Promoting service excellence for tourist destinations
Bibliographic record
Abstract
Purpose This paper aims to understand the relationship between employee satisfaction and customer satisfaction and its impacts on the long-term sustainability of Hemsedal Ski Resort, Norway. Design/methodology/approach This study uses a mixed method approach. Focusing on the case of Hemsedal, Norway, the authors employ survey design to measure employee service attitudes as well as guest satisfaction and loyalty. Correlation analysis and structural equation modeling (SEM) analysis is used to investigate the relationships between the constructs. Findings Results from the four-year programme reveal that the correlation between employee service attitude and customer experience is strongest for behavioural loyalty which was found to have a direct and observable effect for the customer and that working to teach and train employees is important. Further, results showed that guest satisfaction with service not only influenced loyalty to the company that provided the service, but also loyalty to the destination where the company was situated. In explaining the relationships between levels of employee service attitude, customer satisfaction and community sustainability at Hemsedal ski resort, results showed that through partnership and cooperation, training and development have benefitted the individual companies, the destination and local community at large. Practical implications Results suggest that managers of tourism destinations should focus on employee motivation and training to improve their guests’ satisfaction and loyalty, their competitiveness and sustainability for the future. Originality/value The Service Excellence Project at Hemsedal, Norway demonstrates that mountain destinations can have a positive influence on their competitiveness and their sustainability by instituting a programme that works with employees, customers and businesses to promote a climate of service excellence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".