IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v25y2023i6d10.1007_s10668-022-02253-2.html
   My bibliography  Save this article

Research on the promotion path of green technology innovation of an enterprise from the perspective of technology convergence: configuration analysis using new energy vehicles as an example

Author

Listed:
  • Kai Guo

    (Henan University of Science & Technology
    Henan Collaborative Innovation Center of Nonferrous Metals)

  • Tiantian Zhang

    (Henan University of Science & Technology)

  • Yan Liang

    (Henan University of Science & Technology)

  • Jiyao Zhao

    (University of Birmingham Joint Institute at Jinan University, Jinan University)

  • Xiangmin Zhang

    (Henan University of Science & Technology
    Henan Collaborative Innovation Center of Nonferrous Metals)

Abstract

In this study, taking the field of new energy vehicles as an example, the green patent data have been obtained from the smart bud patent database, and 243 organizations have been selected as the research samples based on their technical convergence and lack of data of their patents. From the perspective of technology convergence, five factors, including its degree of convergence, influence, knowledge maturity, experience, and originality, have been selected, and the fuzzy-set qualitative comparative analysis technology has been employed for conducting a configuration analysis. It has been found that there are three high green technological innovation paths, namely, the ‘high convergence-high cognitive legitimacy’ type, the ‘high convergence-high influence’ type, and the ‘high convergence-high quality’ type. In addition, five low green technological innovation paths have been obtained, which can be divided into three types, including the ‘low convergence-high impact’ type, the ‘high convergence-low experience’ type, and the ‘high convergence-low quality’ type. Depending on the low green technological innovation paths, the steps of configuring the specific conditions of the included samples and matching them with the most suitable upgrade path should be performed in order to achieve a quick and efficient improvement in the green technology innovation level of an enterprise.

