Telematics and Informatics最新文献

Digital skills, integral to the functioning of the digital economy and information society, show temporal and spatial variations measured by various indicators. In this article, we assess the spatial and temporal evolution of digital skills under the influence of key factors and domains in the EU countries from 2015 to 2021. Applying spatial autocorrelation analysis, robust geographical heterogeneity and consistent spatial patterns in digital skills are outlined, resulting in two ‘high–high’ and ‘high–low’ clusters in the North and Center, and a ‘low–low’ cluster in the South. Using feature importance selection, key indicators within aggregate domains driving digital skills policy are identified. Spatial lag regression analysis highlights the significance of all domains, revealing spatial and spillover effects on digital skills, with the primary influence observed in the social sphere, technology and innovations, and demography domains. Although the ICT infrastructure domain is statistically more significant in our spatial model along with the economy and technology and innovations, its spillover effects appear relatively modest, indicating a corresponding degree of within-country localization. This study contributes to the understanding of the evolution of digital skills by revealing both spatial relationships and temporal dynamics and strengthening spatial digital policy measures in the EU. The spatial coherence of digital policies, the spatial network of technological and innovation centers in both ‘high–low’ clusters and cross-border locations, and improving the social, demographic, and economic profiles of citizens are critical among other measures to improve digital skills in EU countries.

The integration of generative artificial intelligence technology into research environments has become increasingly common in recent years, representing a significant shift in the way researchers approach their work. This paper seeks to explore the factors underlying the frequency of use of generative AI amongst researchers in their professional environments. As survey data may be influenced by a bias towards scientists interested in AI, potentially skewing the results towards the perspectives of these researchers, this study uses a regression model to isolate the impact of specific factors such as gender, career stage, type of workplace, and perceived barriers to using AI technology on the frequency of use of generative AI. It also controls for other relevant variables such as direct involvement in AI research or development, collaboration with AI companies, geographic location, and scientific discipline. Our results show that researchers who face barriers to AI adoption experience an 11 % increase in tool use, while those who cite insufficient training resources experience an 8 % decrease. Female researchers experience a 7 % decrease in AI tool usage compared to men, while advanced career researchers experience a significant 19 % decrease. Researchers associated with government advisory groups are 45 % more likely to use AI tools frequently than those in government roles. Researchers in for-profit companies show an increase of 19 %, while those in medical research institutions and hospitals show an increase of 16 % and 15 %, respectively. This paper contributes to a deeper understanding of the mechanisms driving the use of generative AI tools amongst researchers, with valuable implications for both academia and industry.