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Exploring the patterns of international technology diffusion in AI from the perspective of patent citations

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Abstract

This paper presents the findings from a thorough analysis of international technology diffusion (ITD) in artificial intelligence (AI) technologies. We construct a novel framework to explore the patterns of ITD in AI based on patent data published from 1970 to 2019. To this aim, we establish a nexus between technology innovation (TI) capacity and international technology diffusion (ITD) degree, and divide the countries/regions into three different groups—the leading, middle and backward. Considering the intersecting characteristic of AI technology, this paper examines the ITD patterns in the whole, single-field and intersecting-field AI technology areas. Empirical results show that: (1) Similar patterns are observed in the whole and single-field AI technology. ITD degree decreases significantly as TI capacity increases in leading countries, while it always remains high though the TI capacity improves in backward countries. Middle countries, however, show a transitional state between the two. (2) Compared to the whole AI and single-field AI technology, the pattern of ITD in intersecting-field AI technology is different. The number of nodes in the intersecting-field AI technology has decreased significantly, and the trend is more pronounced in middle and backward countries than in leading countries. These patterns imply that the technological innovation achievements of middle and backward countries will be first identified and utilized by leading countries, which will broaden the growing digital divide between countries and pose a more significant challenge to achieving technological catch-up in the future.

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Notes

  1. As shown in Table 2 of the Appendix, we classify countries with more than 10,000 patent publication records as leading countries, countries with 1000 to 10,000 records as middle countries, and countries with less than 1000 records as backward countries.

  2. It should be added that the reason for our logarithmic fit is to intuitively observe the trend and direction of the nodes' evolution over time rather than causal analysis.

References

  • Aaronson, S. A., & Leblond, P. (2018). Another digital divide: The rise of data realms and its implications for the WTO. Journal of International Economic Law, 21(2), 245–272.

    Article  Google Scholar 

  • Agrawal, A., Gans, J., & Goldfarb, A. (2019). Economic policy for artificial intelligence. Innovation Policy and the Economy, 19(1), 139–159.

    Article  Google Scholar 

  • Alonso, C., Berg, A., Kothari, S., Papageorgiou, C., & Rehman, S. (2020). Will the AI revolution cause a great divergence? IMF Working Papers, 2020(184), 1–42. https://doi.org/10.5089/9781513556505.001

  • Andrés, L., Cuberes, D., Diouf, M., & Serebrisky, T. (2010). The diffusion of the Internet: A cross-country analysis. Telecommunications Policy, 34(5–6), 323–340.

    Article  Google Scholar 

  • Andrews, D., Criscuolo, C., & Gal, P. N. (2015). Frontier firms, technology diffusion, and public policy: Micro evidence from OECD countries. OECD Productivity Working Papers, No. 2, OECD Publishing, Paris, 1–79. https://doi.org/10.1787/5jrql2q2jj7b-en.

  • Caselli, F., & Coleman, W. J. (2001). Cross-country technology diffusion: The case of computers. American Economic Review, 91(2), 328–335.

    Article  Google Scholar 

  • Cho, T. S., & Shih, H. Y. (2011). Patent citation network analysis of core and emerging technologies in Taiwan: 1997–2008. Scientometrics, 89(3), 795–811.

    Article  Google Scholar 

  • Comin, D., & Hobijn, B. (2004). Cross-country technology adoption: Making the theories face the facts. Journal of Monetary Economics, 51(1), 39–83.

    Article  Google Scholar 

  • Cruz-Jesus, F., Oliveira, T., & Bacao, F. (2018). The global digital divide: Evidence and drivers. Journal of Global Information Management (JGIM), 26(2), 1–26.

    Article  Google Scholar 

  • Duguet, E., & MacGarvie, M. (2005). How well do patent citations measure flows of technology? Evidence from French innovation surveys. Economics of Innovation and New Technology, 14(5), 375–393.

    Article  Google Scholar 

  • Eaton, J., & Kortum, S. (1996). Trade-in ideas Patenting and productivity in the OECD. Journal of International Economics, 40(3–4), 251–278.

