This research aims to solve the research problems regarding the most important value of an object in the form of the wedangan phenomenon. This research objectives to expose the superiority of the communities’ food consumption tradition in the form of wedangan. This research belongs to a qualitative study and uses ethnomethodology as an initial approach. It is because the initial data findings are in the form of an indexical conversation that explicitly refers to the concept of wedangan. The concept refers to wedangan in real life, which is in the form of eating and drinking activities while chatting. The research findings are: 1) the most profound structure of wedangan’s tradition is food provision and food eating; 2) wedangan accommodates three forms (food stall, street food, and restaurant); 3) wedangan also accommodates three food values (delightful, useful, and meritorious); and 4) there is an egalitarian consumption pattern in wedangan, people regardless their social class visiting the same place, eat the same food, being simple and be ordinary (or usually we call it as food marriage). Wedangan is a social activity with advantages from a social, economic, and political perspective. Therefore, this phenomenon requires more serious attention from the government.

Global trade is based on coordinated factors, that means labor and products are moved from their point of origin to the point of use. Strategies have a significant impact on global trade because they enable the effective development of goods across international borders. The decision making is an important task for the development of Logistics Supply Chain (LSC) infrastructure and process. Decisions on supplier selection, production schedule, transportation routes, inventory levels, pricing strategies, and other issues need to be made. These decisions may have a big influence on customer service, profitability, operational efficiency, and overall competitiveness. The Artificial Intelligence (AI) approach of Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy-Promethee-2) is used to assess the priority selection of the factors associated with the LSC and evaluate the importance in global trade. The role of AI is very useful compare to statistical analysis in terms of decision making. The computational analysis placed promotion of exports as the most important priority out of five selected attributes in LSC, with infrastructure development. The result suggests that LSC depends heavily on export promotion as the most significant attribute. Infrastructural development also appeared another factor influencing LSC. The foreign investment was ranked the lowest. The evaluated results are useful for the policy makers, supply chain managers and the logistics professionals associated with the supply chain management.

The objectives of this qualitative research are to study problems and factors promoting success in the career path of government officials in the Ministry of Higher Education, Science, Research, and Innovation (MHESI) in Thailand. The study also finds out career path model to opinions between executives and government officials. This qualitative employed in-depth interview and focus group discussion with executives, academics, and civil servants. It found that the problem was the planning and management of career path due to lacking of standard pattern. Also, it found that the model of career path provides practitioners with career advancement opportunities and job titles from the very beginning to the very top where they can advance and can plan their career progression. The model also provides an opportunity to explore officers’ competencies, aptitudes, and interests that are appropriate for any type of work in the organization and able to prepare them to perform the job, which will affect the success of civil servants’ work and human resource management to create career path and develop oneself to be able to compete for academic and professional excellence, as well as prepare the government officers for appropriate positions in the future.

This research explores the role of digital economy in driving agricultural development in the BIMSTEC region, which includes Thailand, Myanmar, Sri Lanka, Nepal, India, Bangladesh and Bhutan (with Bhutan excluded due to data limitations) with a particular focus on mobile technologies, computing capacity and internet connectivity which were the most readily available data points for BIMSTEC. Using a combination of document analysis, and panel data analysis with the data covering 10 years (2012–2021), the study examines the interplay of key digital technologies with agricultural growth while controlling for factors including water usage, fertilizer consumption, and land temperature and agricultural land area. The analysis incorporates additional variables such as infrastructure development, credit to agriculture, investment in agricultural research, and education level. The findings reveal a strong positive correlation between mobile technology, Internet and computing capacity in BIMSTEC. This study underscores that digital tools are pivotal in enhancing agricultural productivity, yet their impact is significantly combined with investment in infrastructure and education. This study suggests that digital solutions, when strategically integrated with broader socio-economic factors can effectively challenges in developing countries, particularly in rural and underserved regions. This research contributes to the growing body of literature on digital economy in agriculture, highlighting how digital technologies can foster agricultural productivity in developing countries.

Urbanization process affects global socio-economic development. Originally tied to modernization and industrialization, current urbanization policy is focused on productivity, economic activities, and environmental sustainability. This study examines impact of urbanization in various regions of Kazakhstan, focusing on environmental, social, labor, industrial, and economic indicators. The study aims to assess how different indicators influence urbanization trends in Kazakhstan, particularly regarding environmental emissions and pollution. It delves into regional development patterns and identifies key contributing factors. The research methodology is based on classical economic theories of urbanization and modern interpretations emphasizing sustainability and socio-economic impacts and includes two stages. Shannon entropy measures diversity and uncertainty in urbanization indicators, while cluster analysis identifies regional patterns. Data from 2010 to 2022 for 17 regions forms the basis of analysis. Regions are categorized into groups based on urbanization levels leaders, challenged, stable, and outliers. This classification reveals disparities in urban development and its impacts. Findings stress the importance of integrating environmental and social considerations into urban planning and policies. Targeted interventions based on regional characteristics and urbanization levels are recommended to enhance sustainability and socio-economic outcomes. Tailored urban policies accommodating specific regional needs are crucial. Effective management and policy-making demand a nuanced understanding of these impacts, emphasizing region-specific strategies over a uniform approach.