Skip to main content
SpringerLink
Log in

Technology convergence among various technical fields: improvement of entropy estimation in patent analysis

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Recent years have witnessed rapid and widespread technology convergence (TC) in various technical fields. Researchers often focus on developing new methods to identify TC in a specific field without studying the temporal trend of TC and comparing it among different fields. To fill the research gap, this study extends the literature to examine TC in 35 technical fields with 49,687,173 patents from 2000 to 2018. Interestingly, the analysis results show that Shannon, Tsallis, and Renyi entropies, as the indicators of TC, perform differently in describing temporal trends in TC. Further investigation shows that Shannon entropy cannot well depict the incremental trend of TC as single-field patents increased steeply. Thus, Tsallis entropy is used to analyze TC in the study. And we further analyze the complementary (CTC) and substitutability (STC) technology convergence of four representative technical fields. The results show CTC and STC are heterogeneous in the industrial development stage. Industries represent a high level of CTC and a low level of STC in early R&D stage, while STC increases significantly during industrial development. This study contributes to the literature by clarifying the measurements for TC and identifying CTC and STC in various industries.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Acs, Z. J., Anselin, L., & Varga, A. (2002). Patents and innovation counts as measures of regional production of new knowledge. Research Policy, 31(7), 1069–1085.

    Article  Google Scholar 

  • Alizadeh, L., & Ebrahimi, M. (2018). Rényi and Tsallis and Shannon entropies on D-posets. Journal of Intelligent & Fuzzy Systems, 34(4), 2771–2781.

    Article  Google Scholar 

  • Cho, J. H., Lee, J., & Sohn, S. Y. (2021). Predicting future technological convergence patterns based on machine learning using link prediction. Scientometrics, 126(7), 5413–5429.

    Article  Google Scholar 

  • Cho, Y., & Kim, M. (2014). Entropy and gravity concepts as new methodological indexes to investigate technological convergence: Patent network-based approach. PLoS ONE, 9(6), e98009.

    Article  Google Scholar 

  • Choi, J. Y., Jeong, S., & Kim, K. (2015). A study on diffusion pattern of technology convergence: Patent analysis for Korea. Sustainability, 7(9), 11546–11569.

    Article  Google Scholar 

  • Coccia, M. (2012). Evolutionary trajectories of the nanotechnology research across worldwide economic players. Technology Analysis & Strategic Management, 24(10), 1029–1050.

    Article  Google Scholar 

  • Criscuolo, P. (2006). The ‘home advantage’ effect and patent families. A comparison of OECD triadic patents, the USPTO and the EPO. Scientometrics, 66(1), 23–41.

    Article  Google Scholar 

  • Curran, C. S., & Leker, J. (2011). Patent indicators for monitoring convergence–examples from NFF and ICT. Technological Forecasting and Social Change, 78(2), 256–273.

    Article  Google Scholar 

  • Duan, Y., & Guan, Q. (2021). Predicting potential knowledge convergence of solar energy: Bibliometric analysis based on link prediction model. Scientometrics, 126(5), 3749–3773.

    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 

  • Gambardella, A., & Torrisi, S. (1998). Does technological convergence imply convergence in markets? Evidence from the electronics industry. Research Policy, 27(5), 445–463.

    Article  Google Scholar 

  • Gauch, S., & Blind, K. (2015). Technological convergence and the absorptive capacity of standardisation. Technological Forecasting and Social Change, 91, 236–249.

    Article  Google Scholar 

  • Geum, Y., Kim, C., Lee, S., & Kim, M. S. (2012). Technological convergence of IT and BT: Evidence from patent analysis. Etri Journal, 34(3), 439–449.

    Article  Google Scholar 

  • Grupp, H. (1990). The concept of entropy in scientometrics and innovation research: An indicator for institutional involvement in scientific and technological developments. Scientometrics, 18(3–4), 219–239.

    Article  Google Scholar 

  • Hacklin, F. (2007). Management of convergence in innovation: Strategies and capabilities for value creation beyond blurring industry boundaries. New York: Springer.

    Google Scholar 

  • Hacklin, F. (2008). Fundamentals of convergence and innovation. In Management of convergence in innovation: Strategies and capabilities for value creation beyond blurring industry boundaries (pp. 25–49).

  • Hacklin, F., Marxt, C., & Fahrni, F. (2009). Coevolutionary cycles of convergence: An extrapolation from the ICT industry. Technological Forecasting and Social Change, 76(6), 723–736.

    Article  Google Scholar 

  • Hwang, I. (2020). The effect of collaborative innovation on ICT-based technological convergence: A patent-based analysis. PLoS ONE, 15(2), e0228616.

    Article  Google Scholar 

  • Jung, S., Kim, K., & Lee, C. (2021). The nature of ICT in technology convergence: A knowledge-based network analysis. PLoS ONE, 16(7), e0254424.

    Article  Google Scholar 

  • Karvonen, M., & Kässi, T. (2012). Industry convergence analysis with patent citations in changing value systems. International Journal of Business and Systems Research, 6(2), 150–175.

    Article  Google Scholar 

  • Karvonen, M., Lehtovaara, M., & Kässi, T. (2012). Build-up of understanding of technological convergence: Evidence from printed intelligence industry. International Journal of Innovation and Technology Management, 9(03), 1250020.

    Article  Google Scholar 

  • Kim, B., Gazzola, G., Yang, J., Lee, J. M., Coh, B. Y., Jeong, M. K., & Jeong, Y. S. (2017). Two-phase edge outlier detection method for technology opportunity discovery. Scientometrics, 113(1), 1–16.

