Demographic factors affecting Chinese tourists traveling to Thailand in the post-Covid-19 era

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Chinese tourists contribute significantly to the development of the tourism industry in Thailand. However, only some studies have systematically discussed the role of demographic factors in developing Thai international tourism. The study aims to research the behavior of Chinese citizens traveling to Thailand in the post-Covid-19 era based on combining the behavioral dynamics, the push-pull theories and demographics. 432 Chinese travelers who have visited Thailand participated in the survey. The scale included four parts: international tourism development in Thailand (A1-A8); pushers (B1-B9); pullers (C1-C8); demographic variables: gender, age, occupation, income, education level, marital status, and location. The study used exploratory factor analysis, confirmatory factor analysis, correlation, and variance analyses with SPSS 26.0. Therefore, exploratory factor analysis identified for this study three factors: F1 (5 items), F2 (3 items), and F3 (4 items). The correlation between F1 and F2 is 0.8, between F1 and F2 is 0.87, between F2 and F3 is 0.79. The findings of the analysis of demographic variables indicate that: gender does not affect tourists’ perceptions and changes; age has a significant impact on the three constructs; monthly income should be considered in the development of inbound tourism strategies; undergraduate and postgraduate visitors showed higher scores for research constructs; there is no need to consider the marital status of tourists. The study suggests that the Thai tourism department pay attention to the push and pull factors that motivate Chinese citizens to choose Thailand to expand international tourism.

Acknowledgment
The authors would like to acknowledge supervisors, family members, and colleagues for their support and guidance.

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    • Figure 1. Confirmatory factors analysis
    • Table 1. Demographics of the sample
    • Table 2. Reliability statistics
    • Table 3. KMO and Bartlett’s test
    • Table 4. Exploratory factor analysis
    • Table 5. Validation of convergence: AVE and CR
    • Table 6. Model fitting overall appropriate index
    • Table 7. Gender difference analysis
    • Table 8. Age difference analysis
    • Table 9. Location difference analysis
    • Table 10. Occupation difference analysis
    • Table 11. Income difference analysis
    • Table 12. Education level difference analysis
    • Table 13. Marital status difference analysis
    • Conceptualization
      Haiying Fu, Chonlavit Sutunyarak
    • Data curation
      Haiying Fu
    • Formal Analysis
      Haiying Fu, Chonlavit Sutunyarak
    • Investigation
      Haiying Fu, Chonlavit Sutunyarak
    • Methodology
      Haiying Fu, Chonlavit Sutunyarak
    • Project administration
      Haiying Fu, Chonlavit Sutunyarak
    • Validation
      Haiying Fu
    • Visualization
      Haiying Fu
    • Writing – original draft
      Haiying Fu
    • Writing – review & editing
      Haiying Fu, Chonlavit Sutunyarak
    • Supervision
      Chonlavit Sutunyarak