IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v208y2024ics0040162524004633.html
   My bibliography  Save this article

Can science and technology resources co-evolve with high-tech industries? Empirical evidence from China

Author

Listed:
  • Luo, Ting
  • Zhang, Yongqing
  • Zheng, Minggui
  • Zheng, Sujiang
  • Gong, Yinyin

Abstract

Based on the theories of synergetic and evolutionary economics, we build a theoretical model of the co-evolutionary mechanisms of the composite systems of science and technology resources and high-tech industries in China. According to the established index system of the composite systems, we calculated the steady-state solution of the co-evolve and drew the potential function curve of science and technology resources and high-tech industries in China during 2010–2022 using the Haken model. Our empirical results show that high-tech industries are the order parameter of co-evolve. During the sample period, there is a synergistic growth effect of two-way promotion between science and technology resources and high-tech industries in China. However, within the system, the positive feedback mechanism of science and technology resources to high-tech industries in China is not perfect, and the allocation of science and technology resources needs to be further optimized, with a low degree of system order and large room for improvement. In addition, the endowment of science and technology resources and the development degree of high-tech industries in China are different in the eight comprehensive economic zones, and their co-evolvement is different. Therefore, by combining the advantages and allocating the elements of regional science and technology resources, we can better leverage the comparative advantages of high-tech industries in China and improve the degree of co-evolve in the system.

