Interconnected components of holistic development, such as being thankful, addressing basic psychological needs, and acting effectively toward others, should be a priority for college athletes. Athletes at the College level need all-encompassing support systems to ensure their health, happiness, and success because of the special difficulties they have juggling their academic, athletic, and personal schedules. Problems with work-life balance, stress, and performance expectations all impede College Student Athletes’ holistic development. A thorough plan that considers all of the social, emotional, and psychological aspects impacting athlete development is necessary to overcome these obstacles. An Integrated Holistic Development Program for College Athletes (IHDP-CA) is suggested in this paper as a method that incorporates various aspects of positive psychology, mindfulness, resilience training, and the enhancement of interpersonal skills. Athletes at the College level can benefit from this all-encompassing program’s emphasis on helping others, developing an attitude of gratitude, and meeting basic psychological requirements. Sports counseling services, schools, and College athletic teams can all benefit from the IHDP-CA. A more positive and supportive sporting environment can be achieved when the program takes a more holistic approach to athletes’ needs, improving their mental health, social connections, and overall performance. The possible effect of the IHDP-CA on the holistic development outcomes of College Student-Athletes will be predicted through simulation analysis. To gain a better understanding of the program’s long-term viability, efficacy, and scalability, this analysis will run simulations of different situations and tweak program settings.

Purpose: The purpose of this paper is to explore the impact of Artificial Intelligence on the performance of Indian Banks in terms of financial metrics. The study focused specifically on the NIFTY Bank Index. The paper also advocates that a greater transparency in disclosing AI related information in a Bank’s annual report is required even if it is voluntary. Design/Methodology/Approach: The paper uses a mixed method approach where quantitative and qualitative analysis is combined. A dynamic panel data model is used to understand the impact of AI of Return on Equity (RoE) of 12 Indian Banks in the NIFTY Bank Index over a five-year period. In addition to that, Content analysis of annual reports of banks was conducted to examine AI related disclosure and transparency. Findings: The paper highlights that the integration of Artificial Intelligence (AI) significantly influences the financial performance of sample banks of India. Return on Equity the specific parameter positively influenced with adoption of AI. The profitability of banks is positively impacted by reduced errors and improved operational efficiency. The content analysis of annual reports of the banks indicates different approach for AI disclosure where some banks give detailed information and some are not transparent about AI initiatives. The findings suggest that a higher level of transparency could enhance confidence of all stakeholders. Theoretical Implications: The positive relation between adoption of AI and financial performance, specifically ROE, gives a foundation for academic research to explore the dynamics of emerging technology and financial systems. The study can be extended to explore the impact on other performance indicators in different sectors. Practical Implications: The findings of this study emphasize the importance of transparent AI related disclosures. A detailed reporting about integration of AI helps in enhanced stakeholders’ confidence in case of banking industry. The regulatory framework of banks may also consider making mandatory AI disclosure practices to ensure due accountability to maximize the benefits of AI in banking.

The northern territories of Russia need high-quality strategic digital changes in the structure of the regional economy. Digitalization and the introduction of digital technologies in the medium term will be able to transform economic relations in the old industrial and raw materials regions of the North, improve the quality of life of local communities. The growth of digital inequality among the regions under study leads to disproportions in their socio-economic development. The purpose of this study is to develop and test a methodology for assessing the level of development of the digital infrastructure of the Russian northern regions, including classification of an indicators system for each level of digital infrastructure, calculation of an integral index and typology of the territories under study. The objects of the study were 13 northern regions of the Russian Federation, the entire territory of which is classified as regions of the Extreme North and equivalent areas. The methodology made it possible to determine the level of technical, technological and personnel readiness of the northern regions for digitalization, to identify regions with the best solutions at each level of digital infrastructure development. The analysis of the results in dynamics helped to assess the effectiveness of regional policy for managing digitalization processes. As a result, the authors came to the conclusion that increasing the competitiveness of northern regions in the era of rapid digitalization is possible through investments in human capital and the creation of a network of scientific and technological clusters. The presented approach to assessing the development of individual levels and elements of digital infrastructure will allow for the diagnosis of priority needs of territories under study in the field of digitalization. The results of the study can form the basis for regional policy in the field of sustainable digital development of Russia.

This empirical paper investigates the impact of green brand knowledge, green trust, and social responsibility on consumer purchase intentions within the developing nation of Pakistan. By highlighting the importance of these factors in influencing consumer behavior towards environmentally friendly products, the study aims to address the pressing need to mitigate environmental pollutants. Employing a quantitative research methodology, the study utilizes a questionnaire survey adapted from previous research to gather data. Regression analysis reveals significant and positive relationships between green brand knowledge, green trust, social responsibility, and consumer purchase intentions. Notably, green brand knowledge emerges as the most influential factor in shaping purchase intentions. This study contributes to the existing literature by providing insights into the dynamics of consumer behavior in a developing country context and offers practical implications for managers and decision-makers seeking to align organizational goals with consumer preferences for green brands. The findings underscore the importance of integrating environmental considerations into marketing strategies to meet consumer demand for sustainable products and foster environmental stewardship.

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

While the notion of the smart city has grown in popularity, the backlash against smart urban infrastructure in the context of changing state-public relations has seldom been examined. This article draws on the case of Hong Kong’s smart lampposts to analyse the emergence of networked dissent against smart urban infrastructure during a period of unrest. Deriving insights from critical data studies, dissentworks theory, and relevant work on networked activism, the article illustrates how a smart urban infrastructure was turned into both a source and a target of popular dissent through digital mediation and politicisation. Drawing on an interpretive analysis of qualitative data collected from multiple digital platforms, the analysis explicates the citizen curation of socio-technic counter-imaginaries that constituted a consent of dissent in the digital realm, and the creation and diffusion of networked action repertoires in response to a changing political opportunity structure. In addition to explicating the words and deeds employed in this networked dissent, this article also discusses the technopolitical repercussions of this dissent for the city’s later attempts at data-based urban governance, which have unfolded at the intersections of urban techno-politics and local contentious politics. Moving beyond the common focus on neoliberal governmentality and its limits, this article reveals the underexplored pitfalls of smart urban infrastructure vis-à-vis the shifting socio-political landscape of Hong Kong, particularly in the digital age.