RESEARCH ARTICLE
Untangling spatio-temporal dynamics and determinants of technology transfer from a patent assignment perspective: The case of China's AI data
Wen Zeng,
Yuefen Wang,
Zhichao Ba,
Yonghua Cen,
Wen Zeng
School of Intellectual Property, Nanjing University of Science & Technology, Nanjing, China
Search for more papers by this authorCorresponding Author
Yuefen Wang
School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China
Institute for Big Data Science, Tianjin Normal University, Tianjin, China
Correspondence
Yuefen Wang, Institute for Big Data Science, Tianjin Normal University, Tianjin, 300387, China.
Email: [email protected]
Search for more papers by this authorZhichao Ba
School of Digital Economy and Management, Nanjing University, Suzhou, China
Search for more papers by this authorYonghua Cen
Institute for Big Data Science, Tianjin Normal University, Tianjin, China
Search for more papers by this authorWen Zeng,
Yuefen Wang,
Zhichao Ba,
Yonghua Cen,
Wen Zeng
School of Intellectual Property, Nanjing University of Science & Technology, Nanjing, China
Search for more papers by this authorCorresponding Author
Yuefen Wang
School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China
Institute for Big Data Science, Tianjin Normal University, Tianjin, China
Correspondence
Yuefen Wang, Institute for Big Data Science, Tianjin Normal University, Tianjin, 300387, China.
Email: [email protected]
Search for more papers by this authorZhichao Ba
School of Digital Economy and Management, Nanjing University, Suzhou, China
Search for more papers by this authorYonghua Cen
Institute for Big Data Science, Tianjin Normal University, Tianjin, China
Search for more papers by this authorFirst published: 01 July 2024
Abstract
This study delves into the spatio-temporal dynamics and influencing mechanisms of technology transfer. Leveraging graph theory, we constructed a patent transfer network to understand its evolving patterns. We redefined technology transfer types, analyzed transition probabilities through Markov chain, and summarized their temporal and spatial shifts. Incorporating spatial and nonspatial methods, we explored the heterogeneity of key drivers, such as GDP and internal R&D expenditures, across regions. Our findings reveal that China's AI technology transfer network transformed from sparse to densely interconnected, with transfer types evolving from singular to diversified directions and objects. Provinces often maintain stability or transition to adjacent types, forming agglomerations of similar transfer types. GDP and internal R&D expenditures emerge as key drivers, exerting distinct impacts across regions. This study offers insights to enterprises and policymakers in developing tailored strategies for promoting technology transfer.
CONFLICT OF INTEREST STATEMENT
The authors declare that there are no competing interests.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
REFERENCES
- Brain, H., Alexander, A., & Richard, C. (1997). Mapping the university technology transfer process. Journal of Business Venturing, 12(6), 423–434. https://doi.org/10.1016/S0883-9026(96)00064-X
- Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281–298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
- Chen, Y., Wang, K., & Yu, C. (2023). Inter provincial technology transfer in China: Spatial correlation and endogenous evolution mechanism. Studies in Science of Science, 41(1), 38–50. https://doi.org/10.16192/j.cnki.1003-2053.20220426.003
10.16192/j.cnki.1003?2053.20220426.003 Google Scholar
- David, J. T. (2018). Profiting from innovation in the digital economy: Enabling technologies, standards, and licensing models in the wireless world. Research Policy, 47, 1367–1387. https://doi.org/10.1016/j.respol.2017.01.015
- Deng, S., & Zong, Q. (2014). A method of patent visualization based on CNIPR: Taking multiplex communication (H04J) as an example. Information Science, 32(11), 82–87. https://doi.org/10.13833/j.cnki.is.2014.11.046
10.13833/j.cnki.is.2014.11.046 Google Scholar
- Dongwoo, Y. (2011). Height and death in the antebellum United States: A view through the lens of geographically weighted regression. Economics and Human Biology, 10(1), 43–53. https://doi.org/10.1016/j.ehb.2011.09.006
- Duan, D., Chen, Y., & Du, D. (2019). Regional integration process of China's three major urban agglomerations from the perspective of technology transfer. Scientia Geographica Sinica, 39(10), 1581–1591. https://doi.org/10.13249/j.cnki.sgs.2019.10.007
