Addressing the Attitude Behaviour Perception Gap—Multimethod Sustainable Tourist Behaviour Evaluation
Bibliographic record
Abstract
ABSTRACT Quantitative and perceptual studies have been used to define and model sustainable tourist behaviour in past years, but few studies have undertaken qualitative research of actual behaviour to delve deeper into understanding the different classifications of such behaviour. This research employed a three‐phase design, comprising a pretrip survey, in‐person semistructured interviews and participant observation of tourist behaviour during an adventure cruise expedition. The aim was to test the accuracy of the Holmes–Frochot model of everyday behaviour and assess whether sustainable tourist behaviours were actually demonstrated rather than just intended. Survey data were analysed using cluster and regression analysis, while interview transcripts and field notes were examined through deductive thematic analysis. Findings suggest that the Holmes–Dodds–Frochot model of everyday behaviour can help be a good predictor of sustainable travel behaviour, and that the constructs of frugality, pro‐ecological behaviour and altruism emerge differently as distinct tourist influences. By combining these quantitative and qualitative findings, this research has been able to derive meta‐inferences examining how perceived intentions manifest to actual behaviours. Furthermore, these meta‐inferences highlight nuances in how travellers understand and practice sustainability, with the qualitative findings delving deeper into the motivations and constructs that may help destinations understand ‘sustainable’ travellers.
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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 | 0.135 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".