The failure to achieve sustainable development in South Africa is due to the inability to deliver quality and adequate health services that would lead to the achievement of sustainable human security. As we live in an era of digital technology, Machine Learning (ML) has not yet permeated the healthcare sector in South Africa. Its effects on promoting quality health services for sustainable human security have not attracted much academic attention in South Africa and across the African continent. Hospitals still face numerous challenges that have hindered achieving adequate health services. For this reason, the healthcare sector in South Africa continues to suffer from numerous challenges, including inadequate finances, poor governance, long waiting times, shortages of medical staff, and poor medical record keeping. These challenges have affected health services provision and thus pose threats to the achievement of sustainable security. The paper found that ML technology enables adequate health services that alleviate disease burden and thus lead to sustainable human security. It speeds up medical treatment, enabling medical workers to deliver health services accurately and reducing the financial cost of medical treatments. ML assists in the prevention of pandemic outbreaks and as well as monitoring their potential epidemic outbreaks. It protects and keeps medical records and makes them readily available when patients visit any hospital. The paper used a qualitative research design that used an exploratory approach to collect and analyse data.

Currently, there is a unique situation in the global economy, industrial eras coexist together, there is interaction and transformation of financial systems simultaneously within the framework of Industry 4.0 and Industry 5.0. New, digital resources are entering the economy, intellectual capital is becoming virtual, artificial intelligence is increasingly finding its application in the structure of financial support. Financial intermediation in developing countries is also subject to global trends, the active development of new instruments for developing economies is especially important. The aim of the study is to identify effective ways to develop financial intermediation in Industry 5.0 for the economies of developing countries. Based on the results of the study on the development of financial institutions mediation revealed a problem related to the lack of reasonable tools that could be used to improving the efficiency of the financial intermediaries market, proposed the main directions of such a process: mobilization of savings, distribution financial assets, payment system, risk management and control over market agents involved in financial operations.

Background: Digital transformation in the sports industry has become increasingly crucial for sustainable development, yet comprehensive empirical evidence on policy effectiveness and risk management remains limited. Purpose: This study investigates the impact of policy support and risk factors on digital transformation in sports companies, examining heterogeneous effects across different firm characteristics and regional contexts. Methods: Using panel data from 168 sports companies listed on China’s A-shares markets and the New Third Board from 2019 to 2023, this study employs multiple regression analyses, including baseline models, instrumental variables estimation, and robustness tests. The digital transformation level is measured through a composite index incorporating digital infrastructure, capability, and innovation dimensions. Results: The findings reveal that policy support significantly enhances digital transformation levels (coefficient = 0.238, p < 0.01), while financial risks demonstrate the strongest negative impact (−0.162, p < 0.01). Large firms and state-owned enterprises show stronger responses to policy support (0.312 and 0.278, respectively, p < 0.01). Regional development levels significantly moderate the effectiveness of policy implementation. Conclusions: The study provides empirical evidence for the differential effects of policy support and risk factors on digital transformation across various firm characteristics. The findings suggest the need for differentiated policy approaches considering firm size, ownership structure, and regional development levels. Implications: Policy makers should develop targeted support mechanisms addressing specific challenges faced by different types of firms, while considering regional disparities in digital transformation capabilities.

Focusing on Shanghai Port, this in-depth study explores how government support can make port organizations more competitive. This study shall implement qualitative analysis based on in-depth interviews with key industry and government leaders to break down the complicated actions taken by the government and how they have changed the operational and strategic skills of the port industry. Seven factors were found in our study to be the most crucial support factors: Financial, regulatory, infrastructure growth, talent, market, policy, and organizational support. In their ways, each of these groups undermines the ability of port businesses to compete. For instance, finance can make ports more competitive in aspects such as tax cuts, lower interest rates, innovation and R&D funds, financing programs, venture capital funds, and putting up R&D sites. Supporting regulations makes sure that there is fair competition and smooth operations. This is done by protecting intellectual property, keeping the market going smoothly, improving the business environment, and monitoring market regulations. Building new infrastructure, such as innovation and updated buildings, enables the smooth running of the port businesses and minimizes wastage of time; thus, more time is spent on production. Supporting talent, the market, and policy all work together to make the human capital, international cooperation, and strategic regulatory framework that a company needs to stay ahead in the long run. It is clear from organizational support how important collaborative networks are for making ports more competitive. These networks, for instance, can be of assistance in helping schools and businesses work together, create new technologies, and find ways for companies and colleges to study together. This study examines these support systems to determine where the government should step in and how the systems can be made better to make ports more competitive. In terms of practical contribution, this in-depth study helps policymakers and port workers plan for the future. This study shows a fair way for the government to support the port business, which changes with its needs and stays competitive in the world of trade.

The principal objective of this article is to gain insight into the biases that shape decision-making in contexts of risk and uncertainty, with a particular focus on the prospect theory and its relationship with individual confidence. A sample of 376 responses to a questionnaire that is a replication of the one originally devised by Kahneman and Tversky was subjected to analysis. Firstly, the aim is to compare the results obtained with the original study. Furthermore, the Cognitive Reflection Test (CRT) will be employed to ascertain whether behavioural biases are associated with cognitive abilities. Finally, in light of the significance and contemporary relevance of the concept of overconfidence, we propose a series of questions designed to assess it, with a view to comparing the various segments of respondents and gaining insight into the profile that reflects it. The sample of respondents is divided according to gender, age group, student status, professional status as a trader, status as an occasional investor, and status as a behavioural finance expert. It can be concluded that the majority of individuals display a profile of underconfidence, and that the hypotheses formulated by Kahneman and Tversky are generally corroborated. The low frequency of overconfident individuals suggests that the results are consistent with prospect theory in all segments, despite the opposite characteristics, given the choice of the less risk-averse alternative. These findings are useful for regulators to understand how biases affect financial decision making, and for the development of financial literacy policies in the education sector.