10.13249/j.cnki.sgs.2019.10.007 Google Scholar
- Duan, D., Du, D., Chen, Y., & Guan, M. (2018). Technology transfer in China's city system: Process, pattern and influencing factors. Acta Geographica Sinica, 73(4), 738–754. https://doi.org/10.11821/dlxb201804011
10.11821/dlxb201804011 Google Scholar
- Fang, W., & Zheng, L. (2020). Research on regional differences and influencing factors of technology transfer deficiency of civil-military integration enterprises. Operations Research Management Science, 29(8), 1–11. https://doi.org/10.12005/orms.2020.0194
10.12005/orms.2020.0194 Google Scholar
- Fotheringham, S. A., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression. Annals of the American Association of Geographers, 107(6), 1247–1265. https://doi.org/10.1080/24694452.2017.1352480
- Guan, L., Wang, H., & Wang, C. (2022). The role of China Association for Science and Technology in promoting the construction of pivotal technology trading market. Science & Technology Review, 40(6), 122–132. https://doi.org/10.3981/j.issn.1000-7857.2022.06.014
10.3981/j.issn.1000?7857.2022.06.014 Google Scholar
- Guan, P., & Wang, Y. (2020). Advances in patent network. Data Analysis and Knowledge Discovery, 4(1), 26–39. https://doi.org/10.11925/infotech.2096-3467.2019.1201
10.11925/infotech.2096?3467.2019.1201 Google Scholar
- Hang, Y., Xu, K., Wang, Y., & Jin, Z. (2022). New trends of international technology transfer based on a multi-dimensional institutional logics. Science & Technology Progress and Policy, 39(8), 153–160. https://doi.org/10.6049/kjjbydc.2021080015
10.6049/kjjbydc.2021080015 Google Scholar
- Harvey, B. (1968). Physics and the polity. Science, 160(3826), 396–400. https://doi.org/10.1126/science.160.3826.396
- Huang, Q., Wang, Z., Wang, D., & Du, B. (2015). Review for Markov theory and its application in the forecast. Technology and Market, 22(9), 12–13. +16. https://doi.org/10.3969/j.issn.1006-8554.2015.09.003
10.3969/j.issn.1006?8554.2015.09.003 Google Scholar
- Jian, Z., & Zhan, S. (2009). The relationship among absorptive capacity, knowledge integration, organizational knowledge and technology transfer performance. Science of Science and Management of S.& T., 30(6), 81–86.
- Kang, X., Zhang, W., Wang, Y., & Yang, Z. (2020). Research on university-enterprise technology transfer characteristics based on invention patent transfer data. Science and Management, 40(2), 10–19. https://doi.org/10.3969/j.issn.1003-8256.2020.02.002
10.3969/j.issn.1003?8256.2020.02.002 Google Scholar
- Krafft, J., Quatraro, F., & Saviotti, P. P. (2011). The knowledge-based evolution in biotechnology: A social network analysis. Economics of Innovation and New Technology, 20, 445–475. https://doi.org/10.1080/10438599.2011.562355
10.1080/10438599.2011.562355 Google Scholar
- Lewis, T. G. (2009). Network science: Theory and applications (pp. 59–60). John Wiley & Sons. https://doi.org/10.1002/9780470400791
10.1002/9780470400791 Google Scholar
- Li, G., Yu, H., Liang, Z., & Zhang, Y. (2022). Research on the influencing factors of supply and demand matching in technology trading: Configuration analysis based on TOE framework. Information Studies: Theory & Application, 45(2), 85–93. +120. https://doi.org/10.16353/j.cnki.1000-7490.2022.02.012
10.16353/j.cnki.1000?7490.2022.02.012 Google Scholar
- Li, X. (1992). Contemporary technology transfer: Characteristics, types and strategies. Review of Economic Research, Z7, 354–368. https://doi.org/10.16110/j.cnki.issn2095-3151.1992.z7.040
10.16110/j.cnki.issn2095?3151.1992.z7.040 Google Scholar
- Liang, L., Zhang, C., Huang, J., Zhang, J., & Zhang, X. (2020). Research and practice on evaluation method of national technology transfer system. Science and Technology Management Research, 40(10), 56–64. https://doi.org/10.3969/j.issn.1000-7695.2020.10.008
10.3969/j.issn.1000?7695.2020.10.008 Google Scholar
- Liu, C., Niu, C., & Han, J. (2019). Spatial dynamics of intercity technology transfer networks in China's three urban agglomerations: A patent transaction perspective. Sustainability, 11(6), 1647. https://doi.org/10.3390/su11061647
- Liu, C., & Yan, S. (2022). Spatial evolution and determinants of transnational technology transfer network in China. Acta Geographica Sinica, 77(2), 331–352. https://doi.org/10.11821/dlxb202202005
10.11821/dlxb202202005 Google Scholar
