Впровадження цифрових технологій у стратегію розвитку організацій поствоєнного періоду: новий вектор антикризового управління

The article examines the role of digitalization of healthcare management processes as a tool for increasing the efficiency of the system's functioning in the context of military challenges and post-war reconstruction of Ukraine. The relevance of the study is due to the need to modernize public management mechanisms in the healthcare sector, increase the efficiency of resource use, and ensure the sustainability of the medical system in the context of large-scale transformations. Modern scientific approaches to interpreting the concepts of digitalization and digital transformation in public management, as well as their application in the healthcare sector, are analyzed. It is determined that digitalization of management processes involves the integration of information and communication technologies into the management system, which contributes to increased transparency, efficiency of management decision-making, improved interinstitutional interaction, and more efficient use of financial, human, and material and technical resources. Particular attention is paid to the analysis of digital tools for managing the healthcare system, in particular electronic medical records, telemedicine, big data analytics and artificial intelligence technologies, which create opportunities for optimizing resource management, monitoring the state of medical infrastructure and forecasting the population's needs for medical services. It is substantiated that the use of digital platforms and integrated information systems contributes to the formation of a single information space for the healthcare system, which allows increasing management efficiency and ensuring better provision of medical services. A conceptual model for digitalizing healthcare system management is proposed, which involves the integration of digital tools into the public management system at the strategic, managerial and operational levels and ensures interaction between state authorities, regional management structures, medical institutions and citizens. It is proven that digitalizing management processes can be an important tool for increasing the efficiency of restoring the healthcare system of Ukraine, ensuring transparency of management processes, coordination between management entities and optimizing the use of resources during the post-war reconstruction period.

Introduction: the subject area under the study is managing digital business transformation, with the focus on adapting Russian business to the modern context of the artificial intelligence (AI) economy, namely, to a new model of organizing the Russian economic system, the specificity of which is the mass dissemination of AI technologies. Objectives: to develop an ESG strategy for managing sustainable digital business transformation in the artificial intelligence economy. Methods: based on international experience and official statistics for 2022, using the method of regression analysis, the authors compiled an econometric model that mathematically described the influence of the global AI index components on the trade sustainability index. Results: a scientific argumentation has been developed for criticizing the existing strategy for managing digital transformation of business in terms of its sustainability. The market position of a business and its financial well-being are found to be improving to the detriment of society and the environment. As an alternative, ESG management is proposed, ensuring the balanced development of nature, society, economy, and technology. The prospects for sustainable digital business transformation in the Russian AI economy through Pareto-optimization of ESG management has been disclosed. An improved ESG strategy has been developed, the specific feature of which being reliance on integration mechanisms and the focus on infrastructure projects in the AI economy. The proposed strategy scope of application is digital transformation management in the Russian businesses practice. The advantage of the ESG strategy is that it ensures the business system stability in the process of its digital transformation through the unified implementation of the SDGs, the sanction stability and sustainability of positions in the global markets. Conclusions: it is concluded that development of infrastructure support for the artificial intelligence economy is the basis for sustainable digital transformation of business. Maintaining sustainability of the business in this process needs using ESG management to help introduce AI technologies.

Intrinsic value of food chain data

Digital transformation in government operations hastens transparency, efficiency, and customer satisfaction, and the application of artificial intelligence (AI) and machine learning (ML) can optimize digitalization initiatives. This study sought to identify the digitalization and digital transformation policies of the Philippine government and explore how AI technologies can be leveraged in government operations, specifically in frontline services, human resource management, and financial services. Document reviews are the primary methods. It examined legal government documents, published journal articles, and papers. Digital government transformation is still in its foundational/ initiation phase. However, it is noticeable that national policies on AI strategy are set, political support is evident, and government agencies are already leading the digitalization of their services. As AI adoption in digital government transformation has gained traction, there is a need to utilize AI technologies and algorithms to accelerate these efforts, thereby spurring economic growth. Potential AI technologies applicable to these identified services include predictive analytics, deep learning, neural networks, RNN, CNN, and recommender systems, among others.

