π-Economy

RAR RUS 7-25 Fattakhov Khamit Penzin Konstantin Kalinina Olga olgakalinina@bk.ru Assessing the maturity of artificial intelligence technology application in organizational management This article examines the transformation of corporate governance under the influence of artificial intelligence (AI) technologies. The relevance of the topic is driven by the rapid growth in the adoption of AI-based solutions. At the same time, more than a third of enterprises report significant economic benefits from their use. However, the issue of systematizing the technologies employed in organizational activities, particularly in the management function, remains pressing. The authors propose assessing the maturity of AI technology application in organizational management, thereby enabling the avoidance of typical errors and the development of a coherent AI transformation strategy. Consequently, the aim of the study is defined as follows: to develop a methodological framework for assessing the maturity of AI application in organizational management, including the refinement of key concepts, systematization of approaches to analyzing the impact of AI technologies on the management function, and the creation of practical assessment tools for diagnosing and interpreting maturity levels. To analyze the theoretical and methodological aspects of strategic management in the context of digital transformation, methods of data analysis from open sources, scientific research methods including analysis and synthesis, as well as deduction and generalization were employed. The empirical basis of the study comprises case studies of Russian companies from various industries (manufacturing, logistics, retail) at different stages of AI integration into management processes. The authors have developed approaches to assessing the impact of AI on organizational management, clarified the concept of “maturity” in the application of AI technologies in management, constructed a maturity assessment matrix for the use of AI in organizational management activities, created a checklist for diagnosing the current maturity level, and proposed an interpretation of diagnostic results for the current level of AI maturity in management activities. The matrix includes nine sequential levels: from the local use of open models by employees (“Level 0: Interest”) to the creation of a fully autonomous “captive company” capable of operating and generating revenue without human intervention (“Level 8”). Each level is characterized in terms of the AI technologies and tools employed, architectural approaches (robot-centric, AI-centric, multi-agent architecture), key artifacts, the sequence of tool application, and the transformation of management functions (planning, organization, motivation, control, coordination). Based on an analysis of Russian companies’ case studies, the expected effects of progressing through AI maturity levels are examined, the costs required to achieve each level are estimated, and practical examples are provided. The maturity assessment matrix serves as a tool for diagnosing a company’s current state and formulating its AI transformation strategy, enabling an objective assessment of the company’s status and identification of directions for its AI transformation. Promising research directions include the development of detailed metrics for assessing each maturity level, as well as the study of industry-specific features of applying this matrix. Another important area is the analysis of risks and ethical aspects associated with the operation of high-level autonomous systems. 10.18721/JE.19301 004.8:005.7 artificial intelligence maturity assessment corporate governance digital transformation generative AI multi-agent systems management automation large language models https://economy.spbstu.ru/article/2026.119.1/ 01_fattahov_penzin_kalinina.pdf

RAR RUS 26-38 Shaykhulova Aigul Impact of Industry 4.0 technologies on the production function of an enterprise: econometric assessment and scenarios analysis In the context of the Fourth Industrial Revolution (Industry 4.0), the quantitative assessment of digital technologies’ impact on enterprise economic performance becomes critically important. Traditional production functions, limited to accounting only for labor and capital, cannot fully capture the effects of the introduction of cyber-physical systems, robotics, and artificial intelligence. This study aims to fill this gap by developing and econometrically estimating an extended Cobb–Douglas production function that incorporates a composite Industry 4.0 index. The theoretical framework draws on the works of classical economists and contemporary digital transformation researchers, including concepts of the technological multiplier and the productivity paradox. The methodology is based on constructing a regression model in which the exogenous variables are capital stock, labor input, and a composite Industry 4.0 index. The latter aggregates indicators of robot density, the level of adoption of the Internet of Things, and artificial intelligence technologies. Due to the limited availability of micro-level data on Russian enterprises, the empirical estimation was conducted on a simulated dataset of 150 observations. The data generation parameters – variable distributions and their correlations – were calibrated to match actual statistical data from Rosstat, the OECD, and the International Federation of Robotics for the period 2018–2023. Model parameters were estimated using the least squares method with numerical optimization. The results demonstrate that incorporating the digitalization factor dramatically improves model fit compared to the classical specification that does not account for technology. The estimated output elasticities with respect to capital and labor are statistically significant and consistent with theoretical expectations for a manufacturing production function. The digitalization coefficient reveals that a higher Industry 4.0 technology adoption level leads to a substantial increase in output, holding capital and labor constant. Based on scenario analysis of four digital transformation strategies, the most effective approaches in terms of risk-adjusted trade-off are identified. The findings have practical implications for industrial enterprise management when justifying investment budgets for digital technologies, as well as for public authorities in designing industrial policies aimed at stimulating robotization and increasing labor productivity. Limitations of the study include the aggregated nature of the Industry 4.0 index, which does not account for differentiated effects of individual technologies, and the use of simulated data, which requires further model validation on real panel data. 10.18721/JE.19302 330.43:338.45:004.9 Industry 4.0 Cobb–Douglas production function digital transformation econometric modeling scenario analysis labor productivity robotics simulated data https://economy.spbstu.ru/article/2026.119.2/ 02_shayhulova.pdf

