Why Don’t They Travel? The Role of Constraints and Motivation for Non-Participation in Tourism
Bibliographic record
Abstract
Between one-quarter and one-third of the population in developed economies do not travel, but our understanding of this group is rather limited. Studies looking at constraints and motivation often treat non-travelers as an homogeneous group compared to a spectrum of traveler types. Non-travel is also often implied as being a deficit rather than a voluntary decision. A mixed-method approach is applied in this study to explicitly explore the variety within non-travelers in general and voluntary non-travelers in particular. Qualitative interviews with non-travelers were used to gain a more in-depth understanding of the underlying reasons for non-travel. Non-travelers were then segmented based on constraints and motivation in a large-scale survey representative for Germany. The resulting non-traveler typology clearly shows distinct non-travelers types. By adding a pro non-travel preference instead of using deficit-oriented arguments, voluntary types of non-travelers were identified. This implies that non-travel is not necessarily something people want to overcome.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".