This study examines the spatial distribution of socioeconomic conditions in Colombia, using Moran's Index as a tool for spatial autocorrelation analysis. Key indicators related to education, health, infrastructure, access to basic services, employment, and housing conditions are addressed, allowing the identification of inequalities and structural barriers. The research reveals patterns of positive autocorrelation in several socioeconomic dimensions, suggesting a concentration of poverty and underdevelopment in certain geographic areas of the country. The results show that municipalities with more unfavorable conditions tend to cluster spatially, particularly in the northern, northwestern, western, eastern, and southern regions of the country, while the central areas exhibit better conditions. Permutation analyses are employed to validate the statistical significance of the findings, and LISA cluster maps highlight the regions with the highest concentration of poverty and social vulnerability. This work contributes to the literature on inequality and regional development in emerging economies, demonstrating that public policies should prioritize intervention in territories that exhibit significant spatial clustering of poverty. The methodology and findings provide a foundation for future studies on spatial correlation and economic planning in both local and international contexts.

The young Muslim generation’s embracing digital platforms for Zakat payments represents a dynamic fusion of enduring religious values with the modern digital landscape, heralding a new era in Islamic charitable practices. This trend illustrates a profound transformation within the Islamic world, where the pillars of faith are being reimagined and revitalized through the lens of technological advancement. The present study delved into the factors influencing the young Muslim generation’s preference for digital platforms in Zakat transactions across Indonesia and Malaysia. We examined variables such as Performance Expectancy, Effort Expectancy, Social Influence, Trust, Zakat Literacy, and Digital Infrastructure, aiming to discern their impact on the propensity for digital Zakat contributions with the extension of Unified Theory of Acceptance and Use of Technology (UTAUT) model. The research encompassed a diverse sample of 382 participants and utilized advanced methodologies, specifically Partial Least Squares Structural Equation Modeling (PLS-SEM) and PLS Multi Group Analysis (PLS-MGA), for rigorous data analysis. The results indicated that Effort Expectancy, Social Influence, Digital Infrastructure, and Zakat Literacy notably influenced the use of digital platforms for Zakat. Furthermore, PLS-MGA uncovered significant cross-country differences where Digital Infrastructure showed a more pronounced positive impact in Malaysian context, whereas Social Influence had a greater effect in Indonesia. These findings offer critical insights into the young Muslim community’s digital engagement for religious financial obligations, underscoring the need for tailored digital Zakat solutions that cater to the unique preferences of this demographic. This research not only enriches the understanding of digital adoption in religious practices but also challenges the notion of a universal approach, advocating for context-specific strategies in the realm of digital religious financial services. Future researchers are suggested to consider longitudinal investigations as well as examining cross-regional contexts in this realm of research.

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.

Cooperatives have become significant contributors to the realization of the Sustainable Development Goals No. 1: No Poverty. Transitioning associations to cooperatives is crucial for promoting sustainable economic development, empowering communities, and enhancing collective well-being. This research assessed the readiness of Small-Scale Fisheries (SSF) communities in the Global South to form a cooperative. This research employed an exploratory research approach in six coastal Barangays of Batad, situated in the 5th District of Iloilo Province. The findings indicated that respondents have a slight level of awareness with regard to the advantages and economic advantages associated with becoming part of a cooperative. On the other hand, there was a clear difference in members’ perceptions of the benefits and financial returns that comes with belonging to a cooperative. According to the study, females are more likely to support the association’s move towards a cooperative structure, especially younger individuals. The main issue highlighted was the lack of skilled officers and inadequate resources and training for association members. A lecture on Cooperative Awareness and capability trainings on financial management, bookkeeping, and credit management should be organized in order to increase associations readiness to be a cooperative.

Malaysia’s economic development strategies have evolved significantly since independence, focusing on reducing poverty, enhancing education, and integrating technology to foster sustainable growth. Despite substantial progress, challenges persist in achieving inclusive development across rural and urban sectors. This study examines the effectiveness of Malaysia’s New Economic Model (NEM) in addressing poverty and unemployment through technological and educational advancements. Employing a qualitative approach, it reviews literature on technology’s impact on economic growth, poverty alleviation, and the role of tertiary education in national development. Analysis reveals that while NEM initiatives have attracted foreign investment and improved infrastructure, gaps remain in educational access and technological self-reliance. The findings underscore the need for targeted policies that enhance educational outcomes, promote inclusive technology adoption, and address structural inequalities to achieve sustainable economic development. Recommendations include bolstering vocational training, enhancing rural infrastructure, and fostering public-private partnerships in technology innovation to ensure equitable economic progress.