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Internationalizing AI: evolution and impact of distance factors

Author

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  • Xuli Tang

    (Central China Normal University)

  • Xin Li

    (Huazhong University of Science and Technology)

  • Feicheng Ma

    (Wuhan University)

Abstract

International collaboration has become imperative in the field of AI. However, few studies exist concerning how distance factors have affected the international collaboration in AI research. In this study, we investigate this problem by using 1,294,644 AI related collaborative papers harvested from the Microsoft Academic Graph dataset. A framework including 13 indicators to quantify the distance factors between countries from 5 perspectives (i.e., geographic distance, economic distance, cultural distance, academic distance, and industrial distance) is proposed. The relationships were conducted by the methods of descriptive analysis and regression analysis. The results show that international collaboration in the field of AI today is not prevalent (only 15.7%). All the separations in international collaborations have increased over years, except for the cultural distance in masculinity/felinity dimension and the industrial distance. The geographic distance, economic distance and academic distances have shown significantly negative relationships with the degree of international collaborations in the field of AI. The industrial distance has a significant positive relationship with the degree of international collaboration in the field of AI. Also, the results demonstrate that the participation of the United States and China have promoted the international collaboration in the field of AI. This study provides a comprehensive understanding of internationalizing AI research in geographic, economic, cultural, academic, and industrial aspects.

Suggested Citation

  • Xuli Tang & Xin Li & Feicheng Ma, 2022. "Internationalizing AI: evolution and impact of distance factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 181-205, January.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:1:d:10.1007_s11192-021-04207-3
    DOI: 10.1007/s11192-021-04207-3
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