REP RUS 39-52 Chingovo Carlean Performance management frameworks in commercial and smallholder farming (the case of Zimbabwe) Zimbabwe’s economy remains agriculture-based, as it provides the country’s livelihood, employment, and food security. However, the issue of farm productivity and farm sustainability lies in the effectiveness of the performance management frameworks (PMFs) designed and applied within different farming systems. This paper presents literature review on performance management models in the sphere of commercial and smallholder farms in Zimbabwe. Based on empirical research on performance measurement, management practices, institutional structure, and policy instruments, the review compares the nature, drivers, and barriers of PMFs implementation across two systems. The results also reveal that commercial farms, characterized by capital intensity, mechanization and formal management, are better positioned to adopt structured PMFs that align with business and policy objectives. Smallholder farms, on the other hand, rely on informal, advisory-based monitoring of performance which is limited by available assets, access to credit, and existing data collection capacity. The research focuses on evidence gaps concerning farm-level data, which should be integrated by incorporating technical performance indicators into sustainability and viability metrics. The paper concludes with policy and practical recommendations for strengthening farms at both the farm and institutional levels, promoting evidence-based decision-making and agricultural transformation in Zimbabwe. 10.18721/JE.19303 330 performance management framework agricultural efficiency commercial farming smallholder farming Zimbabwe monitoring and evaluation results-based management agricultural policy farm productivity institutional frameworks sustainability https://economy.spbstu.ru/article/2026.119.3/ 03_chingovo.pdf

RAR RUS 53-67 Nurmatov Dilshadbek Employment, wages and social-labor indicators in Uzbekistan’s industry: a statistical assessment The relevance of the study is determined by the fact that industrial modernization in countries with emerging market economies changes not only the technological parameters, but also measurable employment characteristics: the number of jobs, wage levels, the degree of formalization of labor relations, workplace safety and the coverage of collective bargaining agreements. The aim of the article is to provide a statistical assessment of employment, wages, and labor-related social indicators in Uzbekistan’s industry in 2020–2025 in the context of digital modernization. The methodological framework combines comparative, structural-dynamic, and institutional analysis, while the empirical base includes official data from the National Statistics Committee of the Republic of Uzbekistan and the national Sustainable Development Goals monitoring platform. The article examines the dynamics of industrial output, employment, and wages, as well as indicators characterizing selected dimensions of decent work: informal employment, the gender wage gap, occupational injuries, and the prevalence of collective bargaining agreements. The findings show that in 2021–2025, the industry of Uzbekistan remained one of the main drivers of economic growth; however, the positive dynamics of output and wages were not accompanied by a proportional improvement in all labor-related social parameters. Limitations associated with persistent informality, income heterogeneity within industry, and unstable dynamics in occupational safety indicators are identified. The scientific contribution lies in presenting decent work parameters not as a general normative concept but as a system of statistically observable sectoral indicators applied to Uzbekistan’s industry with a focus on the automotive sector. The practical significance of the study consists in its potential use for improving industrial policy, employment policy and sectoral monitoring of job quality. It is concluded that industrial policy needs to be combined with measures to formalize employment, reduce occupational risks, and develop sectoral statistical tools for assessing job quality. 10.18721/JE.19304 330 industrial employment wages social-labor indicators decent work industry digital modernization job quality automotive industry Uzbekistan https://economy.spbstu.ru/article/2026.119.4/ 04_nurmatov.pdf

