IDEAS home Printed from https://ideas.repec.org/a/oup/indcch/v30y2021i1p123-135..html
   My bibliography  Save this article

Sectoral systems of innovation in the era of the fourth industrial revolution: an introduction to the special section
[The magnitude of innovation by demand in a sectoral system: the role of industrial users in semiconductors]

Author

Listed:
  • Daitian Li
  • Zheng Liang
  • Fredrik Tell
  • Lan Xue

Abstract

The sectoral system framework has been adopted to study innovation and industrial dynamics in a wide range of sectors. However, recent development of the so-called “fourth industrial revolution” technologies, such as artificial intelligence, cloud computing, additive manufacturing, advanced robotics, Internet of Things, smartphones, and autonomous vehicles, has been blurring the boundaries of existing sectors, bringing both opportunities and challenges for sectoral systems research. In this introductory essay, we first provide a quick review on the sectoral systems of innovation literature, clarifying some conceptual and methodological issues. Then, we discuss how the fourth industrial revolution might reshape sectoral systems along three different dimensions (i.e. technological, market, and policy dimensions). Finally, we introduce articles in this special section and call for future research on this intriguing topic.

Suggested Citation

  • Daitian Li & Zheng Liang & Fredrik Tell & Lan Xue, 2021. "Sectoral systems of innovation in the era of the fourth industrial revolution: an introduction to the special section [The magnitude of innovation by demand in a sectoral system: the role of indust," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 30(1), pages 123-135.
    • Handle: RePEc:oup:indcch:v:30:y:2021:i:1:p:123-135.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/icc/dtaa064
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    Citations

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


    Cited by:

    1. Aleksey I. Shinkevich & Irina G. Ershova & Farida F. Galimulina, 2022. "Forecasting the Efficiency of Innovative Industrial Systems Based on Neural Networks," Mathematics, MDPI, vol. 11(1), pages 1-25, December.
    2. Mario Benassi & Elena Grinza & Francesco Rentocchini & Laura Rondi, 2022. "Patenting in 4IR technologies and firm performance [Robots and jobs: evidence from US labor markets]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 31(1), pages 112-136.

    More about this item

    JEL classification:

    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

    Statistics

    Access and download statistics

    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:oup:indcch:v:30:y:2021:i:1:p:123-135.. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/icc .

    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.