This study seeks to explore the information value of free cash flow (FCF) on corporate sustainability and investigate the moderating effects of board gender diversity and firm size on the association between FCF and corporate sustainability of Thai listed companies. The dataset consists of companies listed on the Stock Exchange of Thailand (SET) in 2022. Multivariate regression analysis is executed in this study. Subsequently, PROCESS macro served to evaluate the proposed hypotheses. This study found that FCF has a significant positive relationship with corporate sustainability. As well, board gender diversity and firm size both moderate the relationship between FCF and corporate sustainability, such that the positive effect of FCF on corporate sustainability is stronger when the proportion of female boards diminishes, while firm size is smaller. However, when firms have a larger proportion of females on the boards of directors for all levels of firm size, free cash flow indicates that there is no statistically significant effect on corporate sustainability. This study contributes to FCF and sustainability literature by understanding the extent of corporate sustainability.

In Urban development, diversity respect is needed to prioritize and balance the urban development design for sustainable eco-city development. As a result, this research aimed to investigate the causal factor pathways of social network factors influencing sustainable eco-city development in the northeastern region of Thailand through a quantitative research approach. With the aim to survey insightful information, the analysis unit was conducted at the individual level with three hundred and eighty-three (383) samplings in Khon Kaen and Udon Thani provinces, including univariate analysis and multivariate analysis, using path analysis and multiple linear regression. The study results indicated that two pathways of social network factors influencing sustainable eco-city development were indirect influence factors. The indirect influence factor consists of information exchange, benefits exchange in the network, and members’ role in the social network. Additionally, the study revealed that the pathway has influences through social network types and the economic and social dimensions of sustainable cities (R² = 0.330). Therefore, this study concluded that sustainable eco-city development should be implemented through community networks and economic and social network development for environmental development through social network types.

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

This research explores the implementation of streamlined licensing frameworks and consolidated procedures for promoting renewable energy generation worldwide. An in-depth analysis of the challenges faced by renewable energy developers and the corresponding solutions was identified through a series of industry interviews. The study aims to shed light on the key barriers encountered during project development and implementation, as well as the strategies employed to overcome these obstacles. By conducting interviews with professionals from the renewable energy sector, the research uncovers a range of common challenges, including complex permitting processes, regulatory uncertainties, grid integration issues, and financial barriers. These challenges often lead to project delays, increased costs, and limited investment opportunities, thereby hindering the growth of renewable energy generation. However, the interviews also reveal various solutions and best practices employed by industry stakeholders to address these challenges effectively. These solutions encompass the implementation of streamlined licensing procedures, such as single licenses and one-stop services, to simplify and expedite the permitting process. Additionally, the development of clear and stable regulatory frameworks, collaboration between public and private entities, and improved grid infrastructure were identified as key strategies to overcome regulatory and grid integration challenges. The research findings highlight the importance of collaborative efforts between policymakers, industry players, and other relevant stakeholders to create an enabling environment for renewable energy development. By incorporating the identified solutions and best practices, policymakers can streamline regulatory processes, foster public-private partnerships, and enhance grid infrastructure, thus catalyzing the growth of renewable energy projects.

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 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.