Effects of Tourism Experience for Job Involvement and Well-Being
Bibliographic record
Abstract
This study uses the structural equation model as the analysis tool, and aims to explore the effects of tourism experience on job involvement and well-being. The subjects are full-time workers who have travelled in the last 12 months. This investigation is based on purposive sampling and e-questionnaires, uses analytical tools SPSS 18.0 and AMOS 19.0, and 360 valid questionnaires are retrieved. According to the research findings: (1) tourism experience positively influences job involvement; (2) tourism experience does not positively influence well-being; and (3) job involvement positively influences well-being. Based on the above, this study suggests that managers plan appropriate trips according to employees' demands. Experiential activities should be appealing and trigger internal affective connections through external experience, in order to reinforce job involvement and well-being in life. The research results also reveal that, of the five experiences, i.e., sensual experience, emotional experience, thinking experience, action experience, and related experience, the regression coefficient of emotional experience is the highest, which shows why story marketing has taken an important position among marketing strategies. The different types of tourism experience include recreational sightseeing, cultural sightseeing, entertaining sightseeing, and sports sightseeing where recreational sightseeing accounts for 58.1%. Under the existing system, there may have been items that did not apply to the respondents, which would result in deviations or errors in the questionnaires; in the case of any special or major changes in the external environment
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 | 0.000 | 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".