Examination of customer-centric measures among different types of customers in the context of major Canadian ski resort
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it