Who Walks the Walk and Talks the Talk? Understanding What Influences Sustainability Behaviour in Business and Leisure Travellers
Bibliographic record
Abstract
While there is considerable research into what drives tourists to travel sustainably, little has been done to examine business travellers and how they differ from leisure travellers. The purpose of this paper is to fill this gap by looking to understand these differences and what drives them. Specifically, this paper looked to understand the influence that demographics, travel characteristics, and everyday behaviour (pro-ecological actions, frugal consumption patterns, and altruistic behaviours) have on sustainable travel behaviour, and if these influences held true for both business and leisure travellers. To facilitate this investigation, a quantitative study of 869 Canadian travellers in March of 2020 was undertaken. This research found that demographics and travel characteristics to contribute to the prediction of sustainable travel behaviour, but the greatest prediction power came from everyday behaviour. Beyond confirming that everyday behaviour is still the greatest indicator of sustainable travel domestically or abroad, this research found that this influence does not change whether the travel is for business or leisure.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".