Factors Influencing Chinese Tourist to Select Bangkok, Thailand as A Travel Destination
Bibliographic record
Abstract
This study aims to investigate factors influencing the Bangkok-focused tourist destination selection in China. In addition, Kuala Lumpur and Tokyo, Japan were selected for comparison by assessing four destination attributes including culture, transportation, architecture, and food. In this study, we used a sample of 400 Chinese tourists who have traveled to Bangkok. The main research issue is to reveal the first- and second-order potential factors generating significant influences on Chinese tourists’ choice of Bangkok as their destination. The aim of this study is to explore the structural relationships among the mentioned first-order and second-order latent variables, and their impact on the choice of tourist destinations in China. Due to the competitive nature of Chinese tourist destinations, we believe that there might be some potential factors that significantly affected their choice decision, therefore we applied the second-order Structural Equation Models (SEM) to capture these potentially unobservable factors. The result showed that our proposed model appeared to fit well: the RMSEA was 0.03 (<0.06) and values of GFI, AGFI, NFI, TLI, and CFI were greater than 0.9 (most of them were even larger than 0.95). More importantly, Food (F), Emotional Factor (EF) representing food and cultural indulgence, and Physical Factor (PF) representing Architecture and Transportation facility of the destination showed significant impacts on tourist destination choice as their p-values were less than 0.05. Hence, Thai food and anything that could maximize the emotional and functional values of Chinese tourists would make travel choices to become their travel destination. At the same time, it was aimed to provide some valuable suggestions for tourist cities currently under threat from COVID-19, to recover or better in the coming years, providing some evidence for future researchers to further explore this field.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".