PurposeIn the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.Design/methodology/approachTo explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.FindingsThe investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.Originality/valueThis study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.

Purpose This study addresses the gap in digital transformation (DT) maturity models that inadequately consider employee needs, leadership roles in transformation processes and artificial intelligence (AI) integration in change management processes, particularly for skilled trades enterprises. It further examines how these human-centred change capabilities support AI-enabled digital innovation and transformation projects, thereby linking DT, project management and innovation in the skilled trades context. Design/methodology/approach Using the echeloned Design Science Research approach, three new maturity model dimensions for change management were developed, which adequately consider employee needs in DT, leadership roles in transformation processes and AI integration. The dimensions were validated through qualitative focus groups with 39 skilled trades experts using a structured four-step evaluation process. The study focuses on DT projects, such as the introduction of AI-supported tools and digital services, and explores how the maturity dimensions can guide the planning and implementation of these AI-enabled innovation projects. Findings The three dimensions successfully enable organizations to assess change management maturity and derive actionable recommendations, though structured facilitation is required. Practical utility depends on establishing clear baselines (e.g., regulatory requirements) and linking abstract maturity levels to specific activities. Practitioners found the model understandable and applicable after a proper introduction. In the context of AI-enabled transformation projects, the dimensions help leaders and project teams to prioritize actions that foster employee well-being, reduce resistance to AI and digital technologies and align innovation efforts with the organization's dynamic capabilities. Originality/value This research uniquely integrates dynamic capabilities theory with change management practice in projects of DT, positioning leadership, change competence and AI as mutually reinforcing organizational capabilities. The first time, maturity model dimensions specifically designed for skilled trades that combine human-centred change management with AI integration, prioritizing employee well-being and organizational culture in DT processes are introduced. By explicitly framing DT as a series of AI-enabled innovation projects, the study contributes to emerging research on the role of AI in project management and demonstrates how a maturity model can bridge AI, project management and innovation in SME and skilled-trades settings.

The digital wave, represented by new technologies such as big data, IoT, and artificial intelligence, is sweeping the globe, driving all industries toward digitalization and intelligent transformation. Digital twins are becoming an indispensable opportunity for new infrastructure initiatives. As geotechnical engineering constitutes a critical component of new infrastructure, its corresponding digital transformation is essential to align with these initiatives. However, due to the difficulty of modeling, the demand for computing resources, interdisciplinary integration, and other issues, current digital twin applications in geotechnical engineering remain in their nascent stage. This paper delineates the developmental status of geotechnical digital twin technology in China, and it focuses on the advantages and disadvantages of digital twins in five application fields, identifying key challenges, including intelligent sensing and interconnectivity of multi-source heterogeneous physical entities, integrated sharing of 3D geological models and structural models, unified platforms for lifecycle information management, standardization of digital twin data protocols, and theoretical frameworks for digital twin modeling. Furthermore, this study systematically expounds future research priorities across four dimensions: intelligent sensing and interoperability technologies for geotechnical engineering; knowledge graph development and model-based systems engineering; integrated digital twin entity technologies combining 3D geological bodies with engineering structures; and precision enhancement, temporal extension, and spatial expansion of geotechnical digital twins. This paper systematically reviews the application status of digital twin technology in geotechnical engineering for the first time, reveals the common technical challenges in cross-domain implementation, and proposes a theoretical framework for digital twin accuracy improvement and spatiotemporal expansion for geotechnical engineering characteristics, which fills the knowledge gap in the adaptability of existing research in professional fields. These insights aim to provide references for advancing digitalization, intelligent transformation, and sustainable development of geotechnical engineering.

