This study explores the potential of digital preservation in the documentation of colonial cultural heritage in Egypt. It also explores the stories behind historical wars to revive these sites and attract different segments of visitors. Documentation of these sites should enhance Egyptian colonial cultural heritage sites, which include battlefields, war memorials, commanders’ palaces, assassination and murder spots, cemeteries, and mausoleums. The purpose of this study was fulfilled through field visits supplemented with in-depth interviews with experts on colonial heritage sites in Egypt. The findings showed that technology could play a key role in implementing the storytelling documentation and interpretation of colonial history and its relevant events at the Egyptian sites. However, to date, these sites have not made the best use of technology for digital preservation and documentation due to many challenges. The study recommends that decision-makers should integrate technological innovation, which can revitalize the communities built on the ruins of colonialism and revive the heritage of popular resistance. Technological innovation could be implemented not only in digital preservation and documentation but also in service and marketing of these colonial heritage sites.

Financial literacy is an essential life skill today and plays a crucial role in business success. This study examined the relationship between college students’ financial literacy, financial management behavior, and entrepreneurial opportunity recognition. A survey was conducted among college students in the Busan and Gyeongnam regions, and a total of 272 responses were analyzed using SPSS 28.0. The results showed that financial literacy partially positively affects financial management behavior. Furthermore, financial management behavior positively influences entrepreneurial opportunity recognition. Financial management behavior partially mediates the relationship between financial literacy and entrepreneurial opportunity recognition. Improving the financial literacy of college students during adolescence serves as a motivation for entrepreneurship and significantly impacts their exploration and practice of various income activities to achieve their expected future living standards. The study’s findings indicate that for potential entrepreneurs, recognizing and promoting entrepreneurship as a source of innovation and growth requires incorporating financial literacy and desirable financial management behavior education into university curricula.

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.

The study investigates the impact of artificial intelligence (AI)-powered chatbots on brand dynamics within the banking sector, focusing on the interrelationships between AI implementation and key brand dimensions, including awareness, equity, image, and loyalty. Using structural equation modeling (SEM) analysis on data collected from 520 banking customers, the study tests eight hypotheses to explore the direct and indirect effects of AI-driven interactions on brand development. The findings reveal that AI chatbots significantly enhance brand awareness in banking services, demonstrating moderate positive effects on both brand equity and brand image. Notably, while brand awareness exerts a strong influence on brand image, it does not have a significant direct effect on brand loyalty. Instead, the study shows that brand loyalty is primarily developed through the mediating effects of brand equity and image, with brand image exerting a particularly strong influence on brand equity. For banking practitioners, these insights suggest a need to integrate AI chatbots within a comprehensive brand strategy that merges technological innovation with traditional relationship-building approaches. Limitations of the study and potential directions for future research are also discussed, providing avenues for further exploration of AI’s role in brand management.

Public open spaces, such as squares, parks, and sports fields, serve as crucial hubs during and after disasters, fostering a sense of normalcy and community, promoting social cohesion, and facilitating community recovery. Additionally, they offer opportunities for promoting physical and mental well-being during such crises. This study aims to enhance the responsiveness of public open spaces to disasters by prioritizing disaster resilience in their planning and design. This study consists of two main stages. Firstly, a literature review is conducted to explore the current trends in research on public open space planning and design and the incorporation of disaster resilience. Results indicate that the primary focus of the current research on planning and designing public open spaces centers around sociocultural, psychological, environmental, and economic benefits. There is limited emphasis on integrating disaster resilience into public open space planning and design, leading to a lack of clear guidance for planners and architects. The emphasis on disaster resilience in public open space planning and design mainly began after 2010, with a notable increase observed in the last six years (2017–2023). This emphasis notably centers on climate change impacts, followed by floods, and then earthquakes. Secondly, drawing on the pivotal role of public open spaces during disasters, the importance of urban planning and design, and the existing gap in incorporating disaster resilience in current research on public open space planning and design, this study develops a novel framework for enhancing public open spaces’ responsiveness to disasters through resilient urban planning and design, based on four main disaster resilience criteria: multifunctionality, efficiency, safety, and accessibility. The insights gleaned from this study offer invaluable guidance to planners, architects, and decision-makers, empowering them to develop public open spaces that can effectively respond to various circumstances, ultimately contributing to bolstering community resilience and sustainability.

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.