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.

Introduction: Chatbots are increasingly utilized in education, offering real-time, personalized communication. While research has explored technical aspects of chatbots, user experience remains under-investigated. This study examines a model for evaluating user experience and satisfaction with chatbots in higher education. Methodology: A four-factor model (information quality, system quality, chatbot experience, user satisfaction) was proposed based on prior research. An alternative two-factor model emerged through exploratory factor analysis, focusing on “Chatbot Response Quality” and “User Experience and Satisfaction with the Chatbot.” Surveys were distributed to students and faculty at a university in Ecuador to collect data. Confirmatory factor analysis validated both models. Results: The two-factor model explained a significantly greater proportion of the data’s variance (55.2%) compared to the four-factor model (46.4%). Conclusion: This study suggests that a simpler model focusing on chatbot response quality and user experience is more effective for evaluating chatbots in education. Future research can explore methods to optimize these factors and improve the learning experience for students.

The objective is to determine the impact of economic growth on the externalities of infrastructure investments for the Peruvian case for the periods from 2000 to 2022. The methodologies used are descriptive, explanatory and correlational, analyzing qualitative and mainly quantitative methods. Econometric software was used, and correlations of variables were created for each proposed hypothesis. The estimated model shows that all the independent variables have a significant t-statistic greater than 2 and a probability of less than 5%, which indicates that they are significant and explains the model. The R² is 98.02% which indicates that there is a high level of explanation by the independent variables to the LOG(RGDP). The results of the estimated models demonstrate the existence of a positive and significant relationship of investments in infrastructure and externalities on the growth of the non-deterministic component of real GDP, therefore, in a practical way, private and public investment has a positive effect on the non-deterministic growth of real GDP.

Evaluating tourist destinations is extremely important as it is the basis for helping local authorities and the leadership of tourist destinations implement reasonable solutions to strengthen the state management of tourism, encourage investment and upgrade service quality at destinations, better exploit the tourist market, position the tourist destination brand in the international tourism market, increase the length of stay, and increase tourist spending when coming to the tourist destination. The current state of investment and development of tourist destinations means that tourist areas across the country need to be evaluated and classified to have a basis for encouraging investment and strengthening effective management, upgrading service quality at destinations, and gradually positioning the Vietnamese tourism destination brand in the international tourism market. This study evaluates the Ba Na tourist area (Da Nang city, Vietnam) based on the “Set of criteria for evaluating tourist destinations” issued by the Ministry of Culture, Sports and Tourism of Vietnam (2016). issued under Decision No. 4640/QĐ-BVHTTDL on 28 December 2016. Evaluation results show that criteria for tourism resources, landscape, facilities, participation of local communities, and the management of the tourist area are evaluated very well. On the contrary, services for entertainment, shopping, entertainment, and prices of services in the tourist area are limited problems in the Ba Na tourist area.

This study investigates the buying styles of young consumers, especially the millennials—Gen Y, and Gen Z whose idiosyncrasies and consumption peculiarities are quite different from the older generations. Besides Sproles and Kendall’s eight (8) consumer-style inventory dimensions, this study presents new dimensions and develops six constructs that define young consumers’ decision-style inventory in a developing market. The study population consisted of all younger consumers—Gen Y, and Gen Z in Lagos State, Nigeria. One hundred and twenty-five (125) respondents were selected randomly across all 20 Local Governments in Lagos State, Nigeria. Factor analyses through varimax rotation, latent root criterion (eigenvalue = 1), screen plot test and the percentage of variance were conducted to determine the significant factors to retain among the variables. The findings clearly showed that newly developed CSI constructs in this study (sexiness, trendiness, global branding, smartness, socialisation and entertainment) were strong and significant among young consumers’ decision-making styles. The six (6) constructs developed showed that the younger consumers’ consumption styles are evolving, becoming sophisticated and relatively dynamic, hence the reliance on Sproles and Kendall’s dimensions to measure the younger consumers’ consumption decision styles will be inadequate in business/behaviour strategy development. The dimensions of entertainment, sexy, social, trendy, smartness and global branding variables are mostly underpinned and dominate considerations in purchase decision styles and behaviours among young consumers.

Purpose: This research aims to examine the influence of intellectual capital disclosure and the geographical location of universities on the sustainability of higher education institutions in Southeast Asia. Design/methodology/approach: This research is quantitative and uses secondary data obtained through the annual reports of universities that have the Universitas Indonesia Green Metric Rank. This research uses two stages of data analysis techniques, namely the content analysis stage to determine the number of Intellectual Capital disclosures and the hypothesis testing stage. The analysis tool uses the SPSS version 23 application. The population of this research includes all universities in Southeast Asia that are included in the UI Greenmetric World University Rankings. The sampling technique used was purposive sampling technique, which resulted in 86 analysis units of higher education institutions in Southeast Asia. Findings: The research results prove that the geographical location of universities has a negative and significant influence on Universitas Indonesia Green Metric’s performance in Southeast Asia and human capital has a positive influence on UIGM’s performance in Southeast Asia. However, the structural capital and relational capital components do not affect the UIGM performance of universities in Southeast Asia. Originality/value: The originality of the research is the use of higher education sustainability variables with UIGM proxies and modified IC indicators for universities and geographical areas that have not been widely used to see whether there are fundamental differences in the disclosure of intellectual capital for higher education institutions in Southeast Asia.