Identify cross-country knowledge flow and innovation trajectory: insights from patent citation network analysis of 5G technology

ABSTRACT

Identifying cross-border knowledge flow and innovation trajectory helps a nation to achieve competitive advantages in the technology race. This paper uses a comprehensive patent analysis method and assessment of national innovation capability to visualise the innovation trajectory and core technology flow of 5G technology across countries (regions) from 2002 to 2019. Firstly, we uncover technology leading countries, technology imitating countries, technology participating countries, and technology holding countries by assessing the level of national technology innovation capability and identifying their development trajectory. Secondly, we observe that China, Japan, and South Korea show more prominence in pictorial communication; the United States focuses on waveguide-related technology, while European countries emphasise antenna-related technology. Finally, we study the diffusion and flow of 5G dominated technology and analyze the technology layout of 5G technology leading countries. The novelty of this study lies in revealing the relationship and significance between the national level of innovation trajectory and the technology level of knowledge flow trajectory. The paper provides implications for the investment of scientific research funds and the choice of cooperative countries in the future. The proposed identification framework of cross-border knowledge flow and innovation trajectory can be applied to other science and technology domains.

KEYWORDS:

• 5G patent citation network
• social network analysis
• patentometrics
• global competition situation
• technology foresight

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The countries involved in this paper include two regions of the People's Republic of China (Taiwan and Hong Kong), and no special distinction will be made later.

Additional information

Funding

This work was supported by China Scholarship Council: [Grant Number 202106470049]; National Natural Science Foundation of China: [Grant Number 71571022, 72273137].

Notes on contributors

Yadi Xuan

Yadi Xuan, Ph.D candidate of School of Economics and Management, Beijing University of Posts and Telecommunications. She received the B.Sc. degree from Shanxi University, the M.Sc. degree from University of Chinese Academy of Sciences. Her research interests are in the area of patentometrics, bibliometrics, knowledge graph and social network analysis.

Shengtai Zhang

Shengtai Zhang, professor of School of Economics and Management, Beijing University of Posts and Telecommunications. He received the B.Sc. degree from Shanxi Normal University, the M.Sc. degree from Central China Normal University, and Ph.D degree from Xi'an Jiaotong University. His research interests are in the area of knowledge management, human resource management, knowledge sharing and innovation of mobile social network.

Xuerong Li

Xuerong Li, assistant professor of Academy of Mathematics and Systems Science, Chinese Academy of Sciences. She received the B.E. degree from Renmin University, and the Ph.D. degree from University of Chinese Academy of Sciences. Her research interests are in the area of bibliometrics, machine learning and economic forecasting.

Xiuting Li

Xiuting Li, associate professor in finance in the School of Economics and Management, University of Chinese Academy of Science. Her research interests focuse on real estate economics and finance, financial risk management.