This study investigates the role of Chat-GPT with augmented reality applications in enhancing tourism experiences in Thailand, focusing on behavioral intentions and innovation adoption to reduce stress in the tourism industry. The research addresses two key objectives: identifying factors driving consumers' behavioral intentions to adopt AR apps and evaluating the robustness of a modified innovation framework for analyzing these intentions. A conceptual model integrating innovativeness, attitudes, perceived enjoyment, and revisit intentions was developed and tested using Structural Equation Modeling with data from 430 Thai tourists who have one to three years of mobile application experience. The findings highlight that service and technology innovation significantly influence perceived enjoyment and attitude, which in turn mediate the impact on behavioral intention to adopt augmented reality applications. At a significance level of p < 0.001, perceived enjoyment and attitude were identified as critical determinants of BI, underscoring the importance of intrinsic user experiences. Tourists are more likely to adopt augmented reality technologies based on personal perceptions and enjoyment rather than external recommendations. This research provides actionable insights for stakeholders in the tourism technology ecosystem, including technology providers, marketers, and policymakers. By emphasizing the interplay of social, emotional, and hedonic factors in shaping user attitudes, the study introduces a robust framework for advancing augmented reality applications in tourism. The findings underscore the importance of user-centric design to drive technology adoption and offer strategic guidance for developers and entrepreneurs aiming to enhance tourism experiences through innovative augmented reality solutions.

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.

Pattaya City is a well-known tourist destination in Thailand, famous for its beautiful beachfront, lively nightlife, and stunning natural scenery. Since 2019, the Eastern Special Development Zone Act, the so-called EEC (Eastern Economic Corridor), has positioned the city as a focal point for Meetings, Incentives, Conferences, and Exhibitions (MICE), boosting its tourism-driven economy. Infrastructure improvements in the region have accelerated urban development over the past decade. However, it is uncertain whether this growth primarily comes from development within existing areas or the expansion of urban boundaries and what direction future growth may take. To investigate this, research using the Cellular Automata-Markov model has been conducted to analyze land use changes and urban growth patterns in Pattaya, using land use data from the Department of Land for 2013 and 2017. The findings suggest an upcoming city expansion along the motorway, indicating that infrastructure improvements could drive rapid urbanization in coastal areas. This urban expansion emphasizes the need for urban management and strategic land use planning in coastal cities.

Thailand and the EU started negotiating a free trade agreement (FTA) in 2005, but negotiations were subsequently suspended in 2014 after the country’s military coup. The significance of these negotiations are important because of the mutual benefit of achieving higher levels of trade and investment between the world’s largest single market and the second largest ASEAN economy. The Specific Factors (SF) model of production and trade is applied to identify potential winner and loser industries and factors of production in Thailand. The model identifies short-run loses for some labor inputs, return to capital, and output in agriculture and services. In the manufacturing and energy sectors, higher output will benefit some labor inputs and capital owners. Understanding the short-run impact of an FTA could allow policymakers in Thailand to reinforce the institutional infrastructure such as implementing trade adjustment assistance programs (TAA), to help re-train workers who may become unemployed due to free trade.

The purpose of the article is to present the current situation in the rail freight transport in Thailand and the direction of changes in this area. Firstly, Thailand statistics in volume of freight transport by rail and modal share of freight transport have been presented. Afterwards, problems and obstacles in railway operational practices and in using rail transport services have been identified to improve railway system in Thailand and the outcome was assessed in terms of railway capacity and utilization. The findings were used to outline the direction of changes in rail freight transport. The results show that the rail transport capacity in double-track would increase by 48% (at present by 15.5% and as plan by 30%) and the ratio by rail transport to total freight transport would increase from at present by 1.87% to 10% in 2037.