Modification and validation of the travel safety attitude scale (TSAS) in international tourism: a reflective-formative approach
Bibliographic record
Abstract
Purpose This study modified, revised and validated a travel safety attitude scale (TSAS) using data collected from Canadian residents with out-of-country travel experiences. Design/methodology/approach The authors proposed a higher component model (HCM) of TSAS, using a reflective-formative measurement model. In consultation with eight experts, a set of purified TSAS items was revised by checking wording and content. A questionnaire was administered to 531 participants using Amazon Mechanical Turk. The scale was validated with the partial least squares method of structural equation modelling (PLS-SEM), and the analysis was performed using SmartPLS 3.0. Findings The final results suggested a five-factor solution with 27 items, with a satisfactory level of reliability and validity at the first-order (reflective) and second-order (formative) constructs. The predictive validity result showed that TSAS is negatively related to tourist risk-taking intention. Research limitations/implications TSAS advanced research on travel safety attitudes and demonstrated the feasibility of using PLS-SEM in examining the Type II model. Future studies can focus on replicating the study in other countries, adding more variables for predictive validity tests and examining the interrelationship with affective attitudes. Practical implications The authors suggested a more proactive approach to assess tourist safety attitudes based on travel safety information (TSI), health concern (HC), vulnerability to crime (VTC), personal safety (PES) and police safety (PS), listed in descending order of importance. Originality/value The study results provide directions for destination marketing organizations to allocate resources to maintain a positive travel safety attitude from potential and current tourists.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".