Factors effecting destination and event loyalty: examining the sustainability of a recurrent small-scale running event at Banff National Park
Bibliographic record
Abstract
An important form of economic sustainability for tourism businesses is customer loyalty. Using a sample of 387 active sport tourists, factors that influence destination and event tourism loyalty are reported on in this paper. Thirty-six per cent of destination loyalty’s variance was explained – operationalized as intentions of active sport tourists to revisit and recommend Banff National Park (NP). Thirty-one per cent of event loyalty’s variance was explained – operationalized as participation in future offerings of an annual small-scale running race, Melissa’s Road Race, located in the park. Destination loyalty was directly and positively predicted by park attachment and indirectly influenced by event attachment, followed by nature-related travel motives, frequency of visits to the park and history of engagement in the race. Event loyalty was directly and positively predicted by event attachment and racers’ views regarding the appropriateness of Banff NP as a race context and indirectly by history of race participation. Running travel motives, perceived value of park entry and event fees failed to predict loyalty intentions. Two models were used to explore the ‘correct’ conceptualization of relationships between event attachment and park attachment. The model that depicted event attachment as an antecedent to park attachment demonstrated better fit with the data, and thus suggests support for the proposition that attachments which develop for special events may in turn support the development of destination attachment.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".