The transition to a supply-side economy requires a qualitative transformation of the industrial growth management system. The aim of the study is to identify and systematize gaps — including deficiencies, discontinuities, and inconsistencies — in government regulation that impede balanced industrial development in Russia. The methodological framework comprises a systems approach and institutional analysis. The empirical basis consists of data from the Federal State Statistics Service (Rosstat), analytical materials of the National Research University Higher School of Economics, and regulatory and legal acts issued between 2008 and 2026. The authors demonstrate that the key imbalances in the management of industrial growth — low innovation activity, the concentration of innovation potential in the research sector, and critical dependence on imported equipment — result from institutional, coordination, and regulatory-target- setting gaps. The scientific novelty of the study lies in the systematization of these gaps in the context of the transition to a supply-side economy. The findings may be useful both to public authorities in adjusting industrial and science and technology policies and to researchers whose academic interests focus on industrial development management.

The relevance of the study is determined by the key role of data in public administration amid digital transformation. The emergence of a new data-driven governance paradigm is outpacing the development of an appropriate theoretical and methodological framework, creating a need for comprehensive scholarly understanding of this issue. The aim of the article is to develop the conceptual foundations of data-driven governance. The scientific novelty lies in developing a classification of four approaches — evidence- based policymaking, indicative governance, algorithmic governance, and data-driven performance management; identifying systemic risks and limitations of applicability; and substantiating the need for an evolutionary synthesis of these approaches that takes into account national and cultural contexts. In addition, the study provides a terminological distinction between data-driven governance, data governance, and data-centric governance, thereby refining the conceptual framework of the field. The methodological framework comprises systems-structural and institutional analysis, as well as methods of classification and typologization. The article discusses conceptual approaches to differentiating data-driven governance instruments and demonstrates their implementation in the practices of several countries, including the United States, the United Kingdom, the Netherlands, China, the United Arab Emirates, and Russia. The findings open up prospects for further empirical research aimed at assessing the maturity of data-driven governance.

The article addresses the effectiveness of institutions of technological renewal in an environment where conventional solutions fail to produce the expected outcomes due to a substantial shortening of technology life cycles. It is demonstrated that organizational inertia is a significant factor affecting the successful functioning of such institutions. At the same time, institutions established to overcome inertia within national innovation systems themselves gradually become carriers of this inertia. The research methodology is focused on providing a theoretical explanation and qualitative generalization of known failures in the implementation of institutional solutions aimed at promoting innovation activity. Within the identified problem context, the study establishes that the existing national technology policies of virtually all major technological powers fail to provide sufficient protection against the adverse effects of organizational inertia. This supports the hypothesis that organizational inertia should be managed at the level of the institutions themselves. The findings define the role of organizational inertia and its implications for institutions of technological renewal. The study also demonstrates the impact of the integration of such institutions into the institutional matrix through mechanisms including complementarity, isomorphism, and embeddedness. An algorithm for managing organizational inertia is developed, and conclusions are drawn regarding the need to account for its manifestations in the activities of institutions of technological renewal regardless of the prevailing institutional matrix.

This article examines the creation of conditions conducive to engaging the management of Russian organizations in greenhouse gas emission reduction (decarbonization) processes. The study aims to investigate the application of the decarbonization method by companies as a means of enhancing the effectiveness of strategic management. The methodological framework is based on ESG principles, while the information base comprises data published by the United Nations Intergovernmental Panel on Climate Change (IPCC) for the period 2021–2024, climate-related information disclosed in corporate annual reports, and recent publications by Russian researchers. The study employs general scientific methods, including expert assessment, analytical review, qualitative analysis, generalization, and benchmarking. As a result, a practical toolkit for the targeted management of decarbonization processes has been developed. The proposed approach may assist the management of Russian enterprises in redesigning business processes to improve the effectiveness of managerial decision-making and strategic management.

