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논문 기본 정보

자료유형
학술저널
저자정보
Zhou Wenqi (부경대학교) Oguta Job Francis (부경대학교) Ling Li (Yangtze River Delta(Jiaxing) Planning & Design Group Co.) 박세훈 (부경대학교 기술혁신경영연구소) 천동필 (부경대학교)
저널정보
아태인문사회융합기술교류학회 아시아태평양융합연구교류논문지 Asia-pacific Journal of Convergent Research Interchange Vol.10 No.7
발행연도
2024.7
수록면
111 - 132 (22page)
DOI
http://dx.doi.org/10.47116/apjcri.2024.07.09

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초록· 키워드

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As a disruptive emerging technology, AI will have a profound impact on production and life and even reshape the world landscape. Therefore, major economies have elevated the development of AI to the level of a national strategy and are actively creating an environment for AI innovation. Previous studies have examined AI innovation regarding its integration with business and management or international competition. They have adopted a limited focus only on specific application levels and have overlooked the holistic and comprehensive perspective to explore and understand AI innovation. This study fills this gap by adopting configuration perspective to comprehensively analyze national AI innovations, focusing on the coupling effects of antecedent condition interdependencies. This paper empirically analyzes 39 country cases using the fuzzy-set qualitative comparative analysis (fsQCA) methodology, constructs an AI innovation system that includes five antecedent conditions: human capital, infrastructure, government strategy, social support, and commercial environment, and explores what configuration of antecedent conditions can produce high national AI innovation performance. It is also combined with a new Necessary Condition Analysis (NCA) method to test whether there is a single necessary condition and the degree of necessity. The results show that: (1) The commercial environment was found to be a necessary condition for high AI innovation performance through the NCA approach; (2) Three configurations leading to high AI innovation performance and four configurations leading to non-high AI innovation performance were identified through the fsQCA approach. This study chooses a new perspective to reveal the multiple causal concurrent mechanisms of national AI innovation. We unlock the pathways and different configurations that lead to high national AI innovation performance and provide a finer-grained quantitative analysis of the necessary conditions. Our work provides new empirical evidence and managerial insights for understanding national AI innovation, which has yet been done in previous research.

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