Examination of customer-centric measures among different types of customers in the context of major Canadian ski resort
Bibliographic record
Abstract
Purpose Customer-centric measures such as customer satisfaction and repurchase intent are important indicators of performance. The purpose of this paper is to examine what is the strength and significance of the path coefficients in a customer satisfaction model consisting of various customer-centric measures for different types of ski resort customer (i.e. day, weekend and ski holiday visitors as well as season pass holders) in a ski resort in Canada. Design/methodology/approach The results were analyzed using the partial least squares structural equation modeling approach for the four different types ski resort visitors. Findings There appeared to differences in the strength and significance in the customer satisfaction model relationships for the four types of ski resort visitors indicating that the a priori managerial classification of the ski resort visitors is warranted. Originality/value The research pinpoints differences in the strength and significance in the relationships between customer-centric measures for four different types ski resort visitors, i.e. day, weekend and ski holiday visitors as well as season pass holders, which have significant managerial implications for the marketing practice of the ski resort.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".