This study examines the digital transformation of tax administration and the innovative tax management and compliance technologies implemented by the Federal Tax Service of the Russian Federation (FTS of Russia). The research aims to identify the priority measures required to enhance the effectiveness of tax policy pursued by both public authorities and business entities under conditions of an increasing tax burden while maintaining a balanced relationship between tax authorities and taxpayers. The study identifies the key trends shaping the evolution of the tax administration ecosystem, including emerging models of interaction between taxpayers and tax authorities, as well as the digital technologies and analytical tools employed to achieve fiscal objectives while fostering a competitive, trust-based environment and reducing taxpayers’ administrative compliance costs. The findings indicate that improving the quality of interaction between participants in tax relations has become a central element of contemporary tax administration, complementing its primary objective of increasing tax revenues within the national budgetary system. Advanced digital technologies constitute the principal driver of this transformation by strengthening both the analytical capabilities and procedural efficiency of tax control. The study substantiates the current directions of tax administration ecosystem development aimed at creating a transparent, digitally enabled, and taxpayer-oriented environment that promotes voluntary compliance and cooperative relationships between tax authorities and taxpayers. The findings contribute to the understanding of the ongoing modernization of tax administration and are of practical relevance to policymakers, business practitioners, and professional tax advisers.

State Integrated Information System (GIIS) Electronic Budget, is primarily oriented toward fiscal control and does not provide the information base required for strategic management purposes. The key reasons for the insufficiency of available information lie both in the technical characteristics of data transmission channels and in the legally established set of indicators, which comprises exclusively financial parameters and fails to capture significant non-financial aspects of the activities of economic entities. For systemically important institutions, the study identifies incompleteness of publicly available information, while for strategic institutions, there is a lack of regulations governing the transmission of restricted information. The aim of the study is to assess the adequacy of current processes for collecting and consolidating reporting information for the purpose of developing a digital profile of strategic and systemically important institutions. Through a comparative analysis of the indicators disclosed by these organizations, the study demonstrates that non-financial information essential for strategic management — including workforce structure, equipment utilization, and phased R&D results — is not available from public sources but is maintained in specialized local accounting systems. At present, these systems are not integrated with the GIIS Electronic Budget, resulting in a limited scope of publicly disclosed information. The findings of the study are relevant to economic entities in making management decisions, professionals in the field of public-sector digitalization, and all stakeholders interested in accessing financial and non-financial information on the activities of these institutions.

The aim of the study was to develop an approach to aligning sectoral competitive advantages with targeted tax incentives designed to stimulate innovation activity and enhance the manageability of government support. The methodological framework comprised systems, structural-functional, classification, comparative, economic-statistical, and scenario-based approaches. The authors propose a criterion of applicative targeting, which makes it possible to identify four functional areas of innovation activity: innovation generation, production implementation, applied integration, and infrastructure dissemination. A matrix for aligning sectoral competitive advantages with tax incentives is substantiated, while elements of a practical assessment of the budgetary effectiveness of support measures and an information and analytical monitoring framework are presented. The study concludes that the effectiveness of tax incentives for innovation-driven technological growth is determined not by the scale of the benefits provided, but by the quality of their sectoral targeting, the measurability of outcomes, and the availability of a management mechanism for subsequent adjustment. The relevance of the study stems from the need to shift from the universal provision of tax benefits toward a model in which a tax incentive is aligned with a sector-specific function within the innovation cycle, the expected technological outcome, and the possibility of subsequent monitoring. The findings may be useful to public authorities in designing and adjusting tax incentive measures, as well as to organizations involved in the innovation-driven technological development of economic sectors when assessing the applicability of relevant support instruments.

The purpose of this study is to develop a model for designing an intelligent decision support system (IDSS) for enterprise management. The paper examines how artificial intelligence (AI)-based decision support systems can be integrated with specific enterprise management scenarios to facilitate intelligent managerial decision-making and optimize resource allocation in the context of the development of mobile Internet technologies, edge and cloud computing, and 5G networks. Based on an analysis of the limitations of traditional enterprise resource planning systems, the study proposes a paradigm for their deep integration with AI technologies in complex management environments. An intelligent decision support system equipped with cognitive computing capabilities is developed, and its architecture, functions, algorithms, and impact on enterprise management are examined. The findings demonstrate that the proposed approach improves forecasting accuracy, reduces inventory levels, and shortens managerial decision-making time. The proposed methodology can be applied by managers and specialists in manufacturing, trade, and logistics enterprises, as well as by developers of enterprise management systems.

