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 study aims to explore the feasibility of using virtual reality technology to educate students with learning difficulties in the Asir region. To achieve the study aims, the researcher employed a descriptive design and deployed a quantitative technique, depending on the questionnaire as the main instrument for data collection. The research was carried out on a cohort of 240 educators hailing from the Asir region who were enlisted through a process of random sampling. The results of this study show that factors like infrastructure, human resources, administrative regulation, and student population have an impact on the use of virtual reality technology. The results suggest that there are no statistically significant differences in the development of using virtual reality technology among teachers of students with learning disabilities in the Asir region when taking into account factors such as experience and level of qualification.

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.

Educational quality policies are a basic principle that every Peruvian university educational institution pursues in accordance with Law No. 30220, with the objective of training highly competent professionals who contribute to the development of the country. This study to analyzes educational quality policies with the student’s satisfaction of public and private universities in Peru, according to social variables. The study was descriptive-comparative, quantitative, non-experimental, and cross-sectional. One thousand (1000) students from two Peruvian universities, one public (n = 500) and one private (n = 500), were purposively selected by quota using the SERVQUALing instrument. The findings indicate a moderate level of satisfaction reported by 49.2% of participants, with a notable tendency towards high satisfaction observed in 40.9% of respondents. These results suggest that most students perceive that the actual state of service quality policies are in a developmental stage. The results, therefore, indicate that regulatory measures, including university laws, licensing, and accreditation, significantly influence outcomes. These measures are essential for the effective functioning of universities. In addition, the analysis revealed that female and male students at private universities showed higher levels of satisfaction with the educational services offered. It is concluded that educational quality policies in Peru are still being executed, because the implementation of the University Law is in process, according to the satisfaction of the student, this must be improved in central aspects such as optimizing human resources, infrastructure, equipment, curricular plans that differ from the public to the private university, In addition, this should lead to improving and redefining current policies on educational quality and the economic policies that finance the educational service.

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.