IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i8p2928-d349530.html
   My bibliography  Save this article

Idea Generation and New Direction for Exploitation Technologies of Coal-Seam Gas through Recombinative Innovation and Patent Analysis

Author

Listed:
  • Lijie Feng

    (School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China
    School of Economic & Management, Shanghai Maritime University, Shanghai 201306, China)

  • Yilang Li

    (School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Zhenfeng Liu

    (School of Economic & Management, Shanghai Maritime University, Shanghai 201306, China)

  • Jinfeng Wang

    (School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China
    School of Economic & Management, Shanghai Maritime University, Shanghai 201306, China)

Abstract

Coal-seam gas (CSG), as an alternative energy, has the characteristics of resource scarcity and technological exploitation complexity. The generation of ideas is vital to develop more efficient exploitation technologies for CSG. Innovative ideas originate from the recombination of existing knowledge elements according to recombinative innovation. The previous literature has focused on exploring an abundance of combinations, which leads to blindness towards idea generation. For this reason, it is critical to search for more valuable matching patterns among the redundant combinations of elements. In line with this concept, this paper proposes a method that consists of three phases: the collection of knowledge elements, the analysis of knowledge element depth and diversity, and the analysis of knowledge element relationships. In this process, we take the patent document as the carrier of knowledge recombination and identify the optimization method in the reorganization process by means of latent Dirichlet allocation (LDA) and association rules. This method is expected to assist in sparking better ideas for CSG exploitation technologies.