Artificial intelligence is transforming the global economy. Although AI startups currently operating in the market demonstrate significant potential, they often face substantial challenges, including difficulties in survival, technology implementation, and scaling. Existing research frequently fails to fully capture the unique systemic characteristics of AI startups and lacks an analytical framework for the structured comparison of different business model prototypes. The aim of this study is to develop a preliminary systemic framework for analyzing business model innovation in AI startups, thereby addressing these theoretical gaps. The research methodology employs a multi-stage qualitative design combining a systematic literature review and case studies. The results of the literature review identify five core business model prototypes: technology-driven, product-centric, service-customized, platform ecosystem, and data-centric. Furthermore, in-depth interviews with two Chinese AI startups reveal that business models are not static but evolve throughout the stages of organizational development, demonstrating composability and adaptability. Based on evidence from both the literature and case studies, this study develops a preliminary systemic mind map framework that categorizes the five prototypes into four higher-order system clusters: product, customer, financial, and environmental. This framework provides a structured representation of differences in business model element configuration. The main contribution of this study is the development of a systematic visualization-based analytical method that enables researchers and practitioners to move beyond traditional static classifications and better understand the mechanisms underlying the combination and evolution of internal business model elements from a more comprehensive perspective.

Tender procurement distributes public and corporate orders and affects markets and sectoral development. Its growing scale and requirements increase suppliers’ information burden when selecting relevant tenders. Despite the development of electronic platforms and integration with government systems, a gap remains between operational automation and intelligent decision support. This study aims to identify constraints on the digital transformation of tender systems and problems in assessing procedure relevance, and to develop a conceptual approach to intellectualizing tender specialists’ work. It compares national models of major economies and Russian digital solutions: aggregators, counterparty analysis services, and intelligent tools. These solutions optimize individual stages but do not provide a comprehensive multi-criteria relevance model that accounts for strategic goals, resources, and the economic feasibility of participation. A conceptual model of a tender specialist’s digital twin is proposed, representing procurement selection as a sequence of interrelated functions. The results substantiate the need to transition from procedural digitalization to the intellectualization of the management framework and indicate a direction for further research. They may be useful to developers of tender aggregators and corporate decision support systems.

The aim of the article is to develop a methodology for constructing a knowledge base of deep learning models for cybersecurity experts as an element of an organizational risk management system. The proposed methodology and prototype are designed for integration into an organizational risk management system to improve the quality and timeliness of decision-making. They are designed to enable real-time updating of the knowledge base through the automated parsing of up-to-date data from external sources and the organization’s internal sources, followed by the automated annotation, indexing, categorization, and classification of documents and the knowledge contained therein. Scientific relevance. The proposed approach is based on the use of state-of-the-art deep learning models and provides qualitatively new capabilities for the rapid retrieval of knowledge within a risk management system and for information support across virtually all stages of the process life cycle.

The theory of weak signals, originally proposed by Igor Ansoff, has been widely applied to corporate strategy development. However, its application to the prediction and prevention of organizational crises has received considerably less scholarly attention. This gap is particularly significant for diversified industrial organizations (DIOs), where a crisis originating in one business unit may cascade throughout the organization via financial, operational, and reputational channels. The study pursued two objectives. First, it sought to conceptually distinguish the use of weak signals in strategic management from their application to crisis prediction, highlighting the specific characteristics of the latter in the context of diversified industrial organizations. Second, it empirically tested this conceptual framework by examining the geopolitical events of 2022 and their long-term consequences, analyzing how diversified industrial organizations identified — or failed to identify — weak signals of an impending crisis and how these differences affected their financial performance and recovery trajectories. The study draws upon publicly available corporate annual reports, quarterly investor reports, and analytical publications. The findings demonstrate that organizations capable of recognizing weak signals and proactively developing response strategies restored their market positions within 6–18 months, whereas less prepared competitors required 24–48 months to achieve comparable recovery. Based on these findings, the paper proposes an integrated early crisis warning system for diversified industrial organizations that combines weak-signal monitoring, organizational attention theory, and the analysis of cascading crisis mechanisms. The proposed framework extends existing approaches to preventive crisis management and provides practical guidance for enhancing organizational resilience under conditions of high uncertainty. The findings may also be useful in the education and training of economics students, particularly in courses on industrial economics, strategic management, and innovation management.