RAR RUS 68-82 Dinmukhametova Aliya Badykova Idelya idelia.badykova@gmail.com Spatial asymmetry of Russian regions in the context of technological change The increasing influence of technological change intensifies the historically established spatial differentiation of socio-economic development. Digital transformation, which has permeated virtually all spheres of life, is changing traditional understandings of differentiation and creating a situation in which lagging regions can either reduce existing imbalances or exacerbate them. Along with traditional factors, scientific and technological development acts as a key determinant of regional development. However, the effect of this factor is uneven: regions initially in a more advantageous position have a greater chance of strengthening their standing. This creates the risk of deepening spatial asymmetries, making it urgent to develop tools for monitoring the state of heterogeneity in the context of scientific and technological development. The aim of this study is to develop and test a methodological approach to quantitatively assessing the differentiation of territories by level of socio-economic development based on a unified system of indicators. The empirical basis of the study consists of data from regions of the Volga Federal District (VFD). The methodological framework includes a combination of comparative and spatial-temporal analysis methods, as well as an index approach using a taxonomic principle for weighting indicators. Within the framework of the study, a system for assessing spatial differentiation has been developed across four dimensions: natural resource potential, standard of living, economic indicators, and digital transformation. The use of weighting coefficients allowed for more accurate results by taking into account the contribution of each dimension to the integrated index. The data obtained indicate significant asymmetry in the level of spatial development of the regions of the VFD. The practical significance of the developed methodological approach lies in the fact that the obtained results enable regional governments to formulate strategic spatial development decisions that are adapted to local specificities and are targeted in nature. Expanding both the range of indicators and the time horizon for assessment is a promising direction for further research. This will improve the accuracy of measurements and make it possible to track the dynamics of the obtained results. 10.18721/JE.19305 332.1 scientific and technological development digital asymmetry regional ecosystems socio-economic differentiation Volga Federal District https://economy.spbstu.ru/article/2026.119.5/ 05_dinmuhametova_badikova.pdf

RAR RUS 83-99 Vasiliev Mikhail Assessment of the economic effects of structural changes in the manufacturing industry of Russia The article examines methodological issues of assessing the economic effects of structural changes in the manufacturing industry. The aim of the study is to assess the economic effects of structural changes in the manufacturing industry of the Russian Federation in 2020–2024, namely, to evaluate the contribution of the redistribution of average headcount of employees across technological groups of the manufacturing industry to the change in labor productivity. The methodological basis of the study is the works of S. Fabricant, B. Massell, M. Syrquin, M.P. Timmer and A. Szirmai, as well as studies on structural transformation and technological dynamics in industry. The scientific novelty lies in the modification of methods for analyzing structural changes in industry and adapting the shift-share approach to the assessment of the economic effects of structural changes in the average headcount of employees across technological groups of manufacturing industries. The author modifies the methodology of Timmer and Szirmai, in which the change in productivity is decomposed into the within-sector effect, the static structural effect, and the dynamic interaction effect. The difference of the modified methodology lies in applying this decomposition logic to the assessment of structural changes in the manufacturing industry, taking into account the grouping of its industries according to OECD technological groups. Another difference is the choice of time intervals for the calculation. In the Timmer and Szirmai study, labor productivity decomposition is carried out over large intervals, ranging from five to ten years depending on the country and period. This article uses a chain annual calculation for 2020–2024 over the intervals 2020–2021, 2021–2022, 2022–2023, and 2023–2024. This allows tracking the change in the contribution of structural redistribution not only over the entire period, but also over individual years. The empirical base is formed from Rosstat data for 24 types of manufacturing activities, aggregated into four OECD technological groups. It is shown that the structure of the average headcount of employees by technological group changed moderately in 2020–2024. The share of high-technology industries increased, while the share of low-technology industries decreased. The structure of the volume of shipped goods differs significantly from the employment structure: the largest share of shipment volume is accounted for by medium-low- technology industries. Labor productivity, measured as the volume of shipped goods per employee, increased across all technological groups. It was established that the increase in labor productivity was mainly driven by the within-sector effect calculated at the level of technological groups. The static structural effect at the level of technological groups was positive but significantly smaller than the within-sector component, while the dynamic interaction effect was minimal. 10.18721/JE.19306 338.2 manufacturing industry structural changes economic effect labor productivity shift-share analysis Russian industry https://economy.spbstu.ru/article/2026.119.6/ 06_vasilev.pdf

