Modern technologies have intensified innovations and necessitated changes in public service processes and operations. Continuous employee learning development (CELD) is one means of the molecule-atom that keep employees motivated and sustain competitiveness. The study explored the efficacy of CELD in relation to modern technology in the South African (SA) public service departments between 2014 to 2023 era. Departments are faced with challenge of equipping their employees with adequate professional and technical skills for both the present and the future in order to deliver specific government priorities. Data for the study were gathered utilizing a qualitative semi-structured e-questionnaire. The study sample consisted of 677 human capital development practitioners from national and provincial government departments in SA. The inefficacy CELD and the inadequacy of technological infrastructure and service delivery can be attributed to the failure by executive management and senior managers to invest in CELD to prepare employees for digital world. It is recommended that departments should use Ruggles’s knowledge management, Kirkpatrick’s training, and Becker and Schultz’s human capital models as sound measurement tools in order to gain a true return on investment. The study adds pragmatic insight into the value of CELD in the new technological environment in public service departments.

Professional judgments in business valuation should be based on persuasive comparative data and conclusive empirical studies. However, these judgments are frequently made without these conditions, causing professional skepticism. An appraiser should explain in detail what was done to get the market value because valuation is the initial crucial step in the investment decision process. In socially responsible investment schemes, an appraiser has a fiduciary duty and a vital role in protecting the public from fraud and the risk of asset value destruction. Professional skepticism is essential to direct the appraiser’s judgment towards independent valuation for the public interest, assisting in evaluating the relevance and reliability of information, especially relating to social, environmental, and ethical issues. This paper studies the business valuation process from a behavioral finance perspective in the United States and Indonesia, aiming to tweak business valuation practices, identify biases, and mitigate them to ensure the market value does not shift far from fairness opinion. The case study explores experiences from the professional role-learning process. The results highlight the need for an appraisal protocol in business valuation, improvements in the discount for lack of marketability application, and these findings are pertinent to business appraisers and regulators. Recommendations include enhancing the clarity of professional judgments and the integration of recent empirical studies into practice.

This case study employs the Asset-Based Community Development (ABCD) theory as a conceptual framework, utilizing semi-structured interviews combined with focus group discussions to uncover the driving forces influencing rural revitalization and sustainable development within communities. ABCD is considered a transformative approach that emphasizes achieving sustainable development by mobilizing existing resources within the community. Conducted against the backdrop of rural revitalization in China, the study conducts on-site investigations in Yucun, Zhejiang Province. Through the analysis of Yucun’s community development and asset utilization practices, the study reveals successful experiences in various aspects, including community construction, industrial development, cultural heritage preservation, ecological conservation, organizational management, and open economic thinking. The results indicate that Yucun’s sustainable development benefits from its unique resources, leveraging policy advantages, collective financial organizations, and open economic thinking, among other factors. These elements collectively drive rural revitalization in Yucun, leading to sustainable development.

The rise of financial inclusion has notably increased household engagement in risky financial asset allocation, posing challenges to macro-financial stability. This study explored the crucial role of financial literacy in enabling households to effectively engage with complex financial markets and products. Specifically, it examined how different aspects of financial literacy—knowledge, attitudes, and skills—influence both the participation and depth of household investment in risky financial assets in China. Utilizing a comprehensive dataset from the 2019 China Household Finance Survey, which included 32,458 households, this study employed a robust indicator system and regression analysis via STATA 17.0 to assess these impacts. The results demonstrated that enhancements in financial literacy significantly foster increased engagement and deeper involvement in risky asset allocation, particularly through improved financial attitudes. Additionally, the analysis revealed that households led by women show a higher propensity towards risky asset investments than those led by men. These insights suggested the potential for targeted financial education to improve the financial health and economic resilience of Chinese households.

This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.