    Article  Google Scholar 

  • Eaton, J., & Kortum, S. (1999). International technology diffusion: Theory and measurement. International Economic Review, 40(3), 537–570.

    Article  Google Scholar 

  • Fu, X., Pietrobelli, C., & Soete, L. (2011). The role of foreign technology and indigenous innovation in the emerging economies: Technological change and catching-up. World Development, 39(7), 1204–1212.

    Article  Google Scholar 

  • Fujii, H., & Managi, S. (2018). Trends and priority shifts in artificial intelligence technology invention: A global patent analysis. Economic Analysis and Policy, 58, 60–69.

    Article  Google Scholar 

  • Furman, J. L., Porter, M. E., & Stern, S. (2002). The determinants of national innovative capacity. Research Policy, 31(6), 899–933.

    Article  Google Scholar 

  • Globerman, S., Kokko, A., & Sjöholm, F. (2000). International technology diffusion: Evidence from Swedish patent data. Kyklos, 53(1), 17–38.

    Article  Google Scholar 

  • Gong, G., & Keller, W. (2003). Convergence and polarization in global income levels: A review of recent results on the role of international technology diffusion. Research Policy, 32(6), 1055–1079.

    Article  Google Scholar 

  • Hafner, K. A. (2008). The pattern of international patenting and technology diffusion. Applied Economics, 40(21), 2819–2837.

    Article  Google Scholar 

  • Haruna, S., Jinji, N., & Zhang, X. (2010). Patent citations, technology diffusion, and international trade: Evidence from Asian countries. Journal of Economics and Finance, 34(4), 365–390.

    Article  Google Scholar 

  • Ho, C. C., & Tseng, S. F. (2006). From digital divide to digital inequality: The global perspective. International Journal of Internet and Enterprise Management, 4(3), 215–227.

    Article  Google Scholar 

  • Horowitz, M. C. (2018). Artificial intelligence, international competition, and the balance of power. Texas National Security Review, 1(3), 37–57.

  • Hsueh, C. C., & Wang, C. C. (2009). The use of social network analysis in knowledge diffusion research from patent data. In 2009 International Conference on Advances in Social Network Analysis and Mining, IEEE, Athens, Greece, 20-22 July 2009, pp. 393–398.

  • Hu, A. G., & Jaffe, A. B. (2003). Patent citations and international knowledge flow: the cases of Korea and Taiwan. International Journal of Industrial Organization, 21(6), 849–880.

  • Hu, M. C., & Mathews, J. A. (2005). National innovative capacity in East Asia. Research Policy, 34(9), 1322–1349.

    Article  Google Scholar 

  • Huang, H. C., & Shih, H. Y. (2012). Exploring the structure of international technology diffusion. In 2012 Proceedings of PICMET’12: Technology Management for Emerging Technologies, Vancouver, BC, Canada, 29 July-2 Aug. 2012, pp. 1527–1541.

  • Huang, Y., Porter, A. L., Zhang, Y., Lian, X., & Guo, Y. (2019). An assessment of technology forecasting: Revisiting earlier analyses on dye-sensitized solar cells (DSSCs). Technological Forecasting and Social Change, 146, 831–843.

    Article  Google Scholar 

  • Jaffe, A. B., & Trajtenberg, M. (2002). Patents, citations, and innovations: A window on the knowledge economy. MIT Press.

    Google Scholar 

  • Ji, J., Barnett, G. A., & Chu, J. (2019). Global networks of genetically modified crops technology: A patent citation network analysis. Scientometrics, 118(3), 737–762.

    Article  Google Scholar 

  • Johnstone, N., Haščič, I., & Kalamova, M. (2010). Environmental policy design characteristics and technological innovation: Evidence from patent data. OECD Environment Working Papers, No. 16, OECD Publishing, Paris. https://doi.org/10.1787/5kmjstwtqwhd-en.

  • Keller, W. (2002). Geographic localization of international technology diffusion. American Economic Review, 92(1), 120–142.

    Article  Google Scholar 

  • Keller, W. (2004). International technology diffusion. Journal of Economic Literature, 42(3), 752–782.