RAR RUS 100-112 Gus’kova Nadezhda Babkin Ivan babkinivan@spbstu.ru Soldatova Elena The ESG concept in the sustainable development agenda: features and key research vectors Unprecedented sanctions pressure, the transformation of global market structures, and the need to implement technological sovereignty strategies necessitate a rethinking of current ESG approaches in the context of sustainable development. Despite the dynamic development of the ESG agenda at the global and national levels, the scientific community still lacks a unified position on the essence of the concept and an agreed-upon methodology for its implementation, which underlies the high scientific and practical significance of this study. The aim of this study is to systematize the key trends and challenges of the sustainable development agenda, identify the prerequisites for the formation and analysis of the ESG concept, and identify promising areas for research. The study utilized methods of systemic analysis and synthesis of scientific literature, content analysis of regulatory and strategic documents, a comparative analysis of global and national ESG initiatives, and a synthesis of academic and applied research on sustainable development. The prerequisites for the development of the ESG concept are systematized, and the evolution of its understanding is traced – from investment philosophy to strategic management imperative. It is revealed that the current status of ESG research is characterized by fragmentation and immaturity of both the basic concept and individual areas of its development: ESG rating methodology, ESG data disclosure standards, and ESG investment performance assessment. A multi-level system of factors determining interest in the ESG agenda in Russia is established, including the formation of a national ESG infrastructure and deeper integration within the EAEU, SCO, and BRICS. The results can be used by researchers, regulators, and corporate practitioners in developing local ESG strategies, rating methodology, and ESG information disclosure standards. The scientific community lacks a unified position on either the essence of the ESG concept or individual areas of its development, which is explained by the “immaturity” of its fundamental basis. Directions for further research include refining the ESG categorization framework, improving the methodology for rating and assessing the effectiveness of ESG investments, and developing local ESG approaches, standards, and practices taking into account national specifics. 10.18721/JE.19307 330 agenda concept trends sustainable development ESG research transformation https://economy.spbstu.ru/article/2026.119.7/ 07_salimova_babkin_soldatova.pdf

