Evolutionary mechanisms of global AI collaborative innovation networks: evidence from patent data

Xuanting Ye, Wenting Liang, Ziming Yu, Hong Guan and Hao Liu ()
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Xuanting Ye: Beijing Institute of Technology, School of Management
Wenting Liang: Beijing Institute of Technology, School of Global Governance
Ziming Yu: Beijing Institute of Technology, School of Global Governance
Hong Guan: Beijing Institute of Technology, School of Management
Hao Liu: Beijing Institute of Technology, School of Management

Scientometrics, 2025, vol. 130, issue 10, No 17, 5695-5729

Abstract: Abstract Artificial intelligence (AI) is a strategic technology driving the new wave of technological revolution and industrial transformation. Exploring the characteristics of global AI collaborative innovation networks can help countries or regions identify more international collaboration opportunities, accelerate the acquisition and reuse of external knowledge, and enhance their own technological innovation capabilities. Therefore, based on global transnational AI collaboration patent application data from 1998 to 2021, this paper constructed global AI collaborative innovation networks and used the TERGM model to analyze its structural characteristics and evolutionary mechanisms. The research conclusions include: First, the global AI collaborative innovation networks have a small network diameter, relatively short average path length and large average clustering coefficient, with countries or regions tending to establish new collaborative relationships with collaborators of their collaborators. Second, countries or regions tend to establish collaborative relationships with those on the same continent but do not share a border. Third, strong economic attributes, such as market size and economic development level, significantly promote the formation of collaborative relationships between countries or regions. Fourth, although collaborative relationships may change over time, showing slight fragility, the global AI collaborative innovation networks are generally stable. This paper integrates digital innovation theory and social network theory, supplementing and extending AI collaborative innovation networks' structural characteristics and evolutionary mechanisms. It provides strategic references for policymakers and managers seeking more collaboration opportunities and accelerating their integration into global innovation networks.

Keywords: Artificial intelligence; Innovation network; Cross-border collaboration; Evolution characteristics; Temporal exponential random graph model (TERGM) (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11192-025-05433-9

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