Industrial businesses are going through a period of digital disruption and firms are under severe pressure to undertake Digital Transformation and leverage the Industrial Internet of Things (IIoT). Yet, there is next to no scholarly guidance for such an endeavour. Most industrial firms are developing their Digital Transformation strategies, however, they are not sure what kind of capabilities they should develop for such transformation. Though there is limited academic literature about Digital Transformation and how firms are developing digital transformative capabilities, a systematic literature review was performed to disentangle capability transformation processes and how firms are developing dynamic capabilities to remain competitive in a high-velocity environment. The current study extended dynamic capability theory and proposed digital transformative capabilities (DTCs) for Digital Transformation. To understand the IIoT landscape and how it influences Digital Transformation, an industry review was performed. The research was conducted in two phases. Based on the literature review and industry review, in the first phase, two qualitative exploratory studies were performed. The preliminary exploratory study was conducted to get an understanding of the IIoT landscape and how firms were developing capabilities for transformation. Based on the insights from preliminary exploratory study, a detailed exploratory study was performed which revealed critical themes for Digital Transformation and, based on these themes, a conceptual framework for Digital Transformation was derived. The conceptual framework was divided into two models. The front-end model viii identified three DTCs (Business Model Transformation, Operating Model Transformation and Cultural Transformation), three inputs (Digital Twin, Digital Thread and Digital Mindset) and the factors influencing the DTCs. The back-end model examined the influence of DTCs on dynamic capabilities, which may be indicative of digital transformation in a company. In the second phase, these two models were tested through a quantitative analysis, utilizing data generated from 107 respondents from 87 industrial companies via a self-reported online questionnaire and the application of multiple linear regression analysis. The Digital Twin is widely touted as an important input for DTC but the result did not support that. Digital Thread as an input for DTC was supported and Digital Mindset as an input for DTC was partially supported. Using moderator analysis, important insights were identified. The moderators, Technology Turbulence, Market Turbulence, Competitor Turbulence and Path Dependency had some positive moderation effects. The positive influence of ‘DTC – Business Model Transformation’ on dynamic capabilities which may be indicative of digital transformation in a company was not supported. However, the positive influence of ‘DTC – Operating Model Transformation’ was supported and ‘DTC – Cultural Transformation’ was partially supported. The moderation effects of ecosystem partnership and resource scarcity and constraints were partially supported, and the moderation effects of customer and market demands and digital commitment were not supported or refuted.

As a means of industrial recovery in the post-war era, the paper investigates the theoretical and methodological bases as well as pragmatic elements of digital transformation in the travel and tourist sector. The study looks at how digital technology is being used in tourist company operations and notes particular characteristics of digitalization in Ukraine's tourism sector during post-conflict rehabilitation. The study exposes geographical differences in the digital transformation processes of the tourist industry as well as systemic limits, therefore stressing the difficulties in applying technology advancements across diverse areas. Based on a thorough investigation of quantitative and qualitative indicators including both economic performance measurements and technical progress measures, a methodological methodology has been established to assess the efficiency of tourist sector digitalization. By means of an integrated digital tourism ecosystem, the article suggests a way to hasten the digital transformation processes in the travel industry. This method consists on the application of digital marketing communication channels, artificial intelligence technology, and business process automation. Taking into consideration the particular requirements of post-war recovery, strategic vectors for using smart technologies and creating digital marketing communication channels have been supported. Emphasizing the importance of social media platforms and mobile apps in reaching target audiences, the study also points up interesting options for creating marketing communications and digital services to tailor tourist offers. The results help to clarify the part digital transformation plays in the post-war revival of the travel sector and offer doable advice on how to apply digital solutions in travel-oriented companies. The study underlines the need of building an integrated digital ecosystem that guarantees efficient interaction between all market players and improves the competitiveness of national tourist goods in the worldwide market.

