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How to combine term clumping and technology roadmapping for newly emerging science & technology competitive intelligence: “problem & solution” pattern based semantic TRIZ tool and case study

Author

Listed:
  • Yi Zhang

    (Beijing Institute of Technology)

  • Xiao Zhou

    (Beijing Institute of Technology)

  • Alan L. Porter

    (Georgia Institute of Technology
    Search Technology, Inc.)

  • Jose M. Vicente Gomila

    (Universitat Politecnica de Valencia)

Abstract

Competitive technical intelligence addresses the landscape of both opportunities and competition for emerging technologies, as the boom of newly emerging science & technology (NEST)—characterized by a challenging combination of great uncertainty and great potential—has become a significant feature of the globalized world. We have been focusing on the construction of a “NEST Competitive Intelligence” methodology that blends bibliometric and text mining methods to explore key technological system components, current R&D emphases, and key players for a particular NEST. This paper emphasizes the semantic TRIZ approach as a useful tool to process “Term Clumping” results to retrieve “problem & solution (P&S)” patterns, and apply them to technology roadmapping. We attempt to extend our approach into NEST Competitive Intelligence studies by using both inductive and purposive bibliometric approaches. Finally, an empirical study for dye-sensitized solar cells is used to demonstrate these analyses.

Suggested Citation

  • Yi Zhang & Xiao Zhou & Alan L. Porter & Jose M. Vicente Gomila, 2014. "How to combine term clumping and technology roadmapping for newly emerging science & technology competitive intelligence: “problem & solution” pattern based semantic TRIZ tool and case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1375-1389, November.
    • Handle: RePEc:spr:scient:v:101:y:2014:i:2:d:10.1007_s11192-014-1262-2
    DOI: 10.1007/s11192-014-1262-2
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    References listed on IDEAS

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    1. 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.
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    1. Vicente-Gomila, J.M. & Artacho-Ramírez, M.A. & Ting, Ma & Porter, A.L., 2021. "Combining tech mining and semantic TRIZ for technology assessment: Dye-sensitized solar cell as a case," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    2. Yang, Chao & Huang, Cui & Su, Jun, 2018. "An improved SAO network-based method for technology trend analysis: A case study of graphene," Journal of Informetrics, Elsevier, vol. 12(1), pages 271-286.
    3. Zhang, Yi & Huang, Ying & Porter, Alan L. & Zhang, Guangquan & Lu, Jie, 2019. "Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 795-807.
    4. Zhang, Yi & Robinson, Douglas K.R. & Porter, Alan L. & Zhu, Donghua & Zhang, Guangquan & Lu, Jie, 2016. "Technology roadmapping for competitive technical intelligence," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 175-186.
    5. Xuefeng Wang & Huichao Ren & Yun Chen & Yuqin Liu & Yali Qiao & Ying Huang, 2019. "Measuring patent similarity with SAO semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 1-23, October.
    6. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
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    8. Block, Carolin & Wustmans, Michael & Laibach, Natalie & Bröring, Stefanie, 2021. "Semantic bridging of patents and scientific publications – The case of an emerging sustainability-oriented technology," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    9. Ma, Tingting & Zhou, Xiao & Liu, Jia & Lou, Zhenkai & Hua, Zhaoting & Wang, Ruitao, 2021. "Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    10. Chao Yang & Donghua Zhu & Xuefeng Wang & Yi Zhang & Guangquan Zhang & Jie Lu, 2017. "Requirement-oriented core technological components’ identification based on SAO analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1229-1248, September.
    11. Ma, Jing & Abrams, Natalie F. & Porter, Alan L. & Zhu, Donghua & Farrell, Dorothy, 2019. "Identifying translational indicators and technology opportunities for nanomedical research using tech mining: The case of gold nanostructures," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 767-775.
    12. Xuefeng Wang & Pingping Ma & Ying Huang & Junfang Guo & Donghua Zhu & Alan L. Porter & Zhinan Wang, 2017. "Combining SAO semantic analysis and morphology analysis to identify technology opportunities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 3-24, April.
    13. Kyuwoong Kim & Kyeongmin Park & Sungjoo Lee, 2019. "Investigating technology opportunities: the use of SAOx analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 45-70, January.
    14. 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.
    15. Gaizka Garechana & Rosa Río-Belver & Enara Zarrabeitia & Izaskun Alvarez-Meaza, 2022. "TeknoAssistant : a domain specific tech mining approach for technical problem-solving support," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5459-5473, September.
    16. Jing Ma & Yaohui Pan & Chih-Yi Su, 2022. "Organization-oriented technology opportunities analysis based on predicting patent networks: a case of Alzheimer’s disease," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5497-5517, September.
    17. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
    18. Huang, Ying & Porter, Alan L. & Zhang, Yi & Lian, Xiangpeng & Guo, Ying, 2019. "An assessment of technology forecasting: Revisiting earlier analyses on dye-sensitized solar cells (DSSCs)," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 831-843.

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