    Article  Google Scholar 

  • Kim, J., & Lee, S. (2017). Forecasting and identifying multi-technology convergence based on patent data: The case of IT and BT industries in 2020. Scientometrics, 111(1), 47–65.

    Article  Google Scholar 

  • Kim, K. (2017). Impact of firms’ cooperative innovation strategy on technological convergence performance: The case of Korea’s ICT industry. Sustainability, 9(9), 1601.

    Article  Google Scholar 

  • Kodama, F. (1996). Emerging patterns of innovation: Sources of Japan s teleological edge. R&D Management, 26, 179–181.

    Article  Google Scholar 

  • Kolmogorov, A. N., & Uspenskii, V. A. (1958). On the definition of an algorithm. Uspekhi Matematicheskikh Nauk, 13(4), 3–28.

    MathSciNet  MATH  Google Scholar 

  • Lei, D. T. (2000). Industry evolution and competence development: The imperatives of technological convergence. International Journal of Technology Management, 19(7–8), 699–738.

    Article  Google Scholar 

  • Leydesdorff, L., Kushnir, D., & Rafols, I. (2014). Interactive overlay maps for US patent (USPTO) data based on International Patent Classification (IPC). Scientometrics, 98(3), 1583–1599.

    Article  Google Scholar 

  • Lissoni, F. (2001). Knowledge codification and the geography of innovation: The case of Brescia mechanical cluster. Research Policy, 30(9), 1479–1500.

    Article  Google Scholar 

  • Lybbert, T. J., & Zolas, N. J. (2014). Getting patents and economic data to speak to each other: An ‘algorithmic links with probabilities’ approach for joint analyses of patenting and economic activity. Research Policy, 43(3), 530–542.

    Article  Google Scholar 

  • Porter, A., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81(3), 719–745.

    Article  Google Scholar 

  • Rényi, A. (1961, June). On measures of entropy and information. In Proceedings of the fourth Berkeley symposium on mathematical statistics and probability (Vol. 1, pp. 547–561).

  • Rosenberg, N. (1963). Technological change in the machine tool industry, 1840–1910. The Journal of Economic History, 23(4), 414–443.

    Article  Google Scholar 

  • Ruan, J., & Gopinath, M. (2010). Technological convergence, competitiveness, and welfare: A study of international manufacturing industries. The Journal of International Trade & Economic Development, 19(4), 517–551.

    Article  Google Scholar 

  • Schmoch, U. (2008). Concept of a technology classification for country comparisons. Final report to the world intellectual property organisation (wipo), WIPO.

  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423.

    Article  MathSciNet  MATH  Google Scholar 

  • Sinai, Y. G. (1959, December). On the notion of entropy of a dynamical system. In Doklady of Russian Academy of Sciences (Vol. 124(3), pp. 768–771).

  • Singh, A. K., Shafique, M., Kumar, A., & Henkel, J. (2013, May). Mapping on multi/many-core systems: Survey of current and emerging trends. In 2013 50th ACM/EDAC/IEEE design automation conference (DAC) (pp. 1–10). IEEE.

  • Song, J., Almeida, P., & Wu, G. (2003). Learning–by–hiring: When is mobility more likely to facilitate interfirm knowledge transfer? Management Science, 49(4), 351–365.

    Article  Google Scholar 

  • Stieglitz, N. (2007). Digital dynamics and types of industry convergence: the evolution of the handheld computers market. In J. F. Christensen & P. Maskell (Eds.), The new industrial dynamics of the new digital economy (pp. 179–208).

  • Stirling, A. (2007). A general framework for analysing diversity in science, technology and society. Journal of the Royal Society Interface, 4(15), 707–719.

    Article  Google Scholar 

  • Suh, J., & Sohn, S. Y. (2015). Analyzing technological convergence trends in a business ecosystem. Industrial Management & Data Systems, 115, 719–739.

    Article  Google Scholar 

  • Swink, M. (2003). Completing projects on-time: How project acceleration affects new product development. Journal of Engineering and Technology Management, 20(4), 319–344.

    Article  Google Scholar 

  • Teece, D. J. (2018). Dynamic capabilities as (workable) management systems theory. Journal of Management & Organization, 24(3), 359–368.

    Article  Google Scholar 

  • Tsallis, C. (1988). Possible generalization of Boltzmann-Gibbs statistics. Journal of Statistical Physics, 52(1), 479–487.

    Article  MathSciNet  MATH  Google Scholar 

  • Yayavaram, S., & Chen, W. R. (2015). Changes in firm knowledge couplings and firm innovation performance: The moderating role of technological complexity. Strategic Management Journal, 36(3), 377–396.

    Article  Google Scholar 

Download references

Funding

This research is funded by the National Natural Science Foundation of China (NSFC 72072087) and the National Social Science Foundation (20&ZD154).

Author information

Authors and Affiliations

  1. Laboratory of Data Intelligence and Interdisciplinary Innovation, School of Information Management, Nanjing University, 305-1, Nanjing, China

    Wenjing Zhu, Bohong Ma & Lele Kang

Authors
  1. Wenjing Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bohong Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lele Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lele Kang.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose. This article does not contain any studies with human or animal subjects.

Appendix

Appendix

See Table 6.

Table 6 Technology classification of ISI-OST-INPI
Full size table

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, W., Ma, B. & Kang, L. Technology convergence among various technical fields: improvement of entropy estimation in patent analysis. Scientometrics 127, 7731–7750 (2022). https://doi.org/10.1007/s11192-022-04557-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-022-04557-6

Keywords

Search

Navigation