Green, Blue and Digital Economy Journal

CONCEPTUAL MODEL OF DIGITAL TRANSFORMATION OF BUSINESS MANAGEMENT ON THE EXAMPLE OF COMPANIES FOR SALE OF AIR CONDITIONING PRODUCTS

Viktor Bernadskyi — 2026-06-29

The relevance of the problem is caused by the rapid growth of digital technologies which transform management methods in companies-manufacturers and sellers of air conditioning. In the competitive market with significant seasonality of demand, digital transformation of business is becoming a crucial condition for increased efficiency and stability. The main problem is that digitalization is implemented unevenly in various business processes (management, logistics, service), and the capabilities are not realized completely. The aim of the study is to create a conceptual model of digital transformation of business management of companies that install air conditioning systems. The object of the study is the management processes of the studied companies, the subject is digital tools and methods of their introduction into the business environment. To enable the generalize world practices on digitization it applies comparative analysis; to reveal connections of essential business processes – system analysis; to choose important digital tools it applies expert appraisals method; to create integrated concept model – modeling. Analyzed current level of digitalization of the industry, identified problematic spots in the management and service processes, prepared a list of the key digital solutions and built a model for digital transformation. Based on the study results it can be claimed that the proposed model is applicable for more precise planning, better logistics performance and better customer service quality. In digital transformation the phased approach has to be used. First, it would be necessary to audit digital maturity and implement the CRM system with analysis, after that, automate warehouse and service operations, followed by systematic skill development.

ACCOUNTING FOR MARKETING COSTS AS A TOOL FOR INCREASING THE EFFICIENCY OF THEIR MANAGEMENT AT THE ENTERPRISE

Daria Kravets — 2026-06-29

The purpose of the paper is to substantiate the role of accounting for marketing costs as a key tool for improving the efficiency of their management at the enterprise in the context of digitalization, as well as to develop approaches to enhancing the analytical value of accounting information for marketing decision-making. Methodology. The study is based on a systematic approach that combines theoretical generalization of scientific research, analysis of regulatory provisions (NP(S)AS 16 "Expenses"), and evaluation of practical aspects of accounting for marketing costs at enterprises. Methods of comparative analysis, classification, and synthesis were used to identify approaches to the interpretation and accounting of marketing expenses. Results. It is determined that marketing costs are not distinguished as a separate accounting category and are fragmented across selling, administrative, and other operating expenses. The absence of a unified approach to defining and classifying marketing costs reduces the analytical value of accounting data. The study identifies key approaches to the relationship between marketing and selling expenses and highlights the need for detailed analytical accounting by types of marketing activities, channels, and responsibility centers. The importance of integrating accounting data with marketing performance indicators in a digital environment is emphasized. Practical implications. The proposed approach to organizing accounting for marketing costs enhances transparency, improves cost control, and enables the evaluation of the effectiveness of marketing activities. It provides a basis for optimizing resource allocation and supports more informed managerial decision-making. Value / originality. The originality of the study lies in the justification of an analytically oriented approach to marketing cost accounting, which transforms it from a recording system into a strategic management tool. The paper contributes to the development of integrated accounting and analytical frameworks adapted to the conditions of digitalization.

STRATEGIC PLANNING IN THE ERA OF DIGITAL TRANSFORMATION AND AI: UKRAINE IN COMPARISON WITH THE GLOBAL BUSINESS ENVIRONMENT (2018–2025)

