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Discovering Potential Technology Opportunities for Fuel Cell Vehicle Firms: A Multi-Level Patent Portfolio-Based Approach

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  • Xuan Shi

    (School of Economics and Management, Shanxi University, Taiyuan 030006, China)

  • Lingfei Cai

    (College of Information and Computer Science, Shanghai Business School, Shanghai 201400, China)

  • Hongfang Song

    (School of Management Science and Engineering, Hebei University of Business and Economics, Shijiazhuang 050061, China)

Abstract

Technology opportunity discovery (TOD) is an important technique to help fuel cell vehicle (FCV) firms keep market advantage and sustainable development. Under fierce competition in the new energy industry, there is an urgent necessity for innovative TOD methods to effectively identify technology opportunities for FCV firms. This study proposes a structured TOD framework with a multi-level identification process. Based on technology portfolio analysis, it fully integrates the firm’s technology level analysis, technology potential analysis and patent novelty analysis. A series of techniques such as LDA (latent Dirichlet allocation), MDS (multidimensional scaling) and LOF (local outlier factor) are also applied in the framework. A total of 14,858 granted patent data of the FCV industry containing 798 patents of the target firm were extracted from the Derwent Innovation Index database as the input data of the empirical study. The result shows that the framework can provide a more profound analysis for identifying technology opportunities, which offer more appropriate insights in both strategic and operational level technological decisions for technology-oriented firms.

Suggested Citation

  • Xuan Shi & Lingfei Cai & Hongfang Song, 2019. "Discovering Potential Technology Opportunities for Fuel Cell Vehicle Firms: A Multi-Level Patent Portfolio-Based Approach," Sustainability, MDPI, vol. 11(22), pages 1-22, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:22:p:6381-:d:286545
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    References listed on IDEAS

    as
    1. Yoon, Janghyeok & Park, Hyunseok & Seo, Wonchul & Lee, Jae-Min & Coh, Byoung-youl & Kim, Jonghwa, 2015. "Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 153-167.
    2. Fabry, Bernd & Ernst, Holger & Langholz, Jens & Köster, Martin, 2006. "Patent portfolio analysis as a useful tool for identifying R&D and business opportunities--an empirical application in the nutrition and health industry," World Patent Information, Elsevier, vol. 28(3), pages 215-225, September.
    3. Janghyeok Yoon & Hyunseok Park & Kwangsoo Kim, 2013. "Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 313-331, January.
    4. Yoon, Byungun & Magee, Christopher L., 2018. "Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 105-117.
    5. Lee, Jeongjin & Kim, Changseok & Shin, Juneseuk, 2017. "Technology opportunity discovery to R&D planning: Key technological performance analysis," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 53-63.
    6. Park, Youngjin & Yoon, Janghyeok, 2017. "Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 170-183.
    7. Lee, Changyong & Kang, Bokyoung & Shin, Juneseuk, 2015. "Novelty-focused patent mapping for technology opportunity analysis," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 355-365.
    8. Marti Sagarra & Cecilio Mar-Molinero & Miguel García-Cestona, 2015. "Spanish savings banks in the credit crunch: could distress have been predicted before the crisis? A multivariate statistical analysis," The European Journal of Finance, Taylor & Francis Journals, vol. 21(3), pages 195-214, February.
    9. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    10. 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.
    11. Jeong, Yujin & Park, Inchae & Yoon, Byungun, 2019. "Identifying emerging Research and Business Development (R&BD) areas based on topic modeling and visualization with intellectual property right data," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 655-672.
    12. Ha, Sung Ho & Liu, Weina & Cho, Hune & Kim, Sang Hyun, 2015. "Technological advances in the fuel cell vehicle: Patent portfolio management," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 277-289.
    13. Momeni, Abdolreza & Rost, Katja, 2016. "Identification and monitoring of possible disruptive technologies by patent-development paths and topic modeling," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 16-29.
    14. Seo, Wonchul & Yoon, Janghyeok & Park, Hyunseok & Coh, Byoung-youl & Lee, Jae-Min & Kwon, Oh-Jin, 2016. "Product opportunity identification based on internal capabilities using text mining and association rule mining," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 94-104.
    15. Byeongki Jeong & Janghyeok Yoon, 2017. "Competitive Intelligence Analysis of Augmented Reality Technology Using Patent Information," Sustainability, MDPI, vol. 9(4), pages 1-22, March.
    16. Mun, Changbae & Kim, Yongmin & Yoo, Donghyun & Yoon, Sejun & Hyun, Heesu & Raghavan, Nagarajan & Park, Hyunseok, 2019. "Discovering business diversification opportunities using patent information and open innovation cases," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 144-154.
    17. William Acar & Kizhekepat Sankaran, 1999. "The myth of the unique decomposability: specializing the Herfindahl and entropy measures?," Strategic Management Journal, Wiley Blackwell, vol. 20(10), pages 969-975, October.
    18. Bo Wang & Shengbo Liu & Kun Ding & Zeyuan Liu & Jing Xu, 2014. "Identifying technological topics and institution-topic distribution probability for patent competitive intelligence analysis: a case study in LTE technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 685-704, October.
    19. Chang, Shu-Hao & Fan, Chin-Yuan, 2016. "Identification of the technology life cycle of telematics: A patent-based analytical perspective," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 1-10.
    20. Uchida, Yuichiro & Cook, Paul, 2005. "The Transformation of Competitive Advantage in East Asia: An Analysis of Technological and Trade Specialization," World Development, Elsevier, vol. 33(5), pages 701-728, May.
    21. Yoon, Byungun & Park, Inchae & Coh, Byoung-youl, 2014. "Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 287-303.
    22. Haupt, Reinhard & Kloyer, Martin & Lange, Marcus, 2007. "Patent indicators for the technology life cycle development," Research Policy, Elsevier, vol. 36(3), pages 387-398, April.
    23. Ardito, Lorenzo & D'Adda, Diego & Messeni Petruzzelli, Antonio, 2018. "Mapping innovation dynamics in the Internet of Things domain: Evidence from patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 317-330.
    24. 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.
    25. Hei Chia Wang & Yung Chang Chi & Ping Lun Hsin, 2018. "Constructing Patent Maps Using Text Mining to Sustainably Detect Potential Technological Opportunities," Sustainability, MDPI, vol. 10(10), pages 1-18, October.
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