Exploring the mechanisms of tourist well-being: An application of cognitive appraisal theory and self-determination theory
Bibliographic record
Abstract
Previous research has established that tourism can bring happiness and well-being to tourists. However, the specific mechanisms by which travel leads to well-being are not yet fully understood. To address this knowledge gap, this study integrated Cognitive Appraisal Theory (CAT) and Self-Determination Theory (SDT) to predict tourist well-being. The study employed a quantitative approach and used a sample population of tourists who had traveled abroad for at least 3 days in the past 12 months. Data was collected from an online panel owned by the Centre of Tourism Research in Prince Edward Island (PEI), resulting in a final sample size of 396. Two-step analysis, including Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM), was performed on the data. The findings support the predictions of CAT and SDT and establish connections between the theories. Specifically, the study found that SDT's psychological needs of autonomy and relatedness mediate the relationship between the positive emotions elicited by the trip and tourists’ psychological well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 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 | 0.000 |
| 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".