Internationalizing AI: Evolution and Impact of Distance Factors
Xuli Tang, Xin Li, Feicheng Ma

TL;DR
This study analyzes how various distance factors influence international AI research collaboration, revealing that geographic, economic, and academic distances hinder collaboration, while industrial distance promotes it, with US and China participation being significant.
Contribution
It introduces a comprehensive framework with 13 indicators to quantify multiple distance factors affecting international AI research collaboration.
Findings
International AI collaboration is relatively low at 15.7%.
Geographic, economic, and academic distances negatively impact collaboration.
Industrial distance positively influences international collaboration.
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 (MAG) 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…
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Taxonomy
Topicsscientometrics and bibliometrics research
