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A hybrid approach to detecting technological recombination based on text mining and patent network analysis

Author

Listed:
  • Xiao Zhou

    (Xidian University)

  • Lu Huang

    (Beijing Institute of Technology)

  • Yi Zhang

    (University of Technology Sydney)

  • Miaomiao Yu

    (Beijing Institute of Technology)

Abstract

Detecting promising technology groups for recombination holds the promise of great value for R&D managers and technology policymakers, especially if the technologies in question can be detected before they have been combined. However, predicting the future is always easier said than done. In this regard, Arthur’s theory (The nature of technology: what it is and how it evolves, Free Press, New York, 2009) on the nature of technologies and how science evolves, coupled with Kuhn’s theory of scientific revolutions (Kuhn in The structure of scientific revolutions, 1st edn, University of Chicago Press, Chicago, p 3, 1962), may serve as the basis of a shrewd methodological framework for forecasting recombinative innovation. These theories help us to set out quantifiable criteria and decomposable steps to identify research patterns at each stage of a scientific revolution. The first step in the framework is to construct a conceptual model of the target technology domain, which helps to refine a reasonable search strategy. With the model built, the landscape of a field—its communities, its technologies, and their interactions—is fleshed out through community detection and network analysis based on a set of quantifiable criteria. The aim is to map normal patterns of research in the domain under study so as to highlight which technologies might contribute to a structural deepening of technological recombinations. Probability analysis helps to detect and group candidate technologies for possible recombination and further manual analysis by experts. To demonstrate how the framework works in practice, we conducted an empirical study on AI research in China. We explored the development potential of recombinative technologies by zooming in on the top patent assignees in the field and their innovations. In conjunction with expert analysis, the results reveal the cooperative and competitive relationships among these technology holders and opportunities for future innovation through technological recombinations.

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  • Xiao Zhou & Lu Huang & Yi Zhang & Miaomiao Yu, 2019. "A hybrid approach to detecting technological recombination based on text mining and patent network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 699-737, November.
  • Handle: RePEc:spr:scient:v:121:y:2019:i:2:d:10.1007_s11192-019-03218-5
    DOI: 10.1007/s11192-019-03218-5
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    References listed on IDEAS