In this paper authors are using comparative analysis scientific method for analyzing Macedonian and Slovenian crisis management system. Having in mind that, Republic of Slovenia is a NATO member from beginning of 2009 year and EU member from 2013 year, Slovenian crisis management system is used as a base model for corrections of Macedonian crisis management system, with focus on identifying the positive characteristics of Slovenian model for system of crisis management which can be put to practice, portrayed in this thesis as recommendations for improving the Macedonian crisis management system.Crisis management system on Republic of North Macedonia has a normative and organizational establishment129. For uninterrupted functionality on the crisis management system in Republic of North Macedonia constantly are present: suggesting decisions, consultations, coordination, prompt reactions, effective and adequate use of provided resources and abilities in case of a crisis situation.Slovenian crisis management system basically is consists from following state authorities and institutions: Inter-sectoral analytical group, Ministry of defense (national crisis management center, the protection and rescue administration of Republic of Slovenia, which also has protection and rescue forces and reporting center on Republic of Slovenia – Service 112), other ministries and state authorities on Republic of Slovenia.For this comparative analysis, Slovenian crisis management has been chosen, because is similar to Macedonian model in terms of size and structure. Moreover, Slovenian crisis management system model is designed to manage consequences of possible state of crisis for a certain territory and population which are almost identical to ones on Republic of North Macedonia. This organizational and structural similarity on the crisis management systems on Republic of Slovenia and Republic of North Macedonia are basic prerequisite for a comparative analysis.Obtained results from this comparative analysis can be used as a basis for reforming, upgrading and improving the efficiency and effectiveness on Macedonian crisis management system. Comparative analysis between Macedonian and Slovenian crisis management system gives us data that leads to conclusion that there is a need to take action for improving Macedonian crisis management system through adequate changes of organizational structure on CMC which will increase its effectiveness, improve its professionalism, economy and for overall improving of crisis management system in terms of prevention department as well as crisis management department. Continued development of Macedonian crisis management system must follow the experiences of NATO and EU members, own practices and available resources (forces and funds for prevention and crisis management).

With Industry 5.0 comes the next wave of industrialization, integrating advanced technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), Digital Twins (DTs), Extended Reality (XR), and Blockchain. The pillars of Industry 5.0 are human-centricity, sustainability and resilience (Figure 1). Through the technologies and frameworks of Industrial Metaverse, manufacturing systems undergo digital transformation to obtain new possibilities for optimization of production, systems, and services, with the aid of enhanced collaboration capabilities. Benefits of digital transformation include reduced waste sizes, increased throughput, and more effective workforce training, among others. However, digital transformation also poses several challenges for manufacturers, including but not limited to security, compatibility, and costs involved with initial transformation and further utilization of digital technologies.The purpose of this Research Topic is to examine opportunities and challenges presented by Industry 5.0 during the digital transformation of manufacturing through Industrial Metaverse. The Topic explores current and future digital developments in manufacturing systems to provide useful insights to manufacturers aiming to transform their systems and stay ahead of the curve. Rahamaddula et al. suggested that, while large enterprises have already begun their digital transformation, Small and Medium Enterprises (SMEs) are often left behind. This is a major issue according to the authors, as SMEs form a large percentage of the manufacturing capability of many countries, and losing the benefits associated with digitalization and smart manufacturing can impose significant costs. The main challenge identified in this case was uncertainty and the cost-benefit balance of digital transformation for SMEs. A readiness assessment tool was proposed which utilizes several factors recognized by experts that contribute to the readiness level of an SME, which was applied to practical use cases. Magas et al. highlighted collaboration as the main challenge of digital transformation, regarding data transfer between different domains. While robust system mapping that uses ontologies could solve this problem, it proved to be a significant challenge, especially with inexperienced practitioners. The technologies of Industrial Metaverse and their interoperability can be used to establish platforms of ontology standards, creating collaborative tools that can support ontology framework application, enhance system resilience and ensure smooth cross-domain operations throughout the unique life cycle of every domain. As such, digital transformation provides an opportunity for understanding extended and complex systems.Overcoming the challenge of increasing complexity caused by market demands, leading to the transition from Mass Customization to Mass Personalization, requires leveraging the capabilities of digital technologies. Aheleroff et al. suggested that Industry 5.0 cooperates with the previous concept of Industry 4.0, utilizing enhanced versions of existing technologies which can enhance system sustainability and resilience when they are operated around the pillar of human-centricity Figure 1. Specifically, the collaboration of humans with advanced technologies adds significant value by enabling the utilization of the scalability offered by Industry 4.0 while maintaining high flexibility in production and quality in products. Mourtzis, on the other hand, discussed the possibilities provided by Digital Twins for Human-Machine Interaction (HMI). Traditional systems undergoing digital transformation to become Cyber-Physical Systems (CPSs) often proves a challenge in maintaining a human-in-the-loop approach. Industrial Metaverse platforms offer the solution to this challenge by immersing humans into CPS operations, facilitating greater system understanding and collaboration, streamlining communications, and promoting system adaptability, and thus sustainability and resilience. Even so, significant challenges were discovered when delving deeper into digital transformation, including security, privacy, financial stability, standardization, and regulation.Summarising, this Research Topic showcased several opportunities for digital transformation and Industrial Metaverse to promote Industry 5.0 goals, such as resilience and sustainability. These opportunities rely heavily on the immense potential of advanced digital technologies for data transfer,