RAR RUS 113-127 Efanov Vladislav Shlyahov Andrey Yuriev Nikita Operational model of labor productivity in the management of distributed engineering infrastructure Under conditions of limited opportunities for extensive infrastructure renewal, the impor-tance of labor productivity in contemporary service-industrial and manufacturing enterprises that ensure the functioning of a geographically distributed network of engineering facilities is increasing. The system of business processes of the economic activities of such enterprises constitutes the object of the present study. For such enterprises, labor productivity depends not only on the number of employees and the total volume of work performed, but also on the structure of tasks, planning density, intensity of site visits, and personnel travel time. This determines the need to search for models for improving labor productivity that take into account measurable characteristics of business processes. The aim of the study is to develop an operational model of labor productivity for these enterprises that represents this indicator as an analytically interpretable dependence on measurable characteristics of business processes, as well as to formulate and substantiate directions for optimizing economic activity. The research methodology is based on the operationalization of labor productivity through parameters that reflect the organization of work and working time costs. The empirical basis of the study is a cross-section of operational data from a large Russian service-industrial enterprise, Russian Television and Radio Broadcasting Network, for 2025, including indicators for engineering facilities, maintenance units, and territorial branches, as well as derived indicators calculated on their basis. The study identifies controllable factors and diagnostic indicators that characterize the structure of work, frequency and intensity of site visits, planning density, and personnel travel time. The scientific novelty of the study lies in transforming labor productivity from an aggregated reporting indicator into an operationally decomposable value linked to observable process characteristics. The results of the empirical testing show that the parameters of the proposed model are represented in operational data, can be calculated, and make it possible to interpret differences in labor productivity between comparable facilities and units. The practical value of the study lies in the possibility of using the model to identify organizational reasons for differences in labor costs and to justify directions for increasing labor productivity without reducing the analysis to universal standards. The study concludes that the proposed model is applicable for the analytical description of labor productivity using data from a large enterprise. The limitation of the study is related to the use of cross-sectional data for a single period. Further research should focus on the use of panel data, testing the stability of the identified relationships over time, and clarifying the boundaries of applicability of the model for different types of enterprises. 10.18721/JE.19308 65.011.46 business process management labor productivity infrastructure maintenance processes operational model maintenance planning Cobb–Douglas production function https://economy.spbstu.ru/article/2026.119.8/ 08_efanov_shlyahov_yurev.pdf

RAR RUS 128-147 Yashin Sergey jashin@52.ru Shibanov Kirill Assessing the quality of event data in digital traces of end-to-end organizational and management processes The article examines the methodological challenge that arises when organizations transition from “management by reports” to management based on digital traces of end-to-end (cross-functional and intersystem) processes. It demonstrates that the abundance of event data (ERP/CRM/Service Desk logs, integration and user logs, equipment transactions etc.) does not in itself guarantee its suitability for management decisions: systematic measurement flaws – incomplete logging, discontinuities in the end-to-end identity of a case, inconsistency in status semantics, low temporal resolution, and definition drift after releases – can lead not just to “noise” but to persistently biased management conclusions. Particular emphasis is placed on the fact that event data quality requirements differ for two modes of use: descriptive-control (process reconstruction, KPIs, deviation monitoring, process mining) and causal-explanatory (assessing the effect of management interventions based on observational data). The methodological framework of the study is constructed as a synthesis of process mining, data quality management standards and practices, and causal inference theory (potential outcomes, causal graphs, quasi-experimental designs). A two-loop model of event data quality is introduced: “measurement consistency” (how accurately the data reflect the trajectory of a case over time) and “causal suitability” (the extent to which the data allow for the unambiguous determination of an intervention, outcome, time windows, and the validity of identification assumptions). An operationalization of causal suitability is proposed as a set of indices (observation unit integrity, temporal suitability, meaning stability, intervention observability, context sufficiency for confounding control, and resource environment observability) linking event log defects to the risk of false effect estimates. The need for accounting metadata (status and rule versioning, time stamping, and change traceability) is further substantiated, as is the need for process-semantic constraints (process axioms) as a quality control mechanism critical specifically for causal inference. The practical result is a framework for organizational event data quality management (event log passport, intervention registry, meaning versioning, and a quality index panel), enabling the proactive determination of which management decisions and causal assessments are, in principle, permissible based on existing digital traces. 10.18721/JE.19309 004.6:005.311.6 digital traces end-to-end processes event data event log data quality process mining management decisions causal inference https://economy.spbstu.ru/article/2026.119.9/ 09_yashin_shibanov.pdf