    as
    1. Yongho Lee & So Young Kim & Inseok Song & Yongtae Park & Juneseuk Shin, 2014. "Technology opportunity identification customized to the technological capability of SMEs through two-stage patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 227-244, July.
    2. Danhao Zhu & Dongbo Wang & Saeed-Ul Hassan & Peter Haddawy, 2013. "Small-world phenomenon of keywords network based on complex network," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 435-442, November.
    3. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    4. Jacob Wood & Gohar Feroz Khan, 2015. "International trade negotiation analysis: network and semantic knowledge infrastructure," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 537-556, October.
    5. Xiao Zhou & Yi Zhang & Alan L. Porter & Ying Guo & Donghua Zhu, 2014. "A patent analysis method to trace technology evolutionary pathways," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 705-721, September.
    6. van den Bergh, Jeroen C.J.M., 2008. "Optimal diversity: Increasing returns versus recombinant innovation," Journal of Economic Behavior & Organization, Elsevier, vol. 68(3-4), pages 565-580, December.
    7. Byunghoon Kim & Gianluca Gazzola & Jae-Min Lee & Dohyun Kim & Kanghoe Kim & Myong K. Jeong, 2014. "Inter-cluster connectivity analysis for technology opportunity discovery," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1811-1825, March.
    8. Stephen F. Carley & Nils C. Newman & Alan L. Porter & Jon G. Garner, 2017. "A measure of staying power: Is the persistence of emergent concepts more significantly influenced by technical domain or scale?," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 2077-2087, June.
    9. Sungchul Choi & Janghyeok Yoon & Kwangsoo Kim & Jae Yeol Lee & Cheol-Han Kim, 2011. "SAO network analysis of patents for technology trends identification: a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(3), pages 863-883, September.
    10. Takano, Yasutomo & Mejia, Cristian & Kajikawa, Yuya, 2016. "Unconnected component inclusion technique for patent network analysis: Case study of Internet of Things-related technologies," Journal of Informetrics, Elsevier, vol. 10(4), pages 967-980.
    11. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    12. Zhang, Yi & Shang, Lining & Huang, Lu & Porter, Alan L. & Zhang, Guangquan & Lu, Jie & Zhu, Donghua, 2016. "A hybrid similarity measure method for patent portfolio analysis," Journal of Informetrics, Elsevier, vol. 10(4), pages 1108-1130.
    13. Dotsika, Fefie & Watkins, Andrew, 2017. "Identifying potentially disruptive trends by means of keyword network analysis," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 114-127.
    14. Faïz Gallouj, 1997. "Towards a neo-Schumpeterian theory of innovation in services?," Science and Public Policy, Oxford University Press, vol. 24(6), pages 405-420, December.
    15. Martin Zaltz Austwick & Oliver O’Brien & Emanuele Strano & Matheus Viana, 2013. "The Structure of Spatial Networks and Communities in Bicycle Sharing Systems," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-17, September.
    16. Christos Stergiou & Kostas E. Psannis, 2017. "Recent advances delivered by Mobile Cloud Computing and Internet of Things for Big Data applications: a survey," International Journal of Network Management, John Wiley & Sons, vol. 27(3), May.
    17. Gallouj, Faiz & Weinstein, Olivier, 1997. "Innovation in services," Research Policy, Elsevier, vol. 26(4-5), pages 537-556, December.
    18. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    19. Liaquat Hossain & Faezeh Karimi & Rolf T. Wigand & John W. Crawford, 2015. "Evolutionary longitudinal network dynamics of global zoonotic research," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 337-353, May.
    20. Corrocher Nicoletta & Malerba Franco & Montobbio Fabio, 2003. "The emergence of new technologies in the ICT field: main actors, geographical distribution and knowledge sources," Economics and Quantitative Methods qf0317, Department of Economics, University of Insubria.
    21. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
    22. Jon Sundbo & Faïz Gallouj, 1998. "Innovation as a loosely coupled system in services," Post-Print halshs-01113675, HAL.
    23. Guan, Jian Cheng & Yan, Yan, 2016. "Technological proximity and recombinative innovation in the alternative energy field," Research Policy, Elsevier, vol. 45(7), pages 1460-1473.
    24. Corrocher, Nicoletta & Zirulia, Lorenzo, 2010. "Demand and innovation in services: The case of mobile communications," Research Policy, Elsevier, vol. 39(7), pages 945-955, September.
    25. Oliver Williams & Charo I Del Genio, 2014. "Degree Correlations in Directed Scale-Free Networks," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-6, October.
    26. Zhigao Liu & Yimei Yin & Weidong Liu & Michael Dunford, 2015. "Visualizing the intellectual structure and evolution of innovation systems research: a bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 135-158, April.
    27. Yingxu Wang & Bernard Widrow & Lotfi A. Zadeh & Newton Howard & Sally Wood & Virendrakumar C. Bhavsar & Gerhard Budin & Christine Chan & Rodolfo A. Fiorini & Marina L. Gavrilova & Duane F. Shell, 2016. "Cognitive Intelligence: Deep Learning, Thinking, and Reasoning by Brain-Inspired Systems," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 10(4), pages 1-20, October.
    28. Yang, Siluo & Han, Ruizhen & Wolfram, Dietmar & Zhao, Yuehua, 2016. "Visualizing the intellectual structure of information science (2006–2015): Introducing author keyword coupling analysis," Journal of Informetrics, Elsevier, vol. 10(1), pages 132-150.
    29. Schilling, Melissa A. & Green, Elad, 2011. "Recombinant search and breakthrough idea generation: An analysis of high impact papers in the social sciences," Research Policy, Elsevier, vol. 40(10), pages 1321-1331.
    30. Nakamura, Hiroko & Suzuki, Shinji & Sakata, Ichiro & Kajikawa, Yuya, 2015. "Knowledge combination modeling: The measurement of knowledge similarity between different technological domains," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 187-201.
    31. Qian-Ru Zhang & Yue Li & Jia-Shu Liu & Yi-Dan Chen & Li-He Chai, 2017. "A dynamic co-word network-related approach on the evolution of China’s urbanization research," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1623-1642, June.
    32. Zhang, Yi & Porter, Alan L. & Hu, Zhengyin & Guo, Ying & Newman, Nils C., 2014. "“Term clumping” for technical intelligence: A case study on dye-sensitized solar cells," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 26-39.
    33. Lee Fleming & Olav Sorenson, 2004. "Science as a map in technological search," Strategic Management Journal, Wiley Blackwell, vol. 25(8‐9), pages 909-928, August.
    34. Choi, Jinho & Hwang, Yong-Sik, 2014. "Patent keyword network analysis for improving technology development efficiency," Technological Forecasting and Social Change, Elsevier, vol. 83(C), pages 170-182.
    35. Corredoira, Rafael A. & Banerjee, Preeta M., 2015. "Measuring patent's influence on technological evolution: A study of knowledge spanning and subsequent inventive activity," Research Policy, Elsevier, vol. 44(2), pages 508-521.
    36. Marc Gruber & Dietmar Harhoff & Karin Hoisl, 2013. "Knowledge Recombination Across Technological Boundaries: Scientists vs. Engineers," Management Science, INFORMS, vol. 59(4), pages 837-851, April.
    37. Park, Yongtae & Yoon, Byungun & Lee, Sungjoo, 2005. "The idiosyncrasy and dynamism of technological innovation across industries: patent citation analysis," Technology in Society, Elsevier, vol. 27(4), pages 471-485.
    38. Stephen F. Carley & Nils C. Newman & Alan L. Porter & Jon G. Garner, 2018. "An indicator of technical emergence," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 35-49, April.
    39. Yi Zhang & Guangquan Zhang & Donghua Zhu & Jie Lu, 2017. "Scientific evolutionary pathways: Identifying and visualizing relationships for scientific topics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(8), pages 1925-1939, August.
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