Developing a typology of older visitors to heritage attractions
Bibliographic record
Abstract
This article examines older visitors to UK heritage attractions and presents a typology to progress our understanding of this under-researched market. Although there is plenty of research on older tourists, scholars regard this market as homogeneous and neglect to investigate older day visitors to local attractions. We apply push-pull motivation and typology theories and employ a quantitative research design, which involves a survey of older heritage visitors. Confirmatory factor analysis produced five push motivation and five pull motivation dimensions, three visitation constraint dimensions, two constraint negotiation dimensions and two visitation benefit dimensions. A cluster analysis identified three distinct segments: ‘Heritage Enthusiasts’, ‘Motivated but Unfulfilled’ and ‘Somewhat Interested but Satisfied’ and developed a typology based on distinctive characteristics. Significant predictors of older visitors’ satisfaction, recommendation and repeat visitation by cluster were also identified. The findings will facilitate more effective marketing of heritage attractions based on these segments.
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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 | 0.001 |
| 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 | 0.001 |
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".