RAR RUS 148-163 Danilenko Kirill A mathematical model of temporal dynamics in adaptive economic systems based on sheaf theory Coordinating distributed agents in digital platforms and ecosystems remains an open theoretical problem: existing models – from general equilibrium to system dynamics – provide no rigorous criterion for determining under what conditions locally consistent agent strategies cohere into a unified global trajectory and when they do not. Traditional platform theory, in turn, focuses on the structure of market interactions but does not offer tools for diagnosing the temporal gaps that accumulate as agents adapt to a changing environment at different rates. This paper addresses both deficiencies by developing a mathematical model of temporal consistency and inconsistency based on the apparatus of sheaf theory. An adaptive economic system is modeled as a topological space in which open sets correspond to local markets or agent groups, and a sheaf assigns feasible strategic configurations to each point of the system. The first cohomology group serves as a quantitative measure of coordination gaps, while the rate of their resolution is linked to the spectral properties of the sheaf Laplacian. A key methodological contribution is the use of the orthogonal Procrustes method to calibrate restriction maps directly from observed agent data, translating the theoretical construct into a practically applicable diagnostic tool. The model is validated on operational data from a real digital platform covering the second half of 2025: 26 weekly observations across three agents – a supplier, a platform operator, and a logistics partner. Diagnostics reveal four distinct phases of the misalignment life cycle; angular misalignment measures between agents (ranging from 10.9° to 42.6°) are interpreted as cognitive gaps – a quantitative indication of how differently participants construct a shared strategic space even under formally aligned objectives. Spectral analysis of the sheaf Laplacian demonstrates that, given the current interaction architecture, global agent coordination is topologically unattainable (H0 (G; F) = 0), with a characteristic recovery horizon of approximately 320 weeks; a threefold increase in misalignment energy under an exogenous shock confirms the diagnostic sensitivity of the model. The scientific contribution lies in a mathematical model that combines sheaf-cohomological diagnostics of temporal gaps with empirical calibration of restriction maps using the Procrustes method. The findings provide a foundation for developing monitoring and adaptive governance systems for digital platforms and multi-agent economic ecosystems. 10.18721/JE.19310 519.86; 512.7; 330.4 sheaf theory temporal dynamics adaptive economic system sheaf cohomology sheaf Laplacian digital platform https://economy.spbstu.ru/article/2026.119.10/ 10_danilenko.pdf

RAR RUS 164-180 Lechshenko Kirill Popova Elena Hybrid model for predicting the regional digitalization index in a turbulent economy The unevenness of digital development across Russian regions increases the demand for forecasting tools that are suitable for managerial decision-making while preserving an economically interpretable logic of influences. Composite digitalization indicators are convenient for monitoring; however, their dynamics are sensitive to structural shifts and regime changes. This reduces the reliability of simple trend-based approaches and complicates the use of opaque models in scenario analysis. The aim of this study is to develop a hybrid forecasting model for a regional composite digitalization index, that is robust under unstable dynamics and provides an interpretable representation of factor effects. The empirical basis is a region–year data mart constructed by the authors, combining observed values of the digitalization index with comparable socio-economic indicators from official statistics. The proposed methodology relies on a two-component architecture. Nonlinear dependencies of factors are specified by an interpretable layer of Kolmogorov–Arnold networks, where the influence of each feature is represented by a smooth univariate function parameterized by splines. The dynamic component is implemented as a recurrent module with chaotic modulation, designed to account for inertia and accelerated reorganization of the model's internal state during regime changes. To prevent over-complexification, the authors introduce a complexity selection procedure based on a trade-off between forecast accuracy on held-out partitions and the smoothness of the influence functions. The validity of the findings is confirmed by comparison with baseline econometric and neural network solutions and ablation experiments that separate the contribution of interpretable nonlinearity from that of the dynamic mechanism. The results indicate that selecting a minimally sufficient complexity level keeps predictive accuracy close to the best configurations by metrics, while preserving stable, economically readable form of the influence functions. Interpretation of the edge functions reveals nonlinear effects, including saturation of the influence of economic scale and a heterogeneous response of the index to labor market conditions. The practical value of the study lies in the possibility of using the model as an analytical module for monitoring and scenario-based decision support in regional digital policy, since forecasts are accompanied by a transparent structure of factor influences. 10.18721/JE.19311 330.4:338.24:007.25 digitalization index forecasting neural networks interpretable models structural changes https://economy.spbstu.ru/article/2026.119.11/ 11_leshchenko_popova.pdf