Lifelong learning (LLL) is progressively recognized as a crucial component of personal and professional development, particularly for adult students. As a heavily populated developing country, China requires profound national education reform to support its economic development and maintain its competitive advantage on the global economic stage. The governmental policy endorses the execution of diverse forms of lifelong learning programs to bolster the national education reform. However, implementing such programs can be challenging for all the stakeholders of the programs, especially for adult students. The weaker foundational knowledge and insufficient online learning abilities of adult students particularly highlight the academic challenges they face. This study explores the academic challenges faced by adult learners in a Chinese vocational college’s LLL program. Focusing on ex-soldiers, unemployed individuals, migrant workers, and new professional farmers (aged 22–44), data were collected from 16 adult students via purposive sampling. Semi-structured interviews and document analysis revealed recurring thematic academic challenges. Additionally, the study found that adult student attributes (highest education level, age) significantly influenced the unique academic challenges they encountered. This research provides practical solutions to improve LLL programs and promote successful lifelong learning experiences for adult students. 

The Government of Indonesia has modernized the toll road transaction system by implementing the multi-lane free-flow (MLFF) project, set to operate commercially by the end of 2024. This project leverages Global Navigation Satellite System (GNSS) technology to identify vehicles using toll roads and establish a transaction mechanism that allows the MLFF Project Company to charge road users according to distance, vehicle category, and tariff levels. The project has result in a complex business arrangement between the Indonesia National Toll Road Authority (INTRA), Toll Road Companies (TRCs), and the MLFF Project Company. The aim of this paper is to review the regulatory and institutional framework of the MLFF project and analyze its challenges. The methodology employed is a qualitative framework for legal research, utilizing international literature reviews and current regulatory frameworks. The study assesses the proposed transaction architecture of the project and identifies commercial, political, and other risks associated with its implementation. Based on the analysis, the research identifies opportunities for regulatory improvements and better contracting arrangements. This research provides valuable insights into the regulatory landscape and offers policy recommendations for the Government to mitigate the identified risks. This contribution is significant to the academic field as it enhances understanding regulatory and institutional challenges in implementing advanced toll road systems.

As urbanisation increases, questions arise about the desirability of further urban growth, as it was not accompanied by corresponding economic growth, and social and environmental problems began to grow in the largest cities in the world. The objective of the article is to substantiate the limits of urbanization growth in Kazakhstan based on the study of theoretical views on this process, analysis of the dependence of social and economic parameters of 134 countries on the urbanisation level and calculation of the urbanisation level that contributes most to economic growth and social well-being. To achieve the goal, the following tasks have been set and solved: theoretical views on the process of urbanization have been generalized; a hypothesis has been put forward about the emergence of an “urbanization trap” in which the growth of large cities is not accompanied by economic growth and improvement of social well-being; an analysis of the dependence of socio-economic indicators on the level of urbanization has been carried out on the example of 134 countries of the world; the level of urbanization that maximizes economic growth and social well-being is calculated; the necessity of the development of small towns in Kazakhstan is substantiated. To solve the problems, the methods of logical analysis, analogies and generalizations, economic statistics, index, graphical, Pearson correlation analysis, Spearman and Kendall rank regression based on models in SPSS were used. As a result, the following conclusions are made: the hypothesis of a possible deterioration of socio-economic indicators in large cities is confirmed; the best positive result is demonstrated by the level of urbanization of 50%–59%. The recommendations are justified: in Kazakhstan, it is necessary to adhere to the level of urbanization no higher than 59%; the growth of urbanization should be ensured through the development of small towns; it is necessary to improve the methods of managing the process of urbanization and develop individual city plans.

Purpose: This article explores the adoption of Artificial Intelligence (AI) in Human Resource Management (HRM) in the UAE, focusing on the critical challenges of fairness, bias, and privacy in recruitment processes. The study aims to understand how AI is transforming HR practices in the UAE, highlighting the issues of bias and privacy while examining real-world applications of AI in recruitment, employee engagement, talent management, and learning and development. Methodology: Through case study methodology, detailed insights are gathered from these companies to understand real-world applications of AI in HRM. A comparative analysis is conducted, comparing AI-driven HRM practices in UAE-based organizations with international examples to highlight global trends and best practices. Findings: The research reveals that while AI holds significant potential to streamline HR functions such as recruitment, onboarding, performance monitoring, and talent management, it also discusses challenges and strategies companies face and develop in integrating AI into their HRM processes, reflecting the broader context of AI adoption in the UAE’s HR landscape. Originality: This paper contributes to the growing body of literature on AI in HRM by focusing on the unique context of the UAE, a rapidly developing market with a highly diverse workforce. It highlights the specific challenges and opportunities faced by organizations in the UAE when implementing AI in HRM, particularly regarding fairness, bias, and data privacy.

This study’s primary objective is to determine the financial repercussions, including expenses, profits, and losses, that certain stakeholders in the Tuong-mango value chain face at various distribution stages. This was achieved through the utilisation of stakeholders cost-benefit value chain analysis. These individuals collectively contributed 849 sample observations to the dataset including 732 farmers, 10 cooperative, 32 collectors, 25 wholesalers, 30 retailers, 12 exporters and processors, and 08 grocery stores/fruit. The robust financial performance of the Tuong-mango value chain is attributable to its integrated economic efficiency, as evidenced by its over USD 1 billion in revenue and USD 98.2 million in net income. The marketing channels, specifically channels 1, 2, and 3, generate a total of USD 906.1 million in revenue, yielding a net profit of USD 81.9 million. The combined sales from domestic marketing channels 4 and 5 total USD 160 million, yielding a net profit of USD 16.2 million. The findings indicate that due to their limited scope and suboptimal grade 1, farmers are the most vulnerable link in the supply chain. This study proposes three strategies for augmenting quality, fostering technological advancement, and facilitating the spread of benefits. This study’s findings contribute to the existing literature on value chain analysis as it pertains to various tropical fruits and vegetables. The study provides empirical evidence supporting the utility of the value chain method in policy formulation.