The article examines corporate biodiversity conservation activities as an important area of sustainable development and the ESG agenda. The relevance of the topic is attributable to the fact that biodiversity loss risks are ranked among the ten most significant global risks according to the World Economic Forum Global Risks Report 2026 and are also recognized as one of the ten key areas of the global sustainable development (ESG) agenda. The aim of the study is to analyze the tools employed by Russian companies to conserve biodiversity. Particular attention is paid to the types of non-financial reporting through which corporations disclose their biodiversity-related activities, including GRI, TNFD, and CDP frameworks. The study defines the concept of impact management and systematizes biodiversity conservation tools, which are evolving from a peripheral component of corporate environmental responsibility into an independent area within the ESG agenda. The practices of PJSC Severstal and PJSC Polyus are examined in detail as case studies, as both companies are leaders in the RAEX ESG ranking and in the ranking of Russia’s largest mining companies in biodiversity conservation compiled by the Nature and People Foundation. The article identifies the lack of methodologies for assessing ecosystem services and biodiversity at the corporate level as a key challenge. The findings may be of practical interest to directors responsible for strategy and sustainable development, ESG professionals, and researchers from various fields focusing on sustainable development and the impact of business activities on nature.

This article examines the management of strategic human resource risks arising from employee burnout and technostress in companies operating within high-tech sectors of the economy. The relevance of this study is driven by the growing vulnerability of human capital — serving as the primary asset of these industries — under conditions of digital transformation and increasing technological complexity. The aim of the study is to conduct a comprehensive analysis of these risks and to provide a quantitative assessment of their impact on key business performance indicators. The authors employ a methodology based on the synthesis of international and Russian empirical research, as well as systemic and sociotechnical approaches. Particular attention is paid to the systemic integration of human resource risk analysis and the sociotechnical redesign of the working environment, which enables a shift from problem description to quantitative substantiation of economic consequences and the development of integrated managerial solutions. The obtained results demonstrate the significant negative impact of factors such as sudden burnout of key specialists, chronic team overload, and hidden losses associated with presenteeism. The study proposes and substantiates a new managerial paradigm: a transition from isolated corporate interventions to a holistic sociotechnical redesign of work systems. The practical significance of the research lies in the development of integrated management solutions and a system of monitoring metrics aimed at enhancing operational resilience and preserving competitive advantage in high-tech industries.

The paper substantiates the use of process-based management of employee development, which enables HR professionals not only to ensure the effectiveness of such activities but also to prevent potential errors in addressing key tasks related to employees’ career advancement, retention of highly qualified professionals within the organization, and the creation of conditions conducive to the fullest realization of their intellectual and creative potential. The aim of the study is to analyze one of the subprocesses within the employee development management system, namely, employee personal development. Using structural and systems analysis, the author substantiates the applicability of this approach, formulates the parameters defining the normative state of the attributes of this subprocess, and presents a mechanism for applying its normative model using the example of a specific organization engaged in financial and economic activities in the construction sector in the Samara Region. The study identifies problems in the existing subprocess of managing employee personal development and develops recommendations for addressing them. The practical significance of the study lies in the fact that the findings of the analysis can enable the organization to improve both the quality and effectiveness of employee personal development management and the formation of a cohesive team of highly qualified professionals who share common goals, are capable of responding promptly to changes in the organization’s external and internal environment, and can contribute to its innovative development and competitiveness through the effective resolution of current and strategic objectives.

