Finance and Management

This study examines the process of predicting and managing the risks of voluntary employee turnover at nuclear enterprises. The research focuses on the human resources potential of a high-tech nuclear enterprise. Particular attention is paid to identifying and quantifying risk factors specific to the nuclear industry, such as career path clarity, participation in innovative projects, and working under sensitive conditions, with the development of an interpretable mathematical model. Highly qualified personnel are a key asset for nuclear enterprises, where employee turnover can pose critical risks, including the loss of unique production competencies, reduced safety, and increased operating costs. The objective of the study is to develop a multifactorial model for predicting the individual risk of voluntary employee turnover at nuclear enterprises. The research methodology is based on the analysis of HR management system data. Logistic regression, which ensures highly interpretable results, was used to construct the predictive model. The model was chosen due to its advantages for binary classification problems, such as high interpretability of results, robustness to multicollinearity, and relatively low computational complexity. The study demonstrated that decreased career trajectory clarity increases the risk of dismissal, as does a lack of participation in innovative projects. The developed model allows for segmenting personnel into three risk groups and developing targeted interventions for each category of employee. The key findings include prioritizing organizational, psychological, and career factors over financial incentives. The author's particular contribution lies in the creation of a comprehensive proactive risk management system, including a mechanism for assessing the cost-effectiveness of measures taken. The novelty of the study lies in adapting the survival analysis methodology to the nuclear industry and integrating quantitative forecasts with practical HR strategies. The developed model is an effective tool for HR departments at nuclear enterprises, facilitating the transition from reactive to predictive management of personnel stability.

The subject of the study is the methods and tools for managing the finances of creative clusters. The object of the study is the clusters created on in different regions of the Russian Federation. The aim of the study is to scientifically justify the application of public-private partnership agreements by entities in the creative industries when interacting with authorized government bodies regarding the use of state property and budget funds. The relevance of the study is due to the high need to develop scientific foundations for the functioning of creative clusters. Relatively new legislative norms on creative industries need to be supplemented in order to achieve synchronicity and completeness of regulatory provisions. The subject area of the research includes public-private partnership instruments that need to be projected at the legislative level onto creative industries, combining the economic and legal specifics of the issue. The research required the use of general scientific methods: induction and deduction, analysis and synthesis, abstraction and analogy. However, the main results were obtained using specific scientific methods dictated by the specifics of the issues at hand: comparative and systemic analysis, legal analysis, structural-logical analysis, and specific techniques from regional studies (most notably, the method of zoning). The scientific novelty of the research lies in the development of a number of definitions and conceptual provisions for the implementation of public-private partnership principles in the activities of creative clusters and their regulatory consolidation. The results of the study are highlighted as follows: - an authorial approach to defining the concepts of 'finances of the creative cluster' and 'management of finances of the creative cluster' has been formulated; - an analysis of the sources of funding for the activities of creative clusters has been conducted, including the peculiarities of foreign experience, which collectively served as a basis for developing proposals to improve the methods of financial management of entities in the creative industries based on the principles of public-private partnership; - schemes have been developed that reflect the essence of the financial and property conditions for agreements between entities in the creative industries and the state under various models of public-private partnership; - proposals have been submitted to the current legislation that expand the possibilities for incorporating legally established principles of public-private partnership into the activities of creative clusters. The conclusions and results have practical value for economically active entities in the creative industries, authorised and advisory government bodies for the purpose of developing government policy for investment, sectoral and regional development.