Suggested Citation

  • Kai Guo & Tiantian Zhang & Yan Liang & Jiyao Zhao & Xiangmin Zhang, 2023. "Research on the promotion path of green technology innovation of an enterprise from the perspective of technology convergence: configuration analysis using new energy vehicles as an example," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(6), pages 4989-5008, June.
    • Handle: RePEc:spr:endesu:v:25:y:2023:i:6:d:10.1007_s10668-022-02253-2
    DOI: 10.1007/s10668-022-02253-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-022-02253-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-022-02253-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Minyoung Kim, 2013. "Many roads lead to Rome: Implications of geographic scope as a source of isolating mechanisms," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 44(9), pages 898-921, December.
    2. Rene Kemp & Saeed Parto & Robert B. Gibson, 2005. "Governance for sustainable development: moving from theory to practice," International Journal of Sustainable Development, Inderscience Enterprises Ltd, vol. 8(1/2), pages 12-30.
    3. Jeeeun Kim & Sungjoo Lee, 2017. "Forecasting and identifying multi-technology convergence based on patent data: the case of IT and BT industries in 2020," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 47-65, April.
    4. Song, Chie Hoon & Elvers, David & Leker, Jens, 2017. "Anticipation of converging technology areas — A refined approach for the identification of attractive fields of innovation," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 98-115.
    5. Wang, Zhinan & Porter, Alan L. & Wang, Xuefeng & Carley, Stephen, 2019. "An approach to identify emergent topics of technological convergence: A case study for 3D printing," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 723-732.
    6. Ying Tang & Xuming Lou & Zifeng Chen & Chengjin Zhang, 2020. "A Study on Dynamic Patterns of Technology Convergence with IPC Co-Occurrence-Based Analysis: The Case of 3D Printing," Sustainability, MDPI, vol. 12(7), pages 1-26, March.
    7. Wurlod, Jules-Daniel & Noailly, Joëlle, 2018. "The impact of green innovation on energy intensity: An empirical analysis for 14 industrial sectors in OECD countries," Energy Economics, Elsevier, vol. 71(C), pages 47-61.
    8. Manuel Trajtenberg & Rebecca Henderson & Adam Jaffe, 1997. "University Versus Corporate Patents: A Window On The Basicness Of Invention," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 5(1), pages 19-50.
    9. Keijl, S. & Gilsing, V.A. & Knoben, J. & Duysters, G., 2016. "The two faces of inventions: The relationship between recombination and impact in pharmaceutical biotechnology," Research Policy, Elsevier, vol. 45(5), pages 1061-1074.
    10. Jian Cheng Guan & Xia Gao, 2009. "Exploring the h‐index at patent level," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(1), pages 35-40, January.
    11. Du, Jian & Li, Peixin & Guo, Qianying & Tang, Xiaoli, 2019. "Measuring the knowledge translation and convergence in pharmaceutical innovation by funding-science-technology-innovation linkages analysis," Journal of Informetrics, Elsevier, vol. 13(1), pages 132-148.
    12. Jiwon Yu & Jong-Gyu Hwang & Jumi Hwang & Sung Chan Jun & Sumin Kang & Chulung Lee & Hyundong Kim, 2020. "Identification of Vacant and Emerging Technologies in Smart Mobility Through the GTM-Based Patent Map Development," Sustainability, MDPI, vol. 12(22), pages 1-22, November.
    13. Song, Bomi & Suh, Yongyoon, 2019. "Identifying convergence fields and technologies for industrial safety: LDA-based network analysis," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 115-126.
    14. Kose, Toshihiro & Sakata, Ichiro, 2019. "Identifying technology convergence in the field of robotics research," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 751-766.
    15. Chul Lee & Gunno Park & Jina Kang, 2018. "The impact of convergence between science and technology on innovation," The Journal of Technology Transfer, Springer, vol. 43(2), pages 522-544, April.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. Qian Xu & Hua Cheng, 2021. "Research on the Evolution of Textile Technological Convergence in China," Sustainability, MDPI, vol. 13(5), pages 1-13, February.
    3. Seo, Wonchul & Afifuddin, Mokh, 2024. "Developing a supervised learning model for anticipating potential technology convergence between technology topics," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    4. Park, Mingyu & Geum, Youngjung, 2022. "Two-stage technology opportunity discovery for firm-level decision making: GCN-based link-prediction approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    5. Ying Tang & Xuming Lou & Zifeng Chen & Chengjin Zhang, 2020. "A Study on Dynamic Patterns of Technology Convergence with IPC Co-Occurrence-Based Analysis: The Case of 3D Printing," Sustainability, MDPI, vol. 12(7), pages 1-26, March.
    6. Sick, Nathalie & Bröring, Stefanie, 2022. "Exploring the research landscape of convergence from a TIM perspective: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    7. Jong Wook Lee & So Young Sohn, 2021. "Patent data based search framework for IT R&D employees for convergence technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5687-5705, July.
    8. ZHU Chen & MOTOHASHI Kazuyuki, 2022. "Government R&D spending as a driving force of technology convergence," Discussion papers 22030, Research Institute of Economy, Trade and Industry (RIETI).
    9. Lee, Chien-Chiang & Qin, Shuai & Li, Yaya, 2022. "Does industrial robot application promote green technology innovation in the manufacturing industry?," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    10. Li, Jing & Yu, Qian, 2024. "Scientists’ disciplinary characteristics and collaboration behaviour under the convergence paradigm: A multilevel network perspective," Journal of Informetrics, Elsevier, vol. 18(1).
    11. Sasaki, Hajime & Sakata, Ichiro, 2021. "Identifying potential technological spin-offs using hierarchical information in international patent classification," Technovation, Elsevier, vol. 100(C).
    12. Chand Bhatt, Priyanka & Kumar, Vimal & Lu, Tzu-Chuen & Daim, Tugrul, 2021. "Technology convergence assessment: Case of blockchain within the IR 4.0 platform," Technology in Society, Elsevier, vol. 67(C).
    13. Sajad Ashouri & Anne-Laure Mention & Kosmas X. Smyrnios, 2021. "Anticipation and analysis of industry convergence using patent-level indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5727-5758, July.
    14. Kim, Tae San & Sohn, So Young, 2020. "Machine-learning-based deep semantic analysis approach for forecasting new technology convergence," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    15. Juite Wang & Tzu-Yen Hsu, 2023. "Early discovery of emerging multi-technology convergence for analyzing technology opportunities from patent data: the case of smart health," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4167-4196, August.
    16. 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.
    17. Wang, Fang, 2024. "Does the recombination of distant scientific knowledge generate valuable inventions? An analysis of pharmaceutical patents," Technovation, Elsevier, vol. 130(C).
    18. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    19. Chen Zhu & Kazuyuki Motohashi, 2023. "Government R&D spending as a driving force of technology convergence: a case study of the Advanced Sequencing Technology Program," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 3035-3065, May.
    20. Jie Liu & Arnulf Grubler & Tieju Ma & Dieter F. Kogler, 2021. "Identifying the technological knowledge depreciation rate using patent citation data: a case study of the solar photovoltaic industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 93-115, January.

    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:spr:endesu:v:25:y:2023:i:6:d:10.1007_s10668-022-02253-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.