IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v135y2018icp66-74.html
   My bibliography  Save this article

China's manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0”

Author

Listed:
  • Li, Ling

Abstract

In this study, we have compared Germany's “Industry 4.0” and China's “Made-in-China 2025” and estimated China's locus in “Made-in-China 2025”. “Made-in-China 2025” has clear goals, measures and sector focus. Its guiding principles are to enhance industrial capability through innovation-driven manufacturing, optimize the structure of Chinese industry, emphasize quality over quantity, train and attract talent, and achieve green manufacturing and environment. Data show that currently China is no longer the lowest–cost labor market; it is being squeezed by newly emerging low-cost producers such as Vietnam, Cambodia, and Laos. Meanwhile, China is not the strongest player in the high-tech arena; well-established industrialized nations, the US, Germany, and Japan, have all effectively deployed digital technology to create new industrial environments, produce new products, and improve their well-established brands. Having analyzed the data from the World Bank and China's National Bureau of Statistics, we find an upward trajectory in China in manufacturing capability development, research and development commitment, and human capital investment. However, implementing an ambitious strategic plan such as “Made-in-China 2025” is coupled with challenges. This research helps us understand the relationship between technological entrepreneurship and socio-economic changes in emerging economies such as China. Furthermore, the experience accumulated in China can be referenced by both emerging economies and developed nations to advance their technological entrepreneurship.

Suggested Citation

  • Li, Ling, 2018. "China's manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0”," Technological Forecasting and Social Change, Elsevier, vol. 135(C), pages 66-74.
    • Handle: RePEc:eee:tefoso:v:135:y:2018:i:c:p:66-74
    DOI: 10.1016/j.techfore.2017.05.028
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162517307254
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2017.05.028?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. Jiang, Ruth & Kleer, Robin & Piller, Frank T., 2017. "Predicting the future of additive manufacturing: A Delphi study on economic and societal implications of 3D printing for 2030," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 84-97.
    2. Li, Ling, 2013. "The path to Made-in-China: How this was done and future prospects," International Journal of Production Economics, Elsevier, vol. 146(1), pages 4-13.
    3. Popa, Simona & Soto-Acosta, Pedro & Martinez-Conesa, Isabel, 2017. "Antecedents, moderators, and outcomes of innovation climate and open innovation: An empirical study in SMEs," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 134-142.
    4. Su, Hsin-Ning, 2017. "Collaborative and Legal Dynamics of International R&D- Evolving Patterns in East Asia," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 217-227.
    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. Zhu, Lin & Cunningham, Scott W., 2022. "Unveiling the knowledge structure of technological forecasting and social change (1969–2020) through an NMF-based hierarchical topic model," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    2. Lütkenhorst, Wilfried, 2018. "Creating wealth without labour? Emerging contours of a new techno-economic landscape," IDOS Discussion Papers 11/2018, German Institute of Development and Sustainability (IDOS).
    3. Di Zio, Simone & Bolzan, Mario & Marozzi, Marco, 2021. "Classification of Delphi outputs through robust ranking and fuzzy clustering for Delphi-based scenarios," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    4. Florinda Matos & Radu Godina & Celeste Jacinto & Helena Carvalho & Inês Ribeiro & Paulo Peças, 2019. "Additive Manufacturing: Exploring the Social Changes and Impacts," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
    5. Lingyan Meng & Md Qamruzzaman & Anass Hamad Elneel Adow, 2021. "Technological Adaption and Open Innovation in SMEs: An Strategic Assessment for Women-Owned SMEs Sustainability in Bangladesh," Sustainability, MDPI, vol. 13(5), pages 1-22, March.
    6. Pauget, Bertrand & Tobelem, Jean-Michel & Bootz, Jean-Philippe, 2021. "The future of French museums in 2030," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    7. Saeed, Tariq Usman & Burris, Mark W. & Labi, Samuel & Sinha, Kumares C., 2020. "An empirical discourse on forecasting the use of autonomous vehicles using consumers’ preferences," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    8. David M. Goldberg & Jason K. Deane & Terry R. Rakes & Loren Paul Rees, 2022. "3D Printing Technology and the Market Value of the Firm," Information Systems Frontiers, Springer, vol. 24(4), pages 1379-1392, August.
    9. De Moortel, Kevin & Crispeels, Thomas, 2018. "International university-university technology transfer: Strategic management framework," Technological Forecasting and Social Change, Elsevier, vol. 135(C), pages 145-155.
    10. Marzi, Giacomo & Fakhar Manesh, Mohammad & Caputo, Andrea & Pellegrini, Massimiliano Matteo & Vlačić, Božidar, 2023. "Do or do not. Cognitive configurations affecting open innovation adoption in SMEs," Technovation, Elsevier, vol. 119(C).
    11. JUSTIN DORAN & NOIRIN McCARTHY & MARIE O’CONNOR, 2019. "The Importance Of Internal Knowledge Generation And External Knowledge Sourcing For Sme Innovation And Performance: Evidence From Ireland," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 23(07), pages 1-30, October.
    12. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    13. Călin Florin Băban & Marius Băban, 2022. "An Orchestration Perspective on Open Innovation between Industry–University: Investigating Its Impact on Collaboration Performance," Mathematics, MDPI, vol. 10(15), pages 1-23, July.
    14. Kimberley Slater & John Robinson, 2020. "Social Learning and Transdisciplinary Co-Production: A Social Practice Approach," Sustainability, MDPI, vol. 12(18), pages 1-16, September.
    15. Arias-Pérez, José & Vélez-Jaramillo, Juan, 2022. "Ignoring the three-way interaction of digital orientation, Not-invented-here syndrome and employee's artificial intelligence awareness in digital innovation performance: A recipe for failure," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    16. Yu, Xiaoyu & Tao, Yida & Tao, Xiangming & Xia, Fan & Li, Yajie, 2018. "Managing uncertainty in emerging economies: The interaction effects between causation and effectuation on firm performance," Technological Forecasting and Social Change, Elsevier, vol. 135(C), pages 121-131.
    17. Shang, Juan & Li, Pengfei & Li, Ling & Chen, Yong, 2018. "The relationship between population growth and capital allocation in urbanization," Technological Forecasting and Social Change, Elsevier, vol. 135(C), pages 249-256.
    18. Naghshineh, Bardia & Ribeiro, André & Jacinto, Celeste & Carvalho, Helena, 2021. "Social impacts of additive manufacturing: A stakeholder-driven framework," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    19. Bürgel, Tobias R. & Hiebl, Martin R.W. & Pielsticker, David I., 2023. "Digitalization and entrepreneurial firms' resilience to pandemic crises: Evidence from COVID-19 and the German Mittelstand," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    20. Barrios, Maite & Guilera, Georgina & Nuño, Laura & Gómez-Benito, Juana, 2021. "Consensus in the delphi method: What makes a decision change?," Technological Forecasting and Social Change, Elsevier, vol. 163(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:eee:tefoso:v:135:y:2018:i:c:p:66-74. 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.