Suggested Citation

  • Luo, Ting & Zhang, Yongqing & Zheng, Minggui & Zheng, Sujiang & Gong, Yinyin, 2024. "Can science and technology resources co-evolve with high-tech industries? Empirical evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:tefoso:v:208:y:2024:i:c:s0040162524004633
    DOI: 10.1016/j.techfore.2024.123665
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162524004633
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2024.123665?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. Khoshnevis, Pegah & Teirlinck, Peter, 2018. "Performance evaluation of R&D active firms," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 16-28.
    2. Wang, Ya & Pan, Jiao-feng & Pei, Rui-min & Yi, Bo-Wen & Yang, Guo-liang, 2020. "Assessing the technological innovation efficiency of China's high-tech industries with a two-stage network DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    3. Castellacci, Fulvio & Natera, Jose Miguel, 2013. "The dynamics of national innovation systems: A panel cointegration analysis of the coevolution between innovative capability and absorptive capacity," Research Policy, Elsevier, vol. 42(3), pages 579-594.
    4. Chen, Zhuo & Yang, Zhenbing & Yang, Lili, 2020. "How to optimize the allocation of research resources? An empirical study based on output and substitution elasticities of universities in Chinese provincial level," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    5. Edmondson, Duncan L. & Kern, Florian & Rogge, Karoline S., 2019. "The co-evolution of policy mixes and socio-technical systems: Towards a conceptual framework of policy mix feedback in sustainability transitions," Research Policy, Elsevier, vol. 48(10).
    6. Zhang, Gongyi & Zhao, Shukuan & Xi, Yujuan & Liu, Na & Xu, Xiaobo, 2018. "Relating science and technology resources integration and polarization effect to innovation ability in emerging economies: An empirical study of Chinese enterprises," Technological Forecasting and Social Change, Elsevier, vol. 135(C), pages 188-198.
    7. Hong, Jin & Feng, Bing & Wu, Yanrui & Wang, Liangbing, 2016. "Do government grants promote innovation efficiency in China's high-tech industries?," Technovation, Elsevier, vol. 57, pages 4-13.
    8. Li, ChangZheng & Razzaq, Asif & Ozturk, Ilhan & Sharif, Arshian, 2023. "Natural resources, financial technologies, and digitalization: The role of institutional quality and human capital in selected OECD economies," Resources Policy, Elsevier, vol. 81(C).
    9. Haschka, Rouven E. & Herwartz, Helmut, 2020. "Innovation efficiency in European high-tech industries: Evidence from a Bayesian stochastic frontier approach," Research Policy, Elsevier, vol. 49(8).
    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. Miao, Cheng-lin & Duan, Meng-meng & Zuo, Yang & Wu, Xin-yu, 2021. "Spatial heterogeneity and evolution trend of regional green innovation efficiency--an empirical study based on panel data of industrial enterprises in China's provinces," Energy Policy, Elsevier, vol. 156(C).
    2. Yuanyuan Lin & Nianqi Deng & Hailian Gao, 2018. "Research on Technological Innovation Efficiency of Tourist Equipment Manufacturing Enterprises," Sustainability, MDPI, vol. 10(12), pages 1-17, December.
    3. Zhong, Meirui & Huang, Gangli & He, Ruifang, 2022. "The technological innovation efficiency of China's lithium-ion battery listed enterprises: Evidence from a three-stage DEA model and micro-data," Energy, Elsevier, vol. 246(C).
    4. Xueling Guan & Lijiang Chen & Qing Xia & Zhaohui Qin, 2022. "Innovation Efficiency of Chinese Pharmaceutical Manufacturing Industry from the Perspective of Innovation Ecosystem," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    5. Chen, Xiaolei & Guan, Zhongcheng & Yang, Guoliang & Pan, Hao & Xiong, Xi, 2024. "Evaluation of technology transfer performance for Chinese universities based on a dual-game cross-efficiency model," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    6. Yang, Zhenbing & Chen, Zhuo & Shao, Shuai & Yang, Lili, 2022. "Can housing price regulation improve R&D performance in universities? Evidence from China," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    7. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2021. "Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    8. Yunyao Li & Yanji Ma, 2022. "Research on Industrial Innovation Efficiency and the Influencing Factors of the Old Industrial Base Based on the Lock-In Effect, a Case Study of Jilin Province, China," Sustainability, MDPI, vol. 14(19), pages 1-23, October.
    9. Yongqi Feng & Haolin Zhang & Yung-ho Chiu & Tzu-Han Chang, 2021. "Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3091-3129, April.
    10. Othmani, Abdelhafidh & Ben Yedder, Nadia & Bakari, Sayef, 2023. "The Cointegration Relationship between Patent, Domestic Investment and Economic Growth in United States of America," MPRA Paper 118245, University Library of Munich, Germany.
    11. JinHyo Joseph Yun & DongKyu Won & ByungYong Hwang & JinWon Kang & DongHwan Kim, 2015. "Analysing and simulating the effects of open innovation policies: Application of the results to Cambodia," Science and Public Policy, Oxford University Press, vol. 42(6), pages 743-760.
    12. Montserrat Manzaneque & Alfonso A. Rojo-Ramírez & Julio Diéguez-Soto & Maria J. Martínez-Romero, 2020. "How negative aspiration performance gaps affect innovation efficiency," Small Business Economics, Springer, vol. 54(1), pages 209-233, January.
    13. Phung, Manh-Trung & Cheng, Cheng-Ping & Guo, Chuanyin & Kao, Chen-Yu, 2020. "Mixed Network DEA with Shared Resources: A Case of Measuring Performance for Banking Industry," Operations Research Perspectives, Elsevier, vol. 7(C).
    14. Zahedi Rad, Vahid & Seifi, Abbas & Fadai, Dawud, 2023. "Policy design for transition from imitation to innovation in emerging photovoltaic sectors using a system dynamics model," Energy Policy, Elsevier, vol. 183(C).
    15. Chen, Xiaoqing & Liu, Xinwang & Zhu, Qingyuan, 2022. "Comparative analysis of total factor productivity in China's high-tech industries," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    16. Geels, Frank W. & Ayoub, Martina, 2023. "A socio-technical transition perspective on positive tipping points in climate change mitigation: Analysing seven interacting feedback loops in offshore wind and electric vehicles acceleration," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    17. Jiang, Syuan-Yi, 2022. "Transition and innovation ecosystem – investigating technologies, focal actors, and institution in eHealth innovations," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    18. Ul-Durar, Shajara & De Sisto, Marco & Arshed, Noman & Naveed, Shabana & Farooqi, Madiha Rehman, 2024. "FinTech adoption in achieving ecologically sustainable mineral management in Asian OBOR countries – A cross-section and time autoregressive robust analysis," Resources Policy, Elsevier, vol. 91(C).
    19. Rouven E. Haschka & Helmut Herwartz, 2022. "Endogeneity in pharmaceutical knowledge generation: An instrument‐free copula approach for Poisson frontier models," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(4), pages 942-960, November.
    20. Yaliu Yang & Yuan Wang & Cui Wang & Yingyan Zhang & Cuixia Zhang, 2022. "Temporal and Spatial Evolution of the Science and Technology Innovative Efficiency of Regional Industrial Enterprises: A Data-Driven Perspective," Sustainability, MDPI, vol. 14(17), pages 1-21, August.

    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:eee:tefoso:v:208:y:2024:i:c:s0040162524004633. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.