Examining the role of higher education learning, research excellence, and innovation capacity in driving AI-technological advancements in Nordic countries
Abstract Higher education, research, and innovation are essential for advancing a systematic understanding of the responsible deployment and application of AI-driven technologies. These mechanisms facilitate the evaluation of societal impacts, the identification and mitigation of risks associated wi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2025-08-01
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| Series: | Humanities & Social Sciences Communications |
| Online Access: | https://doi.org/10.1057/s41599-025-05665-3 |
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| Summary: | Abstract Higher education, research, and innovation are essential for advancing a systematic understanding of the responsible deployment and application of AI-driven technologies. These mechanisms facilitate the evaluation of societal impacts, the identification and mitigation of risks associated with misuse, and the enhancement of AI capabilities for specific, practical applications. However, how effective are these mechanisms in achieving these outcomes? This study, therefore, investigates the effectiveness of higher education learning, research excellence, and innovation capacity in relation to AI-driven technology, as well as the moderation effect of good governance on these relationships, using data from Nordic countries spanning from 2009 to 2023. The analysis employs the dynamic common correlated effects (DCCE) model by Chudik and Pesaran (2015) and the panel non-causality test by Juodis et al. (2021). The findings revealed that higher education learning, research excellence, and innovation capacity actively promote the development of AI-driven technology in Nordic countries. Furthermore, good governance positively influences the connection, with the magnitude of the influence being greatest on higher education learning, followed by innovation capacity, and then research excellence. Moreover, there is bidirectional causality between all the variables and AI-driven technology; thus, the variables and AI-driven technology are the determinants of one another. In line with these findings, policy recommendations were proposed. |
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| ISSN: | 2662-9992 |