Suggested Citation

  • Lijie Feng & Yilang Li & Zhenfeng Liu & Jinfeng Wang, 2020. "Idea Generation and New Direction for Exploitation Technologies of Coal-Seam Gas through Recombinative Innovation and Patent Analysis," IJERPH, MDPI, vol. 17(8), pages 1-21, April.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:8:p:2928-:d:349530
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/8/2928/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/8/2928/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guan, Jiancheng & Liu, Na, 2016. "Exploitative and exploratory innovations in knowledge network and collaboration network: A patent analysis in the technological field of nano-energy," Research Policy, Elsevier, vol. 45(1), pages 97-112.
    2. Donghyun Choi & Bomi Song, 2018. "Exploring Technological Trends in Logistics: Topic Modeling-Based Patent Analysis," Sustainability, MDPI, vol. 10(8), pages 1-26, August.
    3. An, Jaehyeong & Kim, Kyuwoong & Mortara, Letizia & Lee, Sungjoo, 2018. "Deriving technology intelligence from patents: Preposition-based semantic analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 217-236.
    4. Corey C. Phelps & Ralph Heidl & Anu Wadhwa, 2012. "Networks, knowledge, and knowledge networks: A critical review and research agenda," Post-Print hal-00715591, HAL.
    5. Dosi, Giovanni, 1993. "Technological paradigms and technological trajectories : A suggested interpretation of the determinants and directions of technical change," Research Policy, Elsevier, vol. 22(2), pages 102-103, April.
    6. Janghyeok Yoon & Kwangsoo Kim, 2012. "Detecting signals of new technological opportunities using semantic patent analysis and outlier detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 445-461, February.
    7. 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.
    8. Gallouj, Faiz & Weinstein, Olivier, 1997. "Innovation in services," Research Policy, Elsevier, vol. 26(4-5), pages 537-556, December.
    9. Kim, Gabjo & Bae, Jinwoo, 2017. "A novel approach to forecast promising technology through patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 228-237.
    10. Zhenfeng Liu & Jian Feng & Bin Liu, 2019. "Pricing and Service Level Decisions under a Sharing Product and Consumers’ Variety-Seeking Behavior," Sustainability, MDPI, vol. 11(24), pages 1-16, December.
    11. Scot M. Miller & Anna M. Michalak & Robert G. Detmers & Otto P. Hasekamp & Lori M. P. Bruhwiler & Stefan Schwietzke, 2019. "China’s coal mine methane regulations have not curbed growing emissions," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    12. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
    13. 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.
    14. 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.
    15. Zhenfeng Liu & Jian Feng & Jinfeng Wang, 2020. "Resource-Constrained Innovation Method for Sustainability: Application of Morphological Analysis and TRIZ Inventive Principles," Sustainability, MDPI, vol. 12(3), pages 1-23, January.
    16. Zhenfeng Liu & Jian Feng & Jinfeng Wang, 2019. "Effects of the Sharing Economy on Sequential Innovation Products," Complexity, Hindawi, vol. 2019, pages 1-18, January.
    17. Schellner, Irene, 2002. "Japanese File Index classification and F-terms," World Patent Information, Elsevier, vol. 24(3), pages 197-201, September.
    18. 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.
    19. 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.
    20. Kwon, Heeyeul & Park, Yongtae & Geum, Youngjung, 2018. "Toward data-driven idea generation: Application of Wikipedia to morphological analysis," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 56-80.
    21. Quintana-Garci­a, Cristina & Benavides-Velasco, Carlos A., 2008. "Innovative competence, exploration and exploitation: The influence of technological diversification," Research Policy, Elsevier, vol. 37(3), pages 492-507, April.
    22. 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.
    23. 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.
    24. Boh, Wai Fong & Evaristo, Roberto & Ouderkirk, Andrew, 2014. "Balancing breadth and depth of expertise for innovation: A 3M story," Research Policy, Elsevier, vol. 43(2), pages 349-366.
    25. Song, Kisik & Kim, Karp Soo & Lee, Sungjoo, 2017. "Discovering new technology opportunities based on patents: Text-mining and F-term analysis," Technovation, Elsevier, vol. 60, pages 1-14.
    26. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Jinfeng & Zhang, Zhixin & Feng, Lijie & Lin, Kuo-Yi & Liu, Peng, 2023. "Development of technology opportunity analysis based on technology landscape by extending technology elements with BERT and TRIZ," Technological Forecasting and Social Change, Elsevier, vol. 191(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Yan, Hong-Bin & Li, Ming, 2022. "Consumer demand based recombinant search for idea generation," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. Brennecke, Julia & Rank, Olaf, 2017. "The firm’s knowledge network and the transfer of advice among corporate inventors—A multilevel network study," Research Policy, Elsevier, vol. 46(4), pages 768-783.
    5. Zhang, JingJing & Yan, Yan & Guan, JianCheng, 2019. "Recombinant distance, network governance and recombinant innovation," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 260-272.
    6. Maïder SAINT-JEAN & Nabila ARFAOUI & Eric BROUILLAT & David VIRAPIN, 2019. "Mapping technological knowledge patterns: evidence from ocean energy technologies," Cahiers du GREThA 2019-09, Groupe de Recherche en Economie Théorique et Appliquée(GREThA).
    7. Zhao, Shengchao & Zeng, Deming & Li, Jian & Feng, Ke & Wang, Yao, 2023. "Quantity or quality: The roles of technology and science convergence on firm innovation performance," Technovation, Elsevier, vol. 126(C).
    8. Appio, Francesco Paolo & Martini, Antonella & Fantoni, Gualtiero, 2017. "The light and shade of knowledge recombination: Insights from a general-purpose technology," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 154-165.
    9. John-Paul Ferguson & Gianluca Carnabuci, 2017. "Risky Recombinations: Institutional Gatekeeping in the Innovation Process," Organization Science, INFORMS, vol. 28(1), pages 133-151, February.
    10. Zhang, Zhengang & Luo, Taiye, 2020. "Network capital, exploitative and exploratory innovations——from the perspective of network dynamics," Technological Forecasting and Social Change, Elsevier, vol. 152(C).
    11. Plantec, Quentin & Le Masson, Pascal & Weil, Benoît, 2021. "Impact of knowledge search practices on the originality of inventions: A study in the oil & gas industry through dynamic patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    12. Chen, Wei & Yan, Yan, 2023. "New components and combinations: The perspective of the internal collaboration networks of scientific teams," Journal of Informetrics, Elsevier, vol. 17(2).
    13. Zhang, Guiyang & Tang, Chaoying, 2017. "How could firm's internal R&D collaboration bring more innovation?," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 299-308.
    14. Guiyang Zhang & Chaoying Tang, 2018. "How R&D partner diversity influences innovation performance: an empirical study in the nano-biopharmaceutical field," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1487-1512, September.
    15. Hoppmann, Joern & Wu, Geng & Johnson, Jillian, 2021. "The impact of demand-pull and technology-push policies on firms’ knowledge search," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    16. Martin Kalthaus, 2020. "Knowledge recombination along the technology life cycle," Journal of Evolutionary Economics, Springer, vol. 30(3), pages 643-704, July.
    17. Dibiaggio, Ludovic & Nasiriyar, Maryam & Nesta, Lionel, 2014. "Substitutability and complementarity of technological knowledge and the inventive performance of semiconductor companies," Research Policy, Elsevier, vol. 43(9), pages 1582-1593.
    18. Choi, Jin-Uk & Lee, Chang-Yang, 2022. "The differential effects of basic research on firm R&D productivity: The conditioning role of technological diversification," Technovation, Elsevier, vol. 118(C).
    19. Guan, JianCheng & Zhang, JingJing, 2018. "The dynamics of partner and knowledge portfolios in alternative energy field," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2869-2879.
    20. Jiao, Hao & Wang, Tang & Yang, Jifeng, 2022. "Team structure and invention impact under high knowledge diversity: An empirical examination of computer workstation industry," Technovation, Elsevier, vol. 114(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:8:p:2928-:d:349530. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.