Svitlana Bilan

Ethical leadership in public administration increasingly depends on understanding how employees from different generations prioritise integrity, accountability, anti-corruption, trust and participation within public organisations. Previous research has usually examined corporate social responsibility, organisational ethics and generational differences as separate streams, leaving limited evidence on whether the combined structure of ethical priorities differs systematically across generations of public employees. The study therefore aims to identify and compare generational patterns in the perceived importance of ethical principles of corporate social responsibility in Slovak public administration and to determine which dimensions drive the strongest differentiation. The calculations are based on an online questionnaire conducted in Slovakia between November 2025 and February 2026 among 302 public-administration employees, including 93 respondents from Generation X, 132 from Generation Y and 77 from Generation Z, drawn from territorial self-government organisations and different organisational levels. The five-point Likert survey was processed using descriptive statistics, Mann-Whitney comparisons, Kruskal-Wallis tests, Holm correction, epsilon-squared and rank-biserial effect sizes, strong-endorsement shares, rankings, multiple correspondence analysis and permutation-based multivariate analysis of variance, with calculations performed in Microsoft Excel and Python using SciPy, statsmodels and Matplotlib. The strongest omnibus differences concerned employee ethical integrity, with epsilon-squared of 0.356, and responsibility and oversight, with epsilon-squared of 0.335, whereas public interest was the only principle without a significant between-generation difference, with a Holm-adjusted probability value of 0.521. Pairwise analysis identified the largest contrast between Generations Y and Z for employee ethical integrity, with a rank-biserial correlation of 0.779, while responsibility and oversight separated Generation Z from Generation X with a value of 0.772 and from Generation Y with a value of 0.710. Strong endorsement revealed distinct profiles: 74.2% of Generation X prioritised responsibility and oversight, 68.9% of Generation Y prioritised the anti-corruption environment, and 71.4% of Generation Z prioritised citizen participation, while the global multivariate analysis confirmed separation of the combined ethical profiles with a pseudo-F statistic of 35.02, an explained-variation coefficient of 0.190 and a permutation probability value below 0.001. The findings can support public-sector leaders, integrity officers and policymakers in designing generation-sensitive ethical leadership, accountability and participation practices in Slovakia and comparable administrations, while future cross-country research can test the stability of the identified profiles across institutional contexts.

Type of the article: Research ArticleAbstractArtificial intelligence (AI) is increasingly used in innovation activities, yet firm-level evidence on variation across innovation stages and organizational conditions remains limited in emerging economies such as Kazakhstan. This study examines AI use among surveyed firms in Kazakhstan, focusing on stage-specific influence, organizational correlates, barriers, reported effects, and managerial perceptions. A cross-sectional voluntary survey yielded a non-probability sample of 93 firms; data were analyzed using descriptive statistics, reliability analysis, Friedman and Wilcoxon tests, exploratory bivariate analysis, multinomial logistic regression, and descriptive review of open-ended recommendations. In the sample, 39.8% reported no AI use in innovation activities; 23.7% used AI frequently or always. Idea generation was rated higher than idea testing and selection (r = .398), although concordance in stage rankings was weak (Kendall’s W = .077). The reported AI-related resource-category count was associated with both occasional (RRR = 6.98) and frequent AI use (RRR = 7.10) relative to no reported AI use in innovation activities. At the same time, firm size and age were not significant. Skills shortages, financial constraints, regulatory uncertainty, and ethical concerns were the leading barriers. Among firms reporting AI use in innovation activities, the most frequently reported effects were greater innovation-process efficiency (46.4%) and improved understanding of industry opportunities and trends (30.4%). The findings provide exploratory firm-level evidence and highlight the relevance of organizational resource support, pilot-based implementation, and human oversight.AcknowledgmentThis research was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP23487651).The authors sincerely thank JSC "Center for International Programs" and the "500 Scientists" research internship program of the Republic of Kazakhstan. A. Zhidebekkyzy's participation in the "500 Scientists" program provided a valuable academic environment that inspired the initial idea for this study.

The accelerating interaction among financial markets, venture capital ecosystems, sustainable finance, and energy transition investment has heightened the strategic importance of green and digital energy startups as drivers of financial innovation, low-carbon technological transformation, and long-term economic competitiveness in European economies. Previous studies within the fields of financial markets, investment management, and entrepreneurial finance provide limited evidence on how venture capital expenditures simultaneously affect both the creation and financing of green and digital energy startups over different temporal horizons, while insufficiently accounting for stage-specific effects, innovation ecosystems, and the role of financial-market support in sustainable investment processes. This study aims to assess how venture capital expenditures influence the creation and financing of green and digital energy startups in European and neighboring economies, with particular attention to delayed effects, funding-stage differences, and national innovation conditions. The empirical analysis uses a balanced panel of 37 countries for 2018–2023 and applies Poisson pseudo-maximum likelihood (PPML) models with country- and year-fixed effects for startup counts, two-way fixed-effects models with Driscoll–Kraay standard errors for funding outcomes, and two-part robustness models for later-stage funding. The contemporaneous PPML results show positive but statistically insignificant effects: a 10-point increase in venture capital expenditures is associated with a 1.09% increase in expected green energy startups and a 0.62% increase in expected digital energy startups. The one-year lagged model shows a significant effect only for green energy startups, where the expected number increases by 1.64%. In comparison, the effect for digital energy startups is 0.75% and insignificant. The strongest evidence appears in the two-year lagged models, where venture capital expenditures are associated with a 2.35% increase in expected green energy startups and a 3.89% increase in expected digital energy startups. Funding models reveal no significant immediate or one-year effects. At the same time, two-part robustness results show that two-year lagged venture capital expenditures increase the odds of later-stage green funding by 77.6% and the amount of such funding by 23.8%. These findings contribute to the literature on financial markets, sustainable finance, entrepreneurial investment, and innovation financing, while also providing practical implications for investment management, venture capital policy, and the development of long-term financial support mechanisms for green and digital energy entrepreneurship in European economies.

