Karina Taraniuk

While implementing the radical transformations, such as the business processes reengineering in the work of industrial enterprises, there is a need to increase the personnel operating efficiency in the business processes redesign. The urgent increase of control by the top management of companies is demanded for observing the procedure and standards of business processes reengineering at the enterprises in order to increase its cost effectiveness. Therefore, the relevance of the scientific research subject is indisputable. The research works of academic economists concerning the features of staffing for the business processes reengineering of companies are analyzed. The ways to overcome the resistance to organizational changes of industrial enterprises by the staff are developed. The description of the effective members of the reengineering team in the work of the company has been improved by forming special features of each member of the reengineering team. The personal and professional competencies of the personnel are shaped and offered as the most effective ones for successful realization of marketing activity reengineering at the industrial enterprises. A questionnaire was developed as part of the analysis and selection of personnel during the marketing activities reengineering at the industrial enterprises. The resulting component of the questionnaire was developed for making managerial decision on personnel selection in reengineering teams.

One of the main conditions for improving the overall competitiveness of the country is the formation of its sustainable logistic potential, since logistics is responsible for promoting the country’s products, both in the domestic and foreign markets. This is also characteristic for agricultural companies of the main countries of agricultural production in the world, which need to manage their logistic business processes effectively. The business processes of logistics sphere include the processes of warehousing, transportation and further distribution of agricultural products between the entities of the agrarian market (warehousing, traders, wholesale and retail trade network, consumers) at the national, as well as international level. Internationalization of agricultural production includes the formation of cooperative industrial relations in the agrarian sphere between the countries in the chain of “production, processing and marketing”, and the management of business processes in the logistics sphere. The latter is characterized by the business process management of the warehousing, transport, distribution areas at the national and international levels and food safety, which represents increasing demand for food in many countries of the world. These are some of the main signs of sustainable logistic development at the national and international levels. A methodical approach to assessing the logistic potential of the main countries of agricultural production in the world is improved; this method is characterized by a set of competitive elements and includes the assessment of logistics management of countries by blocks: product safety, internationalization of production and logistics, management of business processes. The result of this improved method of assessing the logistic potential of the world countries is a developed criterial basis, which allows making effective managerial decisions at the interstate and state levels regarding the logistic development of agrarian productions.

Type of the article: Research Article

Artificial intelligence has become a driver of knowledge transformation, skills renewal, and institutional change, making lifelong learning increasingly important for adapting to AI-driven labor markets and societies. This study aims to examine whether national AI development indicators are associated with realized participation in education and training across different adult age groups in European countries, and to discuss what these associations may imply for lifelong learning and knowledge transfer policies. The analysis is based on a panel of 18 European countries for 2017–2024 and applies two-way fixed-effects models with country and year effects, contemporaneous, one-year, and two-year lag specifications, and Driscoll–Kraay robustness checks. The results show that the total AI Vibrancy Score is not a statistically significant predictor of participation in education and training: the contemporaneous coefficients are 0.4822 for adults aged 18-74, 0.1054 for those aged 45-54, and 0.5006 for those aged 50-74. Descriptive statistics indicate that average lifelong-learning participation declines with age, from 20.09% among adults aged 18-74 to 14.82% among those aged 45-54, and 9.34% among those aged 50-74. The lagged structural models show that AI-related R&D is negatively associated with subsequent participation, with one-year lag coefficients of −1.2310, −0.9392, and −0.8911 for the three age groups, respectively. In contrast, AI-related Policy and Government activity has a positive two-year lagged association for adults aged 18-74 and 45-54, with coefficients of 0.6064 and 0.7346. This suggests that policy-related AI development, rather than national AI development alone, may be more relevant for observed adult participation in education and training.

Acknowledgments
This research was funded by an EU grant “Immersive Marketing in Education: Model Testing and Consumers’ Behavior” under project No. 09I03-03-V04-00522/2024/VA and by the Ministry of Education and Science of Ukraine “Modeling and forecasting of socioeconomic consequences of higher education and science reforms in wartime” (No. 0124U000545).