The subject of the research is the multifaceted influence of artificial intelligence (AI) technologies on the socio-economic development of regions in Russia. The author provides a detailed examination of aspects such as the directions of AI's impact on key sectors of the regional economy (business, public administration, infrastructure) and the conditions for maximizing positive effects. Particular attention is given to the fact that the successful integration of artificial intelligence and digital platforms can become a significant factor in the sustainable development of territories, ensuring a balance between economic efficiency, social justice, and technological security. The relevance of the research is determined by the gradual transformation of artificial intelligence from a narrowly technological tool into a systemic element of territorial development. Its implementation can not only optimize individual processes but also form a new paradigm of management based on data, forecasting, and service personalization. The research methodology includes an analysis of regulatory documents (including the national project "Digital Economy"), the peculiarities of AI implementation in large companies and public authorities in the regions of the Russian Federation, as well as a systematic and comparative analysis of the effects of digitalization in different types of settlements. Data processing was conducted based on official statistics and reports from relevant agencies. The main conclusions of the conducted research include the identified effects of implementing AI technologies in public authorities through cost reduction and increased citizen satisfaction, as well as in business processes. Key barriers to successful implementation have been identified, including infrastructure deficits in small towns, a lack of personnel, high costs of scaling, and gaps in the regulatory framework. The scientific novelty lies in the systematization of conditions for maximizing the effects of artificial intelligence for regional economies and clarifying the possibilities of using digital platforms for these processes. The area of application of the results includes the formation of regional policy in the field of digital transformation, the development of retraining programs, the design of information and technological infrastructure, and the improvement of legal regulation of artificial intelligence. The conclusion is drawn regarding the necessity of a comprehensive strategy including the development of digital infrastructure, cross-sector partnerships, and legislative adaptation.

The subject of this research is the transformation process of key motives determining consumer behavior in the Russian e-commerce market within the context of the established dominant role of marketplaces. The study examines the shift in users’ motivational structure from relatively simple models (e.g., searching for a low price or a specific item) to a complex system of drivers shaped by the very architecture of digital platforms. Specifically, the subject encompasses the analysis of the influence of factors such as platformisation and the development of ecosystem services (delivery, finance, media) on changes in the significance and interrelation of traditional motives: price-related, service-logistical (convenience, speed), trust-based (towards the seller or brand), and socio-psychological (status, self-identification). A separate aspect is the investigation of the heterogeneity of this transformation—differences in motivation across age, income, and behavioral segments of consumers—as well as the role of algorithmic personalization, loyalty programs, and user-generated content in shaping new behavioral patterns, such as “promotional expertise” and the redistribution of institutional trust from individual sellers to the platform as a guarantor. The aim of the work is to identify key changes in the motivational structure of marketplace users and to assess their implications for the strategies of market participants and regulators. The methodological foundation constitutes a mixed-methods approach, combining a large-scale online survey of marketplace users, analysis of industry statistics, content analysis of user reviews, and a series of in-depth interviews. The main findings of the study include the identification of an increasing complexity in consumers’ motivational structure and the isolation of motives specific to the platform economy: orientation towards ecosystem-based services, institutional trust in the platform, and “promotional expertise” as a strategy for optimising overall benefit. The author’s particular contribution to the research topic lies in refining the content of classical motives (price, service, socio-psychological) in relation to marketplaces and demonstrating their heterogeneity among different age, income, and behavioral user groups. The novelty of the research consists in conceptualising marketplaces as an institution that not only satisfies existing needs but also generates new motives through interface architecture, algorithmic logic, and ecosystem effects. This provides a basis for practical recommendations concerning audience segmentation, platform positioning, and the development of regulatory policy.

The article is dedicated to studying the possibility of using proportional stratified sampling as a method of survey observation in the agricultural census aimed at obtaining representative data on the resources of personal subsidiary farms. The purpose of the work is to test and evaluate the effectiveness of this approach to reduce data collection costs and improve the quality of statistical estimation of resources of agricultural producers in pilot municipal districts. Additionally, the article addresses issues related to the construction of stratification variables, calculation of sample sizes by strata, weighting procedures, and correction of biases, as well as criteria for accuracy and robustness of estimates. Field observation schemes, quality control algorithms, and approaches to assessing budget savings are presented. The analysis includes validation of the obtained estimates using benchmarking, bootstrap resampling, and comparison with complete local censuses. The study used the method of proportional stratified sampling with stratification by localities of municipal districts and random selection within the strata, as well as the calculation of representativeness errors by comparing sample and population data on crop areas and livestock numbers. The novelty of the research lies in the fact that, for the first time using real microdata from the Agricultural Census of 2016, practical testing of the sampling approach has been conducted for the preparation of the All-Russian Agricultural Census 2027. Methodological and operational limitations of proportional stratified sampling were identified when dealing with scarce and spatially unevenly distributed resources. Satisfactory results were obtained only for widespread and evenly represented indicators (such as main crop areas and large livestock), while for rare crops, niche directions, and small groups of animals, the sampling representativeness proved insufficient even with post-stratification and model-based overestimation of small areas. Considering the requirements for completeness and comparability of data for policy monitoring, it was recognized that a complete method is preferable for the All-Russian Agricultural Census 2027, while sampling procedures are reasonably used as a supplementary tool for calibration, post-census checks, and quality control.

