Causal Relationship Model Between Factors Affecting Tourism Management Development in the Lower Northeastern Provinces Group 1 of Thailand DOI:

Abstract

          This research aimed: 1) to analyze the key causal factors influencing tourism management development in the Lower Northeast 1 provincial cluster of Thailand; and 2) to develop a causal relationship model among factors affecting tourism management development in the Lower Northeast 1 provincial cluster of Thailand. This study employed a mixed methods research design, comprising two main phases. In Phase 1, both quantitative and qualitative research methods were utilized. For the quantitative research, the sample size was determined based on the guidelines proposed by Hair et al. (2014), which suggest an appropriate sample size of 200 to 300 cases or a ratio of at least 10 to 20 cases per observed variable. Given that this study involved 27 observed variables, a minimum sample size of 540 cases (20 times 27) was deemed suitable and sufficient for Structural Equation Modeling (SEM) analysis. Data were collected using a 5-point Likert scale questionnaire and analyzed using the Analysis of Moment Structures (AMOS) program to assess construct validity, reliability, linearity, and through multiple regression analysis. The qualitative research in Phase 1 involved selecting key informants, who were experts relevant to tourism management development in the Lower Northeast 1 provincial cluster of Thailand. A purposive sampling method was used to recruit 44 participants. Data were gathered through focus group discussions, document analysis of academic materials, and structured interviews. Content analysis was employed to describe details, interpret meanings, and explain the relationships within the collected data. The data obtained were then synthesized to construct a tourism management development model for the Lower Northeast 1 provincial cluster of Thailand. In Phase 2, the developed model underwent a validation process, wherein its appropriateness was examined and confirmed by 32 experienced experts. This evaluation aimed to assess the accuracy and reliability of the proposed model, with the ultimate goal of formulating clear policy recommendations that would contribute to the effective development of tourism management in the Lower Northeast 1 provincial cluster of Thailand.

          The research findings revealed that: 1) The analysis of direct, indirect, and total effects elucidated the complex mechanisms of tourism development in the region. Notably, the policy implementation dimension exhibited the most prominent role with the highest direct effect (β=0.84**), signifying that translating policy into tangible action plans possessed a driving force three times greater than other factors when compared to the direct effect of the creative economy dimension (β=0.22**). This suggests that the optimal impact of leveraging economic creativity is achieved when integrated with a clear policy framework. 2) The causal relationship model among factors influencing tourism management development in the Lower Northeast 1 provincial cluster of Thailand, encompassing the dimensions of tourism logistics management, intellectual capital, creative economy, and policy implementation, which influenced tourism development as hypothesized and aligned with the empirical data following model modification, yielded a Chi-square to degrees of freedom ratio (χ²/df) of 1.154, which is less than 2. Given the sensitivity of the Chi-square statistic to sample size, it is suggested that other indices be considered. The Goodness-of-Fit Index (GFI) was 0.973, the Root Mean Square Error of Approximation (RMSEA) was 0.017, the Comparative Fit Index (CFI) was 0.999, and the Root Mean Square Residual (RMR) was 0.005, all meeting the acceptable criteria. In conclusion, the structural equation model of the dimensions of tourism logistics management, intellectual capital, creative economy, and policy implementation influencing tourism development, following model modification, demonstrated an acceptable fit with the empirical data.