In the innovation-driven economy, universities are increasingly expected to serve as drivers of technological development and system-forming centres of innovation activity. However, the traditional architecture of university governance has become too conservative to fulfil this role effectively. The aim of this study is to develop a governance model of the research and educational corporation as an organisationally integrated system in which research, education, and innovation-driven entrepreneurial activities are combined into a unified resource reproduction cycle through integrated corporate governance mechanisms. The study identifies the key components of this governance model, including a Strategic Innovation Committee; an internal venture fund and royalty distribution model; a Partnership Management Office operating as a one-stop interface for external stakeholders; a digital platform for end-to-end project management; staff rotation mechanisms; and an internal reinvestment system for revenues generated through the commercialisation of intellectual property. The study demonstrates that the transition to a regime of managed variability, characterised by the absence of persistent dominance of any single functional subsystem, constitutes a necessary condition for enabling the university to perform the role of an ecosystem hub. The methodological framework is based on the ecosystem approach to analysing the role of universities, the concept of the entrepreneurial university, and institutional and governance analysis of internal university management architectures. The research also employs conceptual modelling, structural-functional analysis, and comparative analysis of governance practices. As a practical outcome, the study proposes a system of performance indicators for assessing the effectiveness of a research and educational corporation and supporting evidence-based managerial decision-making. The findings may be of practical value to university leadership (including rectors and vice-rectors responsible for strategic development and innovation), as well as to development institutions and public authorities responsible for science, higher education, and innovation policy.

The aim of this study is to develop a concept of a management meta-model for the geodesy, cartography, and spatial data (GCS) sector based on the principles of an adaptive ecosystem and designed to overcome systemic fragmentation, accelerate innovation adoption, and create the conditions for technological sovereignty. The study substantiates the need to move away from a departmental and technocratic approach toward a flexible system capable of responding to technological challenges and market changes and structuring the sector across three meta-levels: rules and regulations, services and aggregators, and consumers and users. The article provides a detailed analysis of the functions, key institutions, and technological concepts associated with each level and demonstrates their interrelationships through flows of data, requests, and regulatory mechanisms that form a self-adjusting feedback loop within the sector. Particular attention is paid to feedback mechanisms and the role of public–private partnerships. Applying a metamodel within the framework of strategic planning through 2050 could address existing challenges by enabling the development of a dynamic market for spatial data and services and, ultimately, creating the conditions to strengthen technological sovereignty and enhance the competitiveness of the Russian economy. The findings may be used in the development of strategic documents, regulatory and legal acts, and platform-based solutions in the field of geospatial technologies.

The relevance of developing a framework for ensuring corporate viability stems from increasing environmental uncertainty and the resulting substantial reduction in corporate longevity. The study aims to formulate the key principles of an ecological concept of organizational viability. The analysis employs methods of comparison, classification, and synthesis of findings reported in the academic literature. Metaphorical and analogical reasoning, together with an axiomatic approach, is used to develop the authors’ framework for studying corporate viability. The study systematizes the definitions of “corporate viability” (“organizational viability,” “enterprise viability”) established in the academic literature and distinguishes viability from the semantically related concepts of hardiness, survival, adaptation, and adaptiveness. The analysis reveals that the theoretical foundations for ensuring corporate viability remain underdeveloped, as evidenced by the lack of a clear understanding of its constituent elements, an unsettled conceptual framework, and the absence of a coherent theoretical basis, specific strategies, and models for ensuring viability. The study substantiates the feasibility of developing a concept of corporate viability based on regularities identified in living systems. An ecological approach to the study of viability is proposed, together with an original approach to ensuring viability through the development and transformation of formalized behavioral models. The authors formulate a set of substantiated principles and key propositions underlying their concept of corporate viability and identify priorities for its further development. The findings may serve as a practical guide for organizations seeking to identify and develop their own behavioral models for maintaining viability and may also provide a basis for further academic research.

Purpose of the study: this study aims to develop a systemic understanding of the research process in the history of management thought (HMT). The article identifies the distinctive features of this process, formulates epistemological tasks for achieving the goals of HMT research, identifies the sources of factors influencing the subject matter of HMT research, and explicates the content of the principles of HMT research and their interaction throughout the research process. Methodology: the methodological basis of the study comprises works on the subject published by Russian scholars between 1985 and 2024. Originality and contribution: the article develops an original systemic view of HMT research methodology in the form of a framework that conceptualizes the research process as the performance of epistemological tasks grounded in the principles of HMT research. This framework can be applied in both research and teaching practice.