- Liu, J., Chang, H., Forrest, J., & Yang, B. (2020). Influence of artificial intelligence on technological innovation: Evidence from the panel data of China's manufacturing sectors. Technological Forecasting and Social Change, 158, 399–409. https://doi.org/10.1016/j.techfore.2020.120142
- Liu, X., Jiang, T., & Ma, F. (2013). Collective dynamics in knowledge networks: Emerging trends analysis. Journal of Informetrics, 7(2), 425–438. https://doi.org/10.1016/j.joi.2013.01.003
- Liu, Y., & Tsai, S. B. (2022). Dynamic evolution of service trade network structure and influence mechanism in countries along the “belt and road” with big data analysis. Mathematical Problems in Engineering, 2022(1), 1–13. https://doi.org/10.1155/2022/8378137
- Losacker, S. (2022). License to green: Regional patent licensing networks and green technology diffusion in China. Technological Forecasting and Social Change, 175, 1–17. https://doi.org/10.1016/j.techfore.2021.121336
- Moore, D., & Webb, A. L. (2022). Evaluating energy burden at the urban scale: A spatial regression approach in Cincinnati, Ohio. Energy Policy, 160, 1–11. https://doi.org/10.1016/j.enpol.2021.112651
- Ni, Q., Lu, Y., He, X., & Tang, F. (2021). Differences and dynamic evolution of urban innovation. Journal of Quantitative & Technological, 38(12), 67–84. https://doi.org/10.13653/j.cnki.jqte.2021.12.003
10.13653/j.cnki.jqte.2021.12.003 Google Scholar
- Permai, S. D., Christina, A., & Santoso, G. A. (2021). Fiscal decentralization analysis that affects economic performance using geographically weighted regression. Procedia Computer Science, 179, 399–406. https://doi.org/10.1016/j.procs.2021.01.022
10.1016/j.procs.2021.01.022 Google Scholar
- The Xinhua News Agency. (2015). Law of PRC on promoting the transformation of scientific and technological achievements. Retrieved July 20, 2023, from http://www.npc.gov.cn/npc/c12488/201508/4f3b83a6bb6442f28c9eef5b8935bbfc.shtml
- The Xinhua News Agency. (2017). Notice of The State Council on printing and distributing the Plan for the Construction of the National Technology Transfer System. Retrieved July 20, 2023, from http://www.gov.cn/xinwen/2017-09/26/content_5227705.htm
- Xie, X., Gao, X., Li, Z., & Xiao, Y. (2019). Research on network structure of interregional patent transfer. Science and Technology Management Research, 39(7), 170–176. https://doi.org/10.3969/j.issn.1000-7695.2019.07.024
10.3969/j.issn.1000?7695.2019.07.024 Google Scholar
- Xu, Q., Kang, X., Yang, Z., & Zhang, C. (2017). Research on the characteristics of inter-provincial technology transfer in China based on patent right transfer. Journal of Intelligence, 36(7), 66–72. https://doi.org/10.3969/j.issn.1002-1965.2017.07.012
10.3969/j.issn.1002?1965.2017.07.012 Google Scholar
- Zeng, W., Wang, Y., & Zhou, H. (2020). Analysis of the distribution and evolution of patent application status in the industrial field—Taking the field of artificial intelligence as an example. Information Science, 38(12), 4–11. https://doi.org/10.13833/j.issn.1007-7634.2020.12.001
10.13833/j.issn.1007?7634.2020.12.001 Google Scholar
- Zhang, J., Wang, X., Liu, Y., Huang, Y., Wan, D., Yali, Q., & Liao, Q. (2017). Characteristics of China's patent technology transfer in Beijing-Tianjin-Hebei region. Science and Technology Management Research, 37(22), 79–85. https://doi.org/10.3969/j.issn.1000-7695.2017.22.011
10.3969/j.issn.1000?7695.2017.22.011 Google Scholar
- Zhang, S., & Ni, H. (2013). Analysis of the structure and contribution rate of R&D investment in Beijing. Forum on Science and Technology in China, 5, 5–11. https://doi.org/10.13580/j.cnki.fstc.2013.05.002
10.13580/j.cnki.fstc.2013.05.002 Google Scholar
- Zhang, T., & Wu, J. (2021). Spatial network structure and influence mechanism of green development efficiency of Chinese cultural industry. Scientia Geographica Sinica, 41(4), 580–587. https://doi.org/10.13249/j.cnki.sgs.2021.04.004
10.13249/j.cnki.sgs.2021.04.004 Google Scholar
- Zhang, X., & Lin, J. (2015). Research on the distribution and determinants of technology market in China. Studies in Science of Science, 33(10), 1471–1478. https://doi.org/10.16192/j.cnki.1003-2053.2015.10.005
10.16192/j.cnki.1003?2053.2015.10.005 Google Scholar
- Zhao, S., Liu, Y., Shi, A., & Li, Z. (2020). Research on the transfer of patented technology in the UAV field among provinces based on social network analysis. In Z. Zhou, Q. Dai, Y. Guo, & J. Zhang (Eds.), 2020 2nd international conference on artificial intelligence and advanced manufacture (pp. 185–190). MDPI.
