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Identifying Emerging Technologies and Influential Companies Using Network Dynamics of Patent Clusters

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Cyberdefense

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 342))

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Abstract

The need for dependable and real-time insights on technological paradigm shifts requires objective information. We develop a lean recommender system which predicts emerging technology by a sequential blend of machine learning and network analytics. We illustrate the capabilities of this system with patent data and discuss how it can help organizations make informed decisions.

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Notes

  1. 1.

    For the sake of brevity, the terms ‘recommender system’ and ‘system’ are used interchangeably.

References

  1. Andersen, B. (1999). The hunt for S-shaped growth paths in technological innovation: A patent study. Journal of Evolutionary Economics, 9(4), 487–526.

    Article  Google Scholar 

  2. Bengisu, M., & Nekhili, R. (2006). Forecasting emerging technologies with the aid of science and technology databases. Technological Forecasting and Social Change, 73(7), 835–844.

    Article  Google Scholar 

  3. Boon, W., & Moors, E. (2008). Exploring emerging technologies using metaphors: A study of orphan drugs and pharmacogenomics. Social Science & Medicine, 66(9), 1915–1927.

    Article  Google Scholar 

  4. Clemen, R. (1989). Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5(4), 559–583.

    Article  Google Scholar 

  5. Corrocher, N., Malerba, F., & Montobbio, F. (2003). The emergence of new technologies in the ICT field: Main actors, geographical distribution and knowledge sources. Department of Economics, University of Insubria. https://EconPapers.repec.org/RePEc:ins:quaeco:qf0317.

  6. Daim, T., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73(8), 981–1012.

    Article  Google Scholar 

  7. Halaweh, M. (2013). Emerging technology: What is it? Journal of Technology Management, 8(3), 108–115.

    Google Scholar 

  8. Haupt, R., Kloyer, M., & Lange, M. (2007). Patent indicators for the technology life cycle development. Research Policy, 36(3), 387–398.

    Article  Google Scholar 

  9. Hung, S.-C., & Chu, Y.-Y. (2006). Stimulating new industries from emerging technologies: Challenges for the public sector. Technovation, 26(1), 104–110.

    Article  Google Scholar 

  10. Intepe, G., & Koc, T. (2012). The use of S curves in technology forecasting and its application on 3D TV technology. International Journal of Industrial and Manufacturing Engineering, 6(11), 2491–2495.

    Google Scholar 

  11. Kucharavy, D., Schenk, E., & De Guio, R. (2009). Long-run forecasting of emerging technologies with logistic models and growth of knowledge. In Proceedings of the 19th CIRP Design Conference (p. 277).

    Google Scholar 

  12. Kyebambe, M., Cheng, G., Huang, Y., He, C., & Zhang, Z. (2017). Forecasting emerging technologies: A supervised learning approach through patent analysis. Technological Forecasting and Social Change, 125, 236–244.

    Article  Google Scholar 

  13. Lee, C., Kwon, O., Kim, M., & Kwon, D. (2018). Early identification of emerging technologies: A machine learning approach using multiple patent indicators. Technological Forecasting and Social Change, 127(3), 291–303.

    Article  Google Scholar 

  14. Makridakis, S., & Winkler, R. L. (1983). Averages of forecasts: Some empirical results. Management Science, 29(9), 987–996.

    Article  Google Scholar 

  15. Martin, B. (1995). Foresight in science and technology. Technology Analysis & Strategic Management, 7(2), 139–168.

    Article  Google Scholar 

  16. Meyer, M. (2001). Patent citation analysis in a novel field of technology: An exploration of nano-science and nano-technology. Scientometrics, 51(1), 163–183.

    Article  Google Scholar 

  17. Nieto, M., Lopéz, F., & Cruz, F. (1998). Performance analysis of technology using the S curve model: The case of digital signal processing (DSP) technologies. Technovation, 18(6), 439–457.

    Article  Google Scholar 

  18. Porter, A. L., Roessner, J. D., Jin, X. Y., & Newman, N. C. (2002). Measuring national ‘emerging technology’ capabilities. Science and Public Policy, 29(3), 189–200.

    Article  Google Scholar 

  19. Ranaei, S., Karvonen, M., Suominen, A., & Kässi, T. (2014). Forecasting emerging technologies of low emission vehicle. In Proceedings of the PICMET 2014 Conference (pp. 2924–2937).

    Google Scholar 

  20. Ruhnau, B. (2000). Eigenvector-centrality - a node-centrality? Social Networks, 22(4), 357–365.

    Article  Google Scholar 

  21. Small, H., Boyack, K., & Klavans, R. (2014). Identifying emerging topics in science and technology. Research Policy, 43(8), 1450–1467.

    Article  Google Scholar 

  22. Wang, Z., Porter, A. L., Wang, X., & Carley, S. (2019). An approach to identify emergent topics of technological convergence: A case study for 3D printing. Technological Forecasting and Social Change, 146, 723–732.

    Article  Google Scholar 

  23. Zhou, Y., Dong, F., Li, Z., Du, J., Liu, Y., & Zhang, L. (2020). Forecasting emerging technologies with deep learning and data augmentation. Scientometrics, 123, 1–29.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Cyber-Defence Campus, armasuisse Science and Technology, Thun, Switzerland

    Michael Tsesmelis, Ljiljana Dolamic & Alain Mermoud

  2. Military Academy at the Swiss Federal Institute of Technology Zurich, Birmensdorf, Switzerland

    Marcus M. Keupp

  3. Institute of Entrepreneurship and Management, University of Applied Sciences Valais, Sierre, Switzerland

    Dimitri Percia David

Authors
  1. Michael Tsesmelis
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  2. Ljiljana Dolamic
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  3. Marcus M. Keupp
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  4. Dimitri Percia David
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  5. Alain Mermoud
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Corresponding authors

Correspondence to Michael Tsesmelis or Dimitri Percia David .

Editor information

Editors and Affiliations

  1. Department Head, Department of Defense Economics, Military Academy at the Swiss Federal Institute of Technology, Zurich, Switzerland

    Marcus Matthias Keupp

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Tsesmelis, M., Dolamic, L., Keupp, M.M., Percia David, D., Mermoud, A. (2023). Identifying Emerging Technologies and Influential Companies Using Network Dynamics of Patent Clusters. In: Keupp, M.M. (eds) Cyberdefense. International Series in Operations Research & Management Science, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-031-30191-9_7

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