The manufacturing sector stands on the cusp of the digital revolution that holds the promise of fundamentally reshaping its operational landscape. This paper delves into the transformative journey of digital integration within the manufacturing realm. Employing a scoping review methodology, this study amalgamates insights from prior literature and case study analyses to shed light on the digital transformation process and its consequent outcomes. The discourse initiates by scrutinizing the prevailing state of digital transformation in the manufacturing sector, with a particular focus on the embracement of Internet of Things (IoT), Artificial Intelligence (AI), Digital Twin (DT) and Robotics technologies that are at the forefront of driving efficiency and spurring innovation. The article then cites China’s experience in the digital transformation of manufacturing and outlines the challenges that manufacturers may encounter, including cultural inertia and skills deficiencies, and spells out strategic interventions to overcome these obstacles. Moreover, the discussion ventures into prospective trajectories and innovations in manufacturing digitalization, forecasting the ramifications of emergent technologies such as advanced robotics, 5G connectivity, sustainable manufacturing practices, and customization trends. The significance of this research’s contribution to the scholarly domain is underscored, culminating in an exhortation directed towards industry stewards and policy framers to champion and facilitate digital transformation, accentuating its strategic imperative and the competitive leverage it bestows. This article delineates a strategic framework for navigating the intricacies of digital transformation within the manufacturing sector, offering invaluable perspectives for academicians, industry practitioners, and policy architects endeavoring to unravel new paradigms of efficiency and competitive edge in the digital epoch.

The aim of this paper is to explore the technological innovation mechanism by which digital transformation (DT) influences total factor productivity (TFP). We take the Chinese listed firms from 2007 to 2020 as research samples, and con- tribute to the above goals based on fixed-effect models, instrumental variables, mediation effect, and moderating effect models. It has been found that (1) while DT contributes positively to productivity, the enhancement of TFP in current DT is primarily attributed to artificial intelligence (AI) technology rather than other techno- logical innovation. (2) From an innovation-directed perspective, the impact of DT on TFP may be offset by other forms of technological innovation, such as green and energy technology. Specifically, the non-AI direction of technological innovation may not align with the productivity implications of DT. (3) Intellectual property protection impedes the impact of DT on productivity and constrains the deployment of AI technology. Conversely, business strategic radicalism and corporate intangible asset have yielded favorable outcomes. This study not only verifies that the technological innovation channel of DT for enhancing TFP mainly stems from AI technology, but also implies that the current DT might exert a negative effect on other technologies. First published online 12 February 2025

In today's world, crises can occur at any time, and efficient crisis management is essential to minimize the impact on society. With the advancement of technology, digital transformation has become an integral part of crisis management. Artificial Intelligence (AI) is one such technology that plays a key role in crisis management by enabling faster, more accurate, and data-driven decision-making. This article explores the impact of digital transformation on crisis management and the key role of AI in this process. It discusses the various ways in which AI can be used in crisis management, such as predicting and mitigating disasters, analyzing social media for real-time information, and providing insights to aid decision-making. The article also highlights the challenges and ethical considerations associated with the use of AI in crisis management. Overall, this article emphasizes the importance of leveraging digital transformation and AI in crisis management to improve the speed, accuracy, and effectiveness of response efforts.

Customer concentration and digital transformation