Predicting World Heritage site visitation intentions of North American park visitors
Bibliographic record
Abstract
Purpose World Heritage sites (WHS) can play an important role in promoting visitation to emerging and remote destinations. Guided by the theory of planned behaviour (TPB), this study aims to investigate factors that predict intentions to visit WHS. Design/methodology/approach Survey questionnaires were used to collect data from visitors (n = 519) to four Western North American WHS. Partial least squares structural equation modelling (PLS-SEM) was used to identify three reflective models (attitude toward visiting World Heritage, perceived behavioural control and intention to visit WHS in the future), three formative models (attitude toward World Heritage designation, social influence (subjective norms) to visit World Heritage and World Heritage tourism brand equity) and a structural model. Findings World Heritage tourism brand equity and social influence were strong positive predictors of intentions to visit WHS in the future. Attitudes towards World Heritage designation, followed by World Heritage travel attitudes and perceived behavioural control, were progressively weaker, yet positive predictors. However, the latter two concepts’ impact was negligible. Originality/value This study addresses four deficiencies in tourism studies: TPB studies have failed to find consistent predictors of intentions to visit destinations; very few studies have attempted to verify the factors that predict visitation to WHS, despite the opportunities and costs that can arise from WHS-related tourism; few studies of tourists’ perceptions of World Heritage and related WHS travel intentions have been conducted in North America; and PLS-SEM was used to perform statistical methods not commonly used in tourism studies including formative models, importance-performance mapping and confirmatory tetrad analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".