COVID-19 has presented considerable challenges to fiscal budget allocations in developing countries, significantly affecting decisions regarding number of investments in the transport sector where precise resource allocation is required. Elucidating the long-term relationship between public transport investment and economic growth might enable policymaker to effectively make a decision in regard to those budget allocation. Our paper then utilizes Thailand as a case study to analyze the effects on economic growth in a developing country context. The study employs Cointegration and Vector Error Correction Model (VECM) techniques to account for long-term correlations among explanatory variables during 1991–2019. The statistical findings reveal a significantly positive correlation between transport investment and economic growth by indicating an increase of 0.937 in economic growth for every one-percent increment in transport investment (S.D. = 0.024, p < 0.05). This emphasizes the potential of expanding the transport investment to recover Thailand’s economy. Furthermore, in terms of short-term adjustments, our results indicate that transport investment can significantly mitigate the negative impact of external shocks by 0.98 percent (p < 0.05). These findings assist policymakers in better managing national budget allocations in the post-Covid-19 period, allowing them to estimate the duration of crowding-out effects induced by shocks more effectively.

This research examines intangible assets or intellectual capital (IC) performance of tourism-related industries in an underexplored area which is a tourism intensively-dependent country. In this study, VAIC which is a monetary valuation method and also the most widely applied measurement method, is utilized as the performance measurement method for quantifying IC performance to monetary values. Moreover, to better understand performance, the standard efficiency levels are further applied for classifying the performance levels of tourism industries. The sample sizes of study are 20 companies operating in the tourism-related industries in the world top travel destination or Thailand, and the companies’ data are collected from 2012 to 2021. Therefore, finally, there are 187 firm-year observations. The utilization of VAIC could assess IC performance of tourism firms and industries, and the standard efficiency levels further support the uniform interpretation of IC efficiency levels. The obtained results show the strong performance of both human and structural capital of the focused tourism dependent country especially in the logistics industry that directly supports and connects to the tourism attractions. Moreover, the finding also highlights the significance of human capital which plays as a major contributor for overall IC performance in this tourism dependent economy. This study contributes the new exploration of IC in the high impact industries and also specifically in the top significant tourism country. Moreover, the application of VAIC also confirms a practical application for management. The limited number of studied countries is a limitation of study. However, these new obtained data and information could be further applied for making comparisons or in-depth or statistical analysis in the future works.

This study investigates the factors influencing student satisfaction at higher education institutions in Pathum Thani Province, Thailand. The research uses structural equation modeling (SEM) to analyze the connections among College Reputation, Student Expectation, Perception Value, and Student Satisfaction based on a sample of 660 students. The results indicate that the student population is diverse, with most students enrolled in the Faculty of Business Administration in their first year. The Pearson’s correlation matrix and structural equation modeling (SEM) findings indicate significant positive correlations between the dimensions, emphasizing the crucial influence of College Reputation on both Student Expectation and Student Satisfaction. The goodness-of-fit indices validate the model’s strength, indicating a significant correspondence between the theoretical components and the observed data. This study enhances the comprehension of how student satisfaction changes in Thai higher education and offers practical suggestions for institutional policies to improve student’s educational experiences and achievements. Higher education institutions may create a more fulfilling and effective learning environment by prioritizing reputation improvement, ensuring student expectations match reality, and providing perceived value to improve education quality and equality for Thailand.

This study explores the determinants of political participation among Thai youth, focusing on the roles of political interest, knowledge, and efficacy. Employing stratified random sampling, data were collected from 191 university students in Bangkok. Structural Equation Modeling (SEM) via Smart PLS was utilized to test hypotheses regarding the direct and mediating effects of political interest and knowledge on participation, highlighting the mediating role of political efficacy. The findings indicate that political efficacy significantly enhances participation, while political interest boosts knowledge significantly but does not directly influence efficacy. Furthermore, political knowledge positively affects efficacy but not participation directly. Notably, the indirect effects of political interest on participation through efficacy alone are insignificant, but the pathways from interest to participation through both knowledge and efficacy, and from knowledge to participation through efficacy, are significant. These results elucidate the complex interactions between political interest, knowledge, and efficacy in shaping the political engagement of Thai youth.

The Mass Rapid Transit (MRT) Purple Line project is part of the Thai government’s energy- and transportation-related greenhouse gas reduction plan. The number of passengers estimated during the feasibility study period was used to calculate the greenhouse gas reduction effect of project implementation. Most of the estimated numbers exceed the actual number of passengers, resulting in errors in estimating greenhouse gas emissions. This study employed a direct demand ridership model (DDRM) to accurately predict MRT Purple Line ridership. The variables affecting the number of passengers were the population in the vicinity of stations, offices, and shopping malls, the number of bus lines that serve the area, and the length of the road. The DDRM accurately predicted the number of passengers within 10% of the observed change and, therefore, the project can help reduce greenhouse gas emissions by 1289 tCO₂ in 2023 and 2059 tCO₂ in 2030.