Svitlana Bilan

A comprehensive study of the consumers’ needs and demands, the patterns of their social and cultural behavior are essential to the success of the modern luxury companies in their market activity. The purpose of research is to study the possible differences and similarities in the perception of luxury and motivation to purchase these goods by consumers in different parts of the world and selection of acceptable marketing tools. The study is built on the results of analysis of cultural values in some countries using the country comparison tool developed by Hofstede and the results of analysis of scientific articles on the behavior of luxury goods consumers in different countries. The methodology approach – Hofstede’s model of national culture and scientific studies belonging to cultural impacts on luxury consumers’ behavior – is applied to explore cross-cultural differences in luxury consumption behavior. The findings of the study are as follows: consumers from countries with high power distance, collectivism, and masculinity, long-term orientation, and low indulgence primarily perceive social values in consuming luxury goods and are sensitive to conspicuous luxury-status. In cultures with high individualism, masculinity, indulgence, and low power distance, consumers display to perceive individual and functional values and are sensitive to “emotional hedonistic luxury.” The consumption of luxury goods in the Asian region is characterized by significant focusing on social values and status consumption, more than in the rest of the world. The motives of achievements in the form of self-realization and self-actualization are among the main reasons for the luxury consumption of European Union consumers.

In advanced economies, the acceleration of scientific and technological progress, the introduction of innovations are connected with the opportunities for developing the knowledge economy, commercialization of technologies and innovation transfer networks. The current stage of scientific and technical progress takes place in conditions of dynamic global competition and at the same time requires cooperation and collaboration. This paper aims to determine the dependence of economic development on indicators characterizing the potential of a country’s knowledge economy and transfer of innovations. The study focuses on finding ways to assess the impact of the potential of the country’s scientific and educational activities on innovative development using the characteristics of the “business-education-science” system in the field of innovation transfer. The methodological research tools are the abstract-logical method and correlation-regression analysis, which allowed establishing relevant links between the indicators of the development of education, science and the economy in the context of the knowledge economy and the transfer of innovations for Ukraine and European countries in the period 2016–2021. It was found that in many countries of the “new” Europe (Bulgaria, Greece, Lithuania, Latvia, Estonia, Slovakia, Slovenia, Cyprus, Malta, Portugal), as well as in Ukraine, the trend of independence of GDP growth from indicators of the development of science and education is maintained. At the same time, the countries of “old” Europe maintain their innovative progress at a high level, investing in science-intensive activities, infrastructure and the R&D sector. The results can be used to improve the efficiency of innovation transfer in Ukraine, for which the paper provides relevant recommendations.

The study explores the impact of artificial intelligence (AI) technologies on project management (PM) across different industries. It aims to assess how AI adoption in PM affects project efficiency. The study surveyed 159 project supervisors and specific project managers implementing projects from 7 industries in the Republic of Kazakhstan: software, green energy, engineering, construction, science, transport, and tourism. The research used variance and linear regression analyses to evaluate the relationship between AI adoption and project efficiency level measured by the Likert scale from 1 to 5 and test the associated hypotheses. The results show that AI adoption varies among industries, with software, construction, and scientific projects being the most active users. The study also found that the use of AI differed across eight project performance domains, with the stakeholder domain using voice technologies and process automation and the uncertainty domain using fewer tools. Projects with higher AI adoption rates showed higher efficiency scores (for example, in Software projects, the AI adoption rate is 3.2; the efficiency rate is 3.3), while those with lower efficiency levels (for example, in the Tourism industry, the AI adoption rate is 1.9; the efficiency rate is 2.2) showed the worst results. Decision-making systems, process automation, and voice technologies are the three most critical AI technologies PM professionals use to improve project efficiency.

Acknowledgments
This research has been funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP19680313).

As an upper-middle-income country, Armenia should develop and implement targeted policies, such as increased R&D investments, education reforms, and industry-academia collaboration, to enhance its innovation performance. Strengthening these areas is expected to contribute to higher Global Innovation Index (GII) rankings, reflecting improved national innovation capacity.
This study aims to estimate the impact of various GII components (including pillars, sub-indices, and sub-pillars) on the overall GII and pillar scores for upper-middle-income countries. Based on these findings, the study seeks to identify Armenia’s key policy priorities and provide targeted recommendations for enhancing its innovation performance. This study employs a cross-sectional regression to analyze the factors influencing GII scores in upper-middle-income countries, assessing the impact of sub-indices, pillars, and sub-pillars. The analysis reveals that market sophistication and creative outputs strongly influence GII scores among upper-middle-income countries, contributing significantly to national innovation performance. Additionally, knowledge and technology outputs, human capital and research, and infrastructure pillars show a statistically significant impact at the 5% level. Notably, even minor improvements in innovation output sub-index scores account for substantial variations in GII rankings. These findings suggest that Armenia should prioritize targeted education reforms, increase R&D investment, and strengthen university-industry linkages to enhance its innovation ecosystem and improve its global competitiveness.

Acknowledgment
The research was supported by the Science Committee of the Republic of Armenia within the framework of the project No. 21T-5B234.

This study aims to examine how different types of pro-environmental video content (featuring humans versus AI-generated characters) influence household waste sorting attitudes and behaviors among Ukrainian residents. The research was conducted in two stages using a mixed-method approach. In the first stage, 102 individuals aged 18–45 watched two videos on waste sorting and completed an online questionnaire. Cluster and variance analyses were performed using Statistica software. In the second stage, 35 participants underwent a laboratory-based emotion analysis using iMotions software, heart rate monitors, and galvanic skin response sensors at the Behavioral Lab of Sumy State University (Ukraine) from May to July 2024. The results revealed that videos featuring real people were more effective in generating interest (average rating: 3.5 vs. 3.2) and emotional engagement, particularly joy and contempt, which were the most frequently expressed emotions. Cluster analysis identified four distinct respondent groups. Cluster 1 (39.2%) – primarily young women – responded positively to human-led videos but showed limited behavioral change. Cluster 2 (19.6%) – women aged 26–35 – reacted positively to both videos and were most willing to adopt waste sorting behavior. Cluster 3 (23.5%) – primarily men – showed moderate engagement and sorted waste occasionally. Cluster 4 (17.6%) – highly educated women – exhibited the least positive responses and were least likely to change their behavior. The emotion analysis confirmed that videos featuring real people elicited stronger emotional responses across all categories, whereas AI-generated videos prompted higher levels of anger but generally weaker engagement.

Acknowledgments
The authors would like to acknowledge the Behavioral Laboratory at Sumy State University for providing the essential facilities and resources that enabled the successful completion of this research. Additionally, sincere appreciation is extended to all study participants for their valuable time, commitment, and contributions, which significantly enriched our understanding of consumer behavior related to waste management and the perception of advertising content.
The research is supported by the International Visegrad Fund: Visegrad Fellowship Program. Project № 62410031, “Marketing research consumer behaviour in the waste management system”, and by the budget of the Ministry of Education and Science of Ukraine (research topic 0123U100112 “Post-war recovery of the energy industry of Ukraine: Optimization of waste management taking into account the health of the population, environmental, investment, tax determinants”.