This study conducts a comparative analysis of the competitiveness of two leading streaming platforms – Netflix and Shahid – in the Middle East and North Africa (MENA) market. Netflix, as a global company with an extensive infrastructure, seeks to expand its presence in the region, while Shahid, owned by the MBC Group media holding, focuses on local content and deep integration with regional specifics. The aim of the study is to identify the strengths and weaknesses of both platforms, determine their competitive advantages and weaknesses in terms of consumer demand, market structure, and interaction with supporting industries. Using a comprehensive approach, the work demonstrates how global and local strategies adapt to the cultural and economic conditions of the region and how this impacts the companies' positions in the rapidly growing media consumption segment. The methodological basis of the research is Michael Porter's "Diamond Model," which allows for structuring the analysis of competitiveness through four key dimensions: factor conditions, demand conditions, related and supporting industries, and firm strategy and rivalry. The scientific novelty of the study lies in the systematic comparison of global and regional streaming platforms using Porter's model in relation to the MENA market for the first time. Unlike most previous works that were limited to describing cultural differences or technical aspects of services, this work combines quantitative indicators, market data, and theoretical analysis, enabling an objective assessment of competitiveness. The results show that Shahid holds leading positions in the region due to a high level of content localization, numerous partnerships with telecommunications operators, and support from governmental bodies. At the same time, Netflix retains a global advantage due to its brand, technological infrastructure, and financial stability. The conclusions drawn indicate the need for Netflix to deepen investments in local productions and partnerships, while Shahid should adopt advanced global technologies and improve its pricing policy. Thus, the study demonstrates that success in the MENA market is only possible with a comprehensive consideration of cultural, institutional, and economic factors, as well as flexible adaptation of strategies by both global and local players.

The subject of the study is the structural shifts and adaptive potential of Russia's regional economies during the period of 2018–2023. The object of the study is twelve representative regions of the Russian Federation representing different economic models. The authors examine in detail such aspects of the topic as changes in the regional sectoral specialization under the influence of external shocks, including the COVID-19 pandemic and sanctions pressure. Special attention is paid to analyzing the dynamics of economic concentration at the regional level and identifying successful adaptation strategies. The researchers describe the processes of economic diversification in some regions and the strengthening of raw materials dependence in others. They also analyze in detail cases of risky hyper-specialization and structural stagnation. The paper presents an original typology of regions based on the nature of their structural transformations, which allows for predicting their resilience to external challenges. The practical significance of the study lies in the development of recommendations for a differentiated regional policy aimed at increasing the adaptive potential of the economies of the Russian Federation's constituent entities in conditions of instability. The calculation of the Herfindahl-Hirschman index (HHI) as a key tool for assessing the level of diversification of regional economies was used as a methodological basis. The paper analyzes various approaches to measuring economic concentration and justifies the choice of HHI for monitoring structural changes. The results revealed four types of adaptation trajectories. The first group demonstrates successful diversification with a 33.4–53.2% decrease in HHI due to the redistribution of resources to the manufacturing and IT sectors. The second group is characterized by structural stagnation with hyper-concentration on the extractive sector. The third group shows moderate transformation, while the fourth group exhibits risky hyper-specialization with an increase in HHI up to 146.2%. For each group, key factors have been identified that contributed to the chosen development trajectory, including investment activity, government support, and institutional conditions. The scientific novelty lies in the development of a comprehensive typology of regions based on the nature of structural shifts. The practical significance lies in creating a basis for differentiated regional policies aimed at increasing resilience to external challenges through targeted promotion of diversification. The findings allow for assessing the effectiveness of adaptation strategies and formulating recommendations for public authorities to develop the competitiveness of regional economies in unstable conditions.