Firms, recognizing their Corporate Social Responsibility (CSR), are becoming catalysts for societal change by integrating Environmental, Social and Governance (ESG) criteria into their activities. The fashion industry exemplifies this effort, with an increasing number of companies embracing sustainability and ethical practices. In this context, our purpose is to provide a clear and comprehensive picture of the link between sustainability and business performance in the fashion industry. This work presents a Multivariate Regression Analysis, scrutinizing both external perspectives through stock prices and internal perspectives via profitability indices. Our aim is to discern the intricate relationship between sustainability practices and financial performance within the fashion industry, aligning ESG criteria with long-term economic success. Our regression analysis reveals a significant positive correlation between ESG scores and stock prices, indicating investor recognition of ESG performance as a crucial investment criterion. However, when focusing internally on profitability, the ESG score does not exhibit statistical significance, suggesting a yet-to-be-established connection between ESG policies and corporate profitability. This study underscores the evolving role of companies as sustainability promoters, emphasizing the crucial role of ESG performance in shaping investor perceptions. Nevertheless, it also highlights the need for further exploration into the intricate relationship between sustainable policies and corporate profitability. As businesses increasingly embrace sustainability, in fact, it could become paramount for informed decision-making and fostering ethical societal and environmental progress.

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.

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.

Mangifera indica L. (Mango, Anacardiaceae) is a popular tropical evergreen tree known for its nutritional and medicinal values. It is native to India and Southeast Asia and is known as the “king of fruits” in India and the Philippines. It is considered important in Ayurveda and other systems of medicine. Mango fruit is unique in its taste, colour, aroma, and nutritional qualities. Mangoes are a rich source of polyphenols (Mangiferin, Gallotannins, Quercetin, Isoquercetin, Ellagic acid, Glucogallin, Kaempferol, Catechins, Tannins, and the unique Xanthonoid), phenolic acids (Hydroxybenzoic acids- Gallic, Vanillic, Syringic, Protocatechuic, and p-Hydroxybenzoic acids, Hydroxycinnamic acid derivatives-p-Coumaric, Chlorogenic, Ferulic, and Caffeic acids), flavonoids (β-carotene, α-carotene, β-cryptoxanthin, and Lutein), Vitamin A, Vitamin-B6 (pyridoxine), Vitamin-C, Vitamin-E, Carbohydrates, Amino acids, Organic acids,  micronutrients (Potassium, Copper), fats (Omega-3 and 6 polyunsaturated fatty acids), dietary fibre and certain volatile compounds. About 25 different types of carotenoids have been isolated from the fruit pulp, which contributes to the colour of the fruit. Phytochemical and nutrient content may vary depending on the cultivar. Mangoes possess potential medicinal properties such as antioxidant, gastro-protective, anti-inflammatory, analgesic, immunomodulatory, anti-microbial, and many more. Mango fruit is an abundant source of all essential nutrients and phytochemicals; it could be ultilized as a nutritional supplement in the prevention and cure of several diseases. A comprehensive report on the nutritional and medicinal properties of fruit is presented below.

This research article explores the intricate relationship between cultural impacts and leadership styles in social science management. It emphasizes the importance of cultural-informed decision-making, highlighting its role in fostering inclusive managerial choices. The study also delves into how diverse leadership styles enhance team dynamics and collaboration, contributing to an innovative work environment. While recognizing the potential benefits, challenges like miscommunications are acknowledged, with recommendations for leadership development programs. The research underscores the significance of leadership flexibility in managing diverse teams. In conclusion, the article emphasizes the positive impact of cultural awareness on decision-making, collaboration, and innovation in social science management.

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.