productivity and competitive advantage. It has been noted that most studies that are based on the relationship between HR practices and performance indicators (e.g., service quality) in hotels hide an important element that tells hoteliers which factors to concentrate on in cases of poor performance. Our study aimed to examine the influence of HR practices on service quality in the Canadian hotel industry. This study seeks to investigate how HR practices (recruitment and selection, training, rewards and incentives, and internal career opportunities) help to improve the service quality. We used a qualitative method by establishing three sets of semi-structured interviews to obtain data from the top to the bottom of the hierarchy within hotels. We found that HR practices help in delivering high service quality. A key contribution of this study that it offers a workable definition of service quality and then a robust model for the relationship between HR practices and service quality that contributes to enhance knowledge of the causal relationship between them. In addition, our study contributes by identifying which HR practices a hotel could adopt to gain a service quality advantage in the marketplace. The data gathered for the proposed study may limit the findings' applicability to independent hotels that are not affiliated with international hotel chains. However, because of the low number of empirical research and the need to get a deeper knowledge of the link between HR practices and service quality, generalisation of the findings from the current descriptive-qualitative study is not a concern. Further research may include some control and context variables (e.g., hotel ownership type, position level, gender) that we did not include in this investigation. In addition, in the future, we recommend using mixed method (quantitative and qualitative) in order to come up with more generalisable results.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".