Purpose: to clarify the theoretical and methodological grounds for interpreting generative artificial intelligence (GenAI) as the second wave of cognitive externalization and to reveal the organizational and economic and marketing consequences of this process.

Methods: the study is designed as a systematized conceptual and integrative review and uses PRISMA logic to ensure transparency of the source search, cleaning and selection. Two bodies of literature – on writing and on GenAI – are compared in international article databases; an additional full-text control search was conducted in the Russian Science Citation Index. The sources were analyzed taking into account their theoretical density, cognitive function and suitability for economic and marketing interpretation.

Results: cognitive offloading is shown as a mechanism for reducing the load on memory, attention and primary information processing, whereas cognitive externalization describes a broader transfer of cognitive functions into external symbolic, material and computational systems. Writing established external memory and repeated access to meaning; GenAI becomes involved in intermediate operations of knowledge work: task framing, criteria formation, generation of alternatives, argumentation and preparation of a preliminary decision framework. Thus, not only the speed of text production changes, but also the distribution of responsibility for task framing, result verification and choice.

Conclusions and Relevance: the article proposes an interpretive framework that allows considering GenAI as external procedural mediation. For marketing, this means changes in professional competences, work with market information, choice interfaces and value formation mechanisms. The practical implications require further empirical testing in organizations, marketing departments and sectoral markets.

Purpose: to substantiate the concept of two-tier architecture for the Russian securitization market, which functionally differentiates classical structures based on special purpose vehicles (SPVs) and securitized digital financial assets built on distributed ledgers.

Methods: the paper is designed as a qualitative conceptual and theoretical inquiry at the intersection of applied financial engineering and institutional analysis. Its analytical framework combines structural diagnosis of the Russian securitization market, institutional functional comparison of classical and digital deal configurations, qualitative analysis of control and risk allocation across deal stages, and identification of institutional conditions under which the digital segment may form an autonomous second market tier.

Results: the study shows that the conventional Russian securitization model is approaching an institutional efficiency ceiling: the market remains highly concentrated, while the heavy infrastructure of the deal constrains non-mortgage issuance and small- to medium-scale transactions. The article treats digital securitization as the institutional foundation of a second market tier designed for shorter, more flexible and information-intensive assets. The analysis also specifies the cost threshold limiting the use of classical SPV structures for smaller non-mortgage pools.

Conclusions and Relevance: digitalization is treated as a condition for the emergence of the second tier of Russian securitization. Classical bond structures retain their advantage in long-term mortgage funding, whereas digital financial assets gain institutional advantages in working-capital finance, factoring, SME finance, and selected categories of distressed assets.

Purpose: to provide a theoretical and applied justification for the transformation of agency relationships in financial intermediation in the context of the AI agents use.

Methods: the study utilized theoretical, comparative, and structural-analytical approaches, enabling a comparison of the agency theory tenets with the integration of AI in financial intermediation. Classic tenets of the agency theory are consistently correlated with the conditions for the transfer of functions to algorithmic systems.

Results: it was established that with the AI integration, agency costs do not disappear, but their source changes. It was found that in algorithmic environment, residual losses can no longer be explained solely by the divergence of interests between the principal and the agent, as system behaviour is determined by training parameters, data quality, the chosen goal, and the possibility of subsequent verification of the result. A framework is proposed that allows distinguishing the sources of residual losses and observing how they develop at successive stages, using credit scoring as an example. It is also shown that the principal's position in such conditions changes, as the bank's past actions, embedded in the training dataset, themselves become a source of future deviations.

Conclusions and Relevance: the study demonstrates that the use of AI agents requires a refinement of the agency theory as it applies to algorithmic agents, since the source of costs shifts from the personal interest to the training conditions, data composition, suitability of the previously trained system, and the ability to explain the program's actions. A comparison of coordinated and swarm-based AI agent organizations reveals that the difference between them lies not in the composition of agency losses, but in the way they arise, accumulate, distribute among digital agents, and are subsequently explained. The results of this study can be used in further research on AI agents in financial intermediation, as well as in the development of procedures for implementing algorithms in banking practices.