Andriy Melnyk — 2026-06-29

The purpose of the paper is to examine how the strategic priorities of corporate leaders in Ukraine and globally evolved during 2018–2025, influenced by digital transformation and artificial intelligence. Methodology. The study applies a secondary comparative longitudinal analysis based on recent academic literature and KPMG leadership materials published between 2018 and 2025. Because the survey waves differ across years and are not fully identical in design, the comparison is conducted through harmonized strategic themes rather than strictly matched indicators. Results. The findings indicate a broad shift from digitally informed strategic planning in 2018–2019, through crisis- and resilience-driven digital adaptation in 2020–2023, to AI-centered strategic planning in 2024–2025. In both Ukraine and the global business environment, technology, talent, resilience, and governance increasingly moved from operational concerns to strategic priorities. However, global leaders frame AI more strongly in terms of governance, data readiness, and expected returns, whereas Ukrainian leaders emphasize resilience, security, implementation feasibility, cost, and skills. Practical implications. The study shows that effective strategic planning in the digital era requires not only technology adoption but also talent development, governance capacity, risk management, and contextual adaptation. For Ukraine, this implies that digital transformation and AI should be integrated into the strategy as instruments of both modernization and resilience. Value/originality. The paper contributes a comparative longitudinal synthesis of executive strategic priorities and argues that Ukraine should be interpreted not as a delayed version of the global trajectory, but as a distinct strategic context in which digital transformation and AI are shaped by instability, recovery needs, and wartime resilience.

USE OF ARTIFICIAL INTELLIGENCE AND PREDICTIVE ANALYTICS IN FORECASTING LOGISTICS RISKS: CONTEMPORARY APPROACHES TO SUPPLY CHAIN RESILIENCE MANAGEMENT IN THE UNITED STATES

Nazar Holovchuk — 2026-07-10

The purpose of the article is to examine contemporary approaches to the application of artificial intelligence (AI) and predictive analytics in logistics risk forecasting, analyze their implementation in supply chain management systems of U.S. enterprises, and develop the author's conceptual framework for integrating AI and predictive analytics into logistics risk management in order to enhance supply chain resilience in the context of the digital transformation of the U.S. economy. Research methodology. The methodological framework of the study is based on a combination of general scientific and specialized research methods. Methods of analysis, synthesis, and literature review were employed to systematize contemporary theoretical approaches to the application of AI and predictive analytics in logistics risk forecasting. A systems approach was used to examine the interrelationships among supply chain components and logistics risk management processes. Comparative analysis was applied to investigate AI implementation practices adopted by leading U.S. companies. The method of generalization was used to identify major trends in the digital transformation of logistics and to formulate the study's conclusions. A graphical method was employed to develop the author's conceptual framework for integrating AI and predictive analytics into a logistics risk forecasting system. Finally, the logical-analytical method was applied to substantiate directions for improving supply chain resilience management under conditions of economic digital transformation. Results. The findings indicate that the implementation of artificial intelligence and predictive analytics is fundamentally transforming logistics risk management by enabling a shift toward data-driven models based on continuous monitoring and forecasting of potential supply chain disruptions. The study demonstrates that the effectiveness of such systems depends on the integration of heterogeneous data sources, the application of machine learning algorithms, and their interaction with enterprise information platforms. The analysis of best practices adopted by leading U.S. enterprises made it possible to systematize the principal areas of AI application in logistics, including demand forecasting, transportation route optimization, inventory management, supplier reliability assessment, real-time monitoring of logistics operations, and decision support. The study found that the integrated use of these technologies significantly enhances the adaptability of supply chains to external risks. Practical implications. A major practical outcome of the research is the development of the proposed conceptual framework for integrating AI and predictive analytics into a logistics risk forecasting system. Unlike existing approaches, the proposed framework combines data collection and processing, intelligent forecasting, risk assessment, decision support, and a continuous model improvement mechanism based on newly generated data. This integrated approach provides a foundation for improving logistics management efficiency and strengthening supply chain resilience. The proposed framework may be applied by enterprises, logistics service providers, and other organizations in developing digital risk management strategies, as well as in future research on intelligent decision support systems for logistics. Value/Originality. The principal theoretical contribution of this study is the development of an original conceptual framework for integrating Artificial Intelligence and Predictive Analytics into logistics risk forecasting systems. The framework combines data acquisition, intelligent data analytics, risk forecasting, decision support, and the continuous improvement of predictive models within a unified digital architecture. The proposed approach may serve as a methodological foundation for the development of digital logistics management systems across enterprises operating in various industries.