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.

The economy, unemployment, and job creation of South Africa heavily depend on the growth of the agricultural sector. With a growing population of 60 million, there are approximately 4 million small-scale farmers (SSF) number, and about 36,000 commercial farmers which serve South Africa. The agricultural sector in South Africa faces challenges such as climate change, lack of access to infrastructure and training, high labour costs, limited access to modern technology, and resource constraints. Precision agriculture (PA) using AI can address many of these issues for small-scale farmers by improving access to technology, reducing production costs, enhancing skills and training, improving data management, and providing better irrigation infrastructure and transport access. However, there is a dearth of research on the application of precision agriculture using artificial intelligence (AI) by small scale farmers (SSF) in South Africa and Africa at large. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) and Bibliometric analysis guidelines were used to investigate the adoption of precision agriculture and its socio-economic implications for small-scale farmers in South Africa or the systematic literature review (SLR) compared various challenges and the use of PA and AI for small-scale farmers. The incorporation of AI-driven PA offers a significant increase in productivity and efficiency. Through a detailed systematic review of existing literature from inception to date, this study examines 182 articles synthesized from two major databases (Scopus and Web of Science). The systematic review was conducted using the machine learning tool R Studio. The study analyzed the literature review articled identified, challenges, and potential societal impact of AI-driven precision agriculture.

Decentralized cryptocurrencies, such as bitcoin, use peer-to-peer software protocol, disintermediating the traditional intermediaries that used to be banks and other financial intermediaries, effectuating cross-border transfer. In fact, by removing the requirement for a middleman, the technology has the potential to disrupt current financial transactions that rely on a trusted authority or intermediary operator. Traditional financial regulation, primarily based on the command-and-control approach, is ill-suited to regulating decentralized cryptocurrencies. The present paper aims to investigate the policy option most suitable for regulating decentralized cryptocurrencies. The study employs content analysis method to effectuate the purpose of the study. The paper argues that the combination of both direct and indirect regulatory approaches would be a feasible option for regulating decentralized cryptocurrencies. The absence of centralized authority and the borderless nature of decentralized cryptocurrencies would make them antithetical to centralized direct regulation. Therefore, the findings of the study suggest that regulators should focus on regulating intermediaries bridging the connection between the online world (crypto ecosystem) and the physical world (the point of converting crypto into fiat money). These intermediaries can work as passive actors or surrogate regulators who are indirectly responsible for implementing policy options on behalf of the central authority.

Purpose—Quality service plays a significant role in enhancing customer satisfaction and loyalty. The main objective of this research is to investigate the effect of Salalah port service quality on customer satisfaction. Design/methodology/approach—This paper used a quantitative research design. Data were collected from 300 repeated customer of Salalah Port in Oman. Statistical Package (SPSS) version 25.0 was used for analysis of data and adopted to test the hypothesized model. Findings—The research findings confirm the positive influence of the five dimensions of service quality – tangible, empathy, reliability, responsiveness, assurance (TERRA) on customer satisfaction. Originality/value—The findings of this study develop the literature by adding empirical research evidence that the TERRA of Salalah port service quality which have a significant effect on customer satisfaction. The result also provide evidence from the Arab region where the data and research in this region are limited.

Using individual- and panel country-level data from 118 countries for the period 1981–2020, this study investigates the effects of national- and individual-level economic and environmental factors on subjective well-being (SWB). Two individual SWB indicators are selected: the feeling of happiness and life satisfaction. Additionally, two environmental factors are also considered: CO2 emissions by country level and personal perspective on environmental protection. The ordered probit estimation results show that CO2 emissions have a significant negative effect on SWB, and a higher perspective on environmental protection has a significant and positive effect. Compared with the average marginal effect of national income, CO2 emissions are a more important determinant of SWB when considering a personal perspective on protecting the environment. The estimation results are robust to various estimation model specifications: inclusion of additional air pollutants (CH4 and N2O), PM 2.5 and various sample groupings. This study makes a novel contribution by providing comprehensive insights into how both individual environmental attitudes and national pollution levels jointly influence subjective well-being.

This research aims to investigate the factors shaping the investment choices of individuals in Saudi Arabia concerning cryptocurrencies, particularly focusing on the influence of the Fear of Missing Out (FOMO) psychological phenomenon. This study employs a mixed-methods approach to comprehend the factors influencing Saudi investors' decisions in the cryptocurrency realm. Quantitative surveys are conducted to gauge perceptions of risk, return, regulatory factors, and social influence. Additionally, qualitative interviews delve into the nuanced interplay of these elements and the impact of FOMO on decision-making. Integrating the Theory of Planned Behavior and Behavioral Finance theories, this research offers a holistic understanding of cryptocurrency investment determinants. The combined quantitative and qualitative methods provide a comprehensive view, enabling an in-depth analysis of the subject matter. The study reveals that Saudi Arabian investors' decisions regarding cryptocurrencies are significantly influenced by multiple factors, including perceived risk, potential return, regulatory environment, and social dynamics. FOMO emerges as a crucial psychological factor, interacting with these influences and driving decision-making. This research underscores the intricate interplay between these factors and FOMO, shedding light on the dynamics of cryptocurrency investment choices in the Saudi Arabian market. The findings hold implications for policymakers, financial institutions, and investors seeking deeper insights into this evolving landscape. Drawing from the Theory of Planned Behavior and Behavioral Finance, it examines perceived risk, return, regulatory factors, and social influence in influencing cryptocurrency investment choices among Saudi investors, focusing on the influence of Fear of Missing Out (FOMO).  The research outcome provides insights for policymakers, financial institutions, and investors seeking to understand cryptocurrency investment dynamics in Saudi Arabia.