Elena Kašťáková

Type of the article: Research Article

Understanding the alignment between public interest and academic research is increasingly relevant in the context of global sustainability challenges. This study aims to investigate the relationship between societal attention, as measured by Google Trends, and scholarly output on renewable energy and the shadow economy. Using bibliometric data from Scopus and Web of Science alongside global Google Trends data from 2004 to 2025, the analysis employed Pearson and Spearman correlation coefficients, Granger causality, and distance correlation to assess the strength, direction, and form of association between public search trends and academic activity. The results reveal a significant Granger-causal relationship from public searches on “renewable energy” to academic publications, with F-statistics above 5.2 (p < 0.01), and strong positive correlations (Pearson r = 0.72; Spearman ρ = 0.69; distance correlation = 0.63). In contrast, the terms “informal economy” and “feed-in tariff” demonstrated weak or inconsistent associations, with correlations below 0.25 and statistically insignificant causality tests (p > 0.1). Cross-country comparisons further highlighted uneven alignment, with India showing high search intensity (Google Trends index > 75) but relatively low publication volume (< 2% of global output). At the same time, South Africa displayed closer coherence, with both indicators moving in tandem (r ≈ 0.61). These findings underscore scholarly research’s partial and asymmetric responsiveness to public demand, varying significantly by topic and geographic context. Moreover, while Google Trends offers robust signals of societal interest, disparities in digital access and literacy reduce its universality, pointing to critical underexplored research gaps with direct policy relevance.

Acknowledgment
This study was prepared as part of the project supported by the National Scholarship Programme of the Slovak Republic, the project 101127491-EnergyS4UA-ERASMUS-JMO2023-HEI-TCH-RSCH. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or European Education and Culture Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. This research was funded by the grants VEGA 1/0689/23 “Sustainable growth and the geopolitics of resilience in the context of crisis prevention” and VEGA 1/0254/25 “Artificial Intelligence and FDI-invested Business Service Centers: Selected Macroeconomic and Corporate Aspects”.

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).