Purpose: to develop a comprehensive set of dynamic models based on differential equations for quantitative forecasting of regional food security parameters trajectories, and on this basis, to formulate corrective managerial decisions within the framework of agrifood policy. The first part of the study is devoted to formalizing the interrelationships among the indicators of required food production volumes, consumption, and food quality characteristics.

Methods: the research methodology is based on constructing an economic model of regional food security as a set of differential equations used to assess the system's parameters and predict its functioning. The model was validated using data from Krasnoyarsk Krai.

Results: a set of dynamic models of regional food security is presented, incorporating production, consumption and technological factors. The models have been verified against statistical data from Krasnoyarsk Krai and provide quantitative forecasts of changes in security indicators with sufficient accuracy for managerial decision-making. Estimates of production volumes required to meet regional domestic consumption were obtained, along with forecasts of food quality dynamics. Recommendations for the management of the region's agrifood system are formulated.

Conclusions and Relevance: among the threats destabilizing the agrifood system of Krasnoyarsk Krai are the following: the widening gap between actual harvest yields and the required production volumes of vegetables needed to cover current consumption according to nutritional standards, which points to a projected inflection point in the trend toward aligning the dietary structure with rational norms amid rising prices. Similar conclusions were obtained for milk and meat production and consumption, for which the region exhibits a high dependency on imports. These trends are observed against the backdrop of a projected decline in food quality across a number of parameters, indicating a situation worse than the national average. To mitigate the identified risks, adjustments to regional programs are required to expand vegetable production, stimulate dairy and meat livestock farming, and implement a product traceability system.

Purpose: to develop a methodological approach to diagnosing structural and technological gaps in regional specializations based on assessing organic connectivity as a stable and recurring situation of joint localization of industries in the economy of the constituent entities of the Russian Federation.

Methods: the study was conducted using methods of the system and economic-statistical analysis based on data from 82 constituent entities of the Russian Federation for the period 2024–2025. The study was based on 256 groups of economic activities in the context of industries that are jointly localized in the region. The mapping of the regional industry space was conducted using the Force Atlas 2 graph layout algorithm.

Results: a graph of the industrial space of the regions is constructed, the connectivity and mutual arrangement of 256 groups of activities in the economy of the constituent entities of the Russian Federation are determined. The boundaries and composition of organic clusters are defined – fragments of the industrial space that require similar regional conditions and form specific industrial competencies. Using the example of the Chelyabinsk region, a set of organic clusters has been revealed that form the economic specializations and comparative advantages of the region. Technological gaps (activities with a low level of localization) and structural gaps (industries that are not localized in the region) have been identified, generally limiting the industrial self-sufficiency, potential and long-term economic growth of the region.

Conclusions and Relevance: it has been established that the joint localization of industries in the constituent entities of the Russian Federation is not accidental. Within the framework of the formed organic clusters, the low localization of certain activities indicates the presence of technological gaps as a medium-term development reserve, while non-localized activities are considered as structural gaps that need to be neutralized to ensure the self-sufficiency of economic specializations and the growth of the economy's production capabilities in the long term.

Purpose: to develop an adapted approach to the application of CAPM and WACC models in the Russian economy, taking into account sanctions restrictions, volatility of credit rates, and industry specifics.

Methods: the article utilizes comparative analysis of four calculation techniques: the classical method of A. Damodaran with adjustments, a hybrid approach (synthesis of data from the Damodaran and Moscow Exchange websites), a method based on Russian stock indices, and a modification by Fernández. The study relied on statistical data from the Moscow Exchange, TestFirm audit reports, and indicators from the Central Bank of Russia.

Results: the authors have obtained the ranges of return rates on invested capital by industry as of December 31, 2025: 17.11–21.16% for the oil and gas industry, 16.65–19.78% for the electric power industry, 16.54–21.7% for the metallurgical industry, 17.23–20.53% for the construction industry, and 15.06–21% for the financial sector. A theoretical sensitivity analysis of WACC to changes in the cost of debt is performed: an increase in the cost of debt by 8.71% leads to a theoretical change in the return on invested capital of 4.3% for construction (with a debt share of 66%) and 2.2% for telecommunications (with a debt share of 33%), demonstrating significant sectoral differentiation due to the tax shield effect and differences in capital structure. The authors propose an algorithm for replacing critical parameters in models: switching to OFzs as a risk-free asset, calculating the risk premium using industry indices, and using β = 1 in cases of low regression determination.