    Article  Google Scholar 

  • Li, X., Chen, H., Huang, Z., & Roco, M. C. (2007). Patent citation network in nanotechnology (1976–2004). Journal of Nanoparticle Research, 9(3), 337–352.

    Article  Google Scholar 

  • Metcalfe, S., & Ramlogan, R. (2008). Innovation systems and the competitive process in developing economies. The Quarterly Review of Economics and Finance, 48(2), 433–446.

    Article  Google Scholar 

  • Perkins, R., & Neumayer, E. (2005). The international diffusion of new technologies: A multi-technology analysis of latecomer advantage and global economic integration. Annals of the Association of American Geographers, 95(4), 789–808.

    Article  Google Scholar 

  • Schiff, M., & Wang, Y. (2006). North-South and South-South trade-related technology diffusion: An industry-level analysis of direct and indirect effects. Canadian Journal of Economics, 39(3), 831–844.

    Article  Google Scholar 

  • Seck, A. (2012). International technology diffusion and economic growth: Explaining the spillover benefits to developing countries. Structural Change and Economic Dynamics, 23(4), 437–451.

    Article  Google Scholar 

  • Shih, H. Y., & Chang, T. L. S. (2009). International diffusion of embodied and disembodied technology: A network analysis approach. Technological Forecasting and Social Change, 76(6), 821–834.

    Article  Google Scholar 

  • Suarez-Villa, L. (1990). Invention, inventive learning, and innovative capacity. Behavioral Science, 35(4), 290–310.

    Article  Google Scholar 

  • Tsay, M. Y., & Liu, Z. W. (2020). Analysis of the patent cooperation network in global artificial intelligence technologies based on the assignees. World Patent Information, 63(2020), 1–14.

  • Tseng, C. Y., & Ting, P. H. (2013). Patent analysis for technology development of artificial intelligence: A country-level comparative study. Innovation, 15(4), 463–475.

    Article  Google Scholar 

  • Verspagen, B. (1991). A new empirical approach to catching up or falling behind. Structural Change and Economic Dynamics, 2(2), 359–380.

    Article  Google Scholar 

  • Wang, W., & Siau, K. (2018). Artificial intelligence: a study on governance, policies, and regulations. MWAIS 2018 proceedings, 1–5.

  • Xu, B., & Chiang, E. P. (2005). Trade, patents and international technology diffusion. The Journal of International Trade & Economic Development, 14(1), 115–135.

    Article  Google Scholar 

  • Yang, W., Yu, X., Zhang, B., & Huang, Z. (2019). Mapping the landscape of international technology diffusion (1994–2017): network analysis of transnational patents. The Journal of Technology Transfer. https://doi.org/10.1007/s10961-019-09762-9

    Article  Google Scholar 

  • Zhao, R., Li, X., Li, D. (2020). Research of Institutional Technology Diffusion Rules Based on Patent Citation Network—A Case Study of AI Field. In Zhang, Y. D., Senjyu, T., Chakchai, S., Joshi, A (Eds.), Smart Trends in Computing and Communications (pp. 41–49). Springer, Singapore.

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Acknowledgements

The authors would like to acknowledge support from the National Natural Science Foundation of China (Grant No. 71904096). The findings and observations contained in this paper are those of the authors and do not necessarily reflect the views of the supporters.

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Authors and Affiliations

  1. School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Lidan Jiang

  2. School of Public Policy and Management, Tsinghua University, Beijing, 100084, China

    Jingyan Chen & Yuhan Bao

  3. School of Public Administration, Hunan University, Changsha, 410082, China

    Fang Zou

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  1. Lidan Jiang
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  2. Jingyan Chen
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  4. Fang Zou
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Correspondence to Jingyan Chen.

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Appendix

Appendix

See Tables 1 and 2

Table 1 Search strategy for AI patents
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Table 2 Countries/regions and their country code
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Jiang, L., Chen, J., Bao, Y. et al. Exploring the patterns of international technology diffusion in AI from the perspective of patent citations. Scientometrics 127, 5307–5323 (2022). https://doi.org/10.1007/s11192-021-04134-3

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