Seasonal Variation in Visitor Satisfaction and Its Management Implications in Banff National Park
Bibliographic record
Abstract
Seasonal variations in tourist satisfaction is an important issue for the sustainable management of national parks worldwide. Visitors should have high-quality experiences in both the high season and the off-season. This research investigated visitor satisfaction patterns and determinants in Banff National Park in different seasons. The study was conducted through a face-to-face questionnaire survey that collected visitor demographic, expectation and satisfaction data in July 2019 (high season) and December 2019 (off-season) in Banff National Park. The data analyses were based on a sample of 741 respondents and were processed using principal component analysis, correlation analysis and logistic regression models for different seasons. There were significant differences in visitor satisfaction levels and their determinants in different seasons. The quality of the park’s natural characteristics and the park’s activities were the most important determinant of visitor satisfaction in the high season and off-season, respectively. The correlation between visitor satisfaction and expectations in the high season was generally negative, whereas all correlations in the off-season were positive. The results fill a knowledge gap by examining the seasonal differences in visitor experience and their determinants in the national park, and by building a bridge between visitor experience and tourism seasonality. The findings may assist both practitioners and scholars in understanding visitor expectations and satisfaction in different seasons. They may assist in the prioritization and effective management of the park to optimize the visitor experience in both seasons and achieve tourism sustainability.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".