Conclusions and Relevance: the comparability of the approaches under consideration and the possibility of their joint use for constructing range estimates of the return on capital within the sample have been confirmed. The Friedman test revealed no significant differences between the four methods (χ² = 7.2; p = 0.066), indicating their comparability. Prospects for further research include the creation of a national database of financial parameters and the integration of machine learning for dynamic model calibration.

Purpose: to test the applicability of the methodology for selecting “smart” regional specialization proposed by the authors by identifying, with its help, promising areas of such specialization in the Tambov region within the field of FoodTech.

Methods: alongside general scientific methods of cognition, including analysis and synthesis, abstraction, comparison, and generalization, the study employs a systems approach, as well as methods of content analysis, bibliometrics, and network analysis. Expert interviews and strategic sessions were also conducted. The core research method, which is simultaneously undergoing empirical validation, is the authors’ matrix “Existing Capacities – Potential Impact” (on the region’s sustainable development).

Results: the authors propose a model for selecting priorities of “smart” specialization in the Tambov region, based on the identification of the region’s dominant competencies in the field of FoodTech. Particular attention is paid to assessing FoodTech market niches where the Tambov region is capable of demonstrating sustainable competitive advantages. The study develops a governance framework that ensures coordinated decision-making among public authorities, universities, research centers, and businesses.

Conclusions and Relevance: the application of the authors’ methodology for selecting “smart” specialization to identify and substantiate the development prospects of the Tambov region has demonstrated that the region possesses the potential to form a regional FoodTech hub based on agricultural, biotechnological, and digital competencies. The proposed method makes it possible to identify priority niches of territorial specialization, form a necessary project portfolio, and create a foundation for a systemic approach to managing the region’s innovative development and building an appropriate governance model. This enhances regional innovativeness, increases competitiveness, and contributes to ensuring national food security and technological sovereignty.

Purpose: to assess the factors shaping migration flows in municipalities of the Ural Federal District, Russia, in the context of territorial social transformation. Methods: the empirical panel is based on Rosstat municipal statistics for 2012–2024 and includes 198 municipalities of the Ural Federal District. The variables describe investment activity, employment, wages, social and utility infrastructure, transport connectivity and environmental conditions. A dynamic spatial autoregressive model is used to account persistence and intermunicipal dependence. To select the final specification, four spatial weight matrices were compared: linear distances, normalized linear distances, adjacent boundaries, and normalized adjacent boundaries.

Results: the model indicates persistence in migration growth and a positive spatial lag, meaning that local migration flows are linked to nearby and functionally connected municipalities. Fixed capital investment, average wages and the length of local public roads have positive coefficients. Negative estimates for average employee headcount and the number of places in preschool institutions are interpreted as evidence of municipal heterogeneity: high employment concentration in certain economic sectors and spare capacity in the social infrastructure are not capable of generating population inflow unless accompanied by quality jobs, adequate incomes, transport connectivity, and development prospects.

Conclusions and Relevance: migration flows reflect labour potential, infrastructure pressure and territorial attractiveness, and can therefore indicate municipal social transformation. The results support the hypothesis that migration flows in the Ural Federal District depend on local conditions, previous migration patterns, proximity, investment, wages and road connectivity. Practically that means that policy measures should target groups of connected municipalities rather than isolated territories.

Purpose: to identify configuration of vertical (inter-level) and horizontal (inter-municipal) mechanisms of interaction in the management of agglomeration development, taking into account the stages of the management cycle and ensuring balanced development of the core and periphery.

Methods: systematic, comparative, statistical, institutional analysis, case study, functional typology are used. The object of the study is the Yekaterinburg urban agglomeration, which was chosen due to a long history of inter-municipal cooperation, the heterogeneity of its constituent municipalities and the representativeness of the problems for Russian agglomerations.