Ethical and transparent governance has become increasingly important because clean-energy transformation depends on institutional quality, investor confidence, and the capacity of public authorities to create predictable conditions for sustainable innovation. This article aims to assess whether ethical and effective institutional governance creates favorable conditions for the development of green and digital energy start-ups. The study uses an unbalanced panel of 144 countries and economies over 2000–2023, combining data from the IEA Energy Start-up Data Explorer and the World Bank Worldwide Governance Indicators, and applies PPML models with country- and year-fixed effects, one- and two-year lags, multicollinearity diagnostics, and quadratic specifications. The results show that green start-up counts are not significantly associated with any WGI indicator in contemporaneous, one-year or two-year lagged models, suggesting that green start-up formation is less directly linked to short-term institutional variation. For digital start-up counts, Political Stability is consistently positive and significant: a one-point increase is associated with approximately 20.7% higher expected digital start-up activity in the contemporaneous model, 13.9% in the one-year lag model and 12.1% in the two-year lag model. Rule of Law becomes significant only in lagged models, with a one-point increase associated with approximately 16.9% higher expected digital start-up activity after one year and 39.1% after two years. For green energy finance, Voice and Accountability is associated with approximately 21.4% higher early-stage green funding. In comparison, Government Effectiveness is associated with approximately 4.7% higher early-stage and 3.8% higher later-stage green funding. Quadratic models reveal governance thresholds, including 67.26 for Voice and Accountability and 74.84 for Rule of Law in green start-up formation.

Type of the article: Research ArticleThe study analyzes how academic excellence is conceptualized within Kazakhstani universities, focusing on two key internal stakeholder groups: faculty members and administrative staff. While academic excellence has become a global priority, little empirical evidence exists on how it is interpreted in emerging higher education systems. The paper addresses this gap by examining the Kazakhstani case, where government-led excellence initiatives are still in their early stages. A quota-based survey was conducted across 42 universities, producing weighted responses from 832 faculty and 155 administrators. Quantitative data were processed with IBM SPSS Statistics 25, employing descriptive statistics, Welch’s t-test, and two-way ANOVA to compare perceptions between the groups. Despite a broad consensus on the multidimensional nature of academic excellence (positive agreement averaged > 94%), the results reveal consistent differences in their interpretation of core parameters. Of the 32 indicators tested, only four showed no statistically significant difference between faculty and administrators: faculty numbers (p = 0.246), academic reputation and stakeholder recognition (p = 0.701), graduate employability and employer satisfaction (p = 0.106), and student enrollment (p = 0.588). Overall, administrators assigned systematically higher importance to institutional characteristics, enabling components, and barriers across all thematic blocks. Consistent with the conceptual framework integrating institutional and stakeholder perspectives, these patterns indicate that external policy pressures and role-specific responsibilities shape interpretations of excellence. These findings provide a data-driven basis for designing initiatives that couple system-level reforms with participatory governance and co-created metrics, thereby improving the translation of policy into practice.Acknowledgment This research was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. BR21882373).

Type of the article: Research Article AbstractThe paper aims to explore tourists’ perceptions and behaviors regarding sustainable tourism and sustainable development in Armenia. An online survey method was employed to collect data from 385 domestic tourists in Armenia from July to October 2024, enabling efficient and cost-effective access to a diverse respondent group. The sample size was determined based on a 95% confidence level and 5% margin of error, ensuring statistical reliability. The online format allowed for a broader reach and anonymity, making it suitable for exploring tourists’ knowledge of the SDGs, comprehension of sustainable tourism, and willingness to embrace sustainable behaviors.Interestingly, despite only 46% of respondents being explicitly familiar with the SDGs, a significant proportion (64%) demonstrated knowledge of sustainable development, 62% understood sustainable tourism, and 64% believed tourism could positively impact sustainable development. Statistical analysis, specifically the Pearson Chi-Square test, revealed a strong correlation between knowledge of sustainable development and awareness of sustainable tourism, and tourists’ willingness to modify their travel habits and invest in eco-friendly options. Additionally, a sense of environmental responsibility was found to significantly influence the adoption of sustainable practices. Moreover, prioritizing sustainable tourism when selecting destinations strongly correlated with support for sustainable tourism initiatives.While many tourists acknowledged the importance of sustainable tourism, challenges such as limited information and higher costs hindered the adoption of eco-friendly behaviors. To promote sustainable tourism and contribute to achieving the SDGs, the study emphasizes the need for intensified awareness campaigns, accessible sustainable options, and collaborative efforts between the tourism industry, government, and tourists.

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. AcknowledgmentsThe 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”.

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

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).