Results: it is proved that the Yekaterinburg agglomeration is a heterogeneous territorial and economic system, the management of which is of a hybrid nature. It has been established that horizontal mechanisms dominate at the stages of initiation, examination and approval of decisions, while vertical channels remain crucial at the stages of resource provision and regulatory approval. A functional classification of inter-municipal projects has been developed, which makes it possible to design management decisions in a targeted manner, taking into account the differentiated involvement of municipalities in agglomeration processes.

Conclusions and Relevance: the hybrid management model ensures the promotion of strategic initiatives and coordination of the interests of participants, but contains systemic limitations: dependence on vertical financing in the absence of fixed financial support for inter municipal projects, the asymmetry of power between the core and the periphery, the legal non-requirement of horizontal agreements, as well as the lack of mechanisms for synchronizing interdependent projects and monitoring their implementation. It is established that achieving the development targets of an agglomeration requires its institutional design as a management entity with the definition of functions, sources of financing and decision-making procedures at the inter-municipal level. The prospects for further research include a quantitative assessment of the inter-municipal projects efficiency and a comparative analysis of other Russian agglomerations’ practices.

Purpose: to formalise the conceptual mechanism describing the process of transforming the business models of technology companies in the context of corporate ecosystems formation.

Methods: the research is based on the integration of the theoretical provisions of the business models concept, the resource theory of firms, dynamic capabilities, key competencies, process and value approaches. The methods of theoretical modelling, system, comparative and structural analysis are applied. The trajectories of the transition of domestic technology companies to ecosystem business models are determined using the case study method.

Results: the role of the corporate ecosystem as an organizational environment that facilitates the formation of a network model for creating, delivering, and monetizing shared value is revealed. The results of a comparative analysis of traditional and ecosystem based business models are presented, focusing on the elements of the Business Model Canvas as a tool for assessing a company’s readiness for ecosystem transformations, clarifying the directions and necessary competencies for their implementation. A process-oriented transformation mechanism was formalised, revealing the sequence of integrating resource assets, dynamic capabilities, and interaction with partners of technology companies into new value propositions that ensure business resilience growth and strengthen competitive positions.

Conclusions and Relevance: the ecosystem transformation of business models represents a natural stage in the evolution of modern technology companies, reflecting the shift from the prevailing management of internal resource assets to the coordination of complex network interactions, the integration of distributed flows of data, knowledge, and competencies within corporate ecosystems. It has been proven that the ability to continuously create, update, and restructure internal and external competencies to meet the increasingly complex context of the digital environment is a necessary condition for successful transformation. The research results create a methodological basis for further quantitative verification of the identified relationships and the development of tools for assessing the effectiveness of the ecosystem transformation of technology companies.

Purpose: to explain the need to develop new methodological approaches to the development of civil aviation taking into account the growing utilization of resources, spatial priorities and importance for national economic security.

Methods: interdisciplinary analysis, statistical and empirical studies, results of foreign sociological interviews and expert assessments were used. The study was based on the holistic approach and endogenous methodology. A retrospective analysis of the domestic aviation development made it possible to identify the features of this industry and establish the continuity of individual principles at the present stage.

Results: the development of civil aviation is closely linked to the development of the standardized dual-use technologies. This makes the active role of the state in the development of aviation valuable. National capabilities in the aircraft production “from tail to nose” are becoming a critical requirement for aviation in many countries. In the modern era aviation faces numerous challenges that require a more decisive role for the state, but at the same time, the risks are also increasing. To solve this problem, it is necessary to master a new methodological approach that considers aviation as a subsystem of the national economy, for which the spatial aspect is also important.

Conclusions and Relevance: national industrial policy is a prerequisite for the development of civil aviation. Division of competence between federal and regional governments and business is essential for the successful development of aviation. Along with restoring national capability to produce aircraft “from tail to nose”, plans must be developed for expansion into foreign markets. Of particular importance is the expansion of investment payback horizons as a result of increased economic activity in remote and hard-to-reach regions, an increase in population, as well as the volume of value added created in the regions.