IDEAS home Printed from https://ideas.repec.org/a/eee/techno/v129y2024ics0166497223001955.html
   My bibliography  Save this article

Reacting and recovering after an innovation failure. An agent-based approach

Author

Listed:
  • Ponta, Linda
  • Puliga, Gloria
  • Manzini, Raffaella
  • Cincotti, Silvano

Abstract

A company's growth depends not only on its achievements but also on how it can recover from failures. The study of innovation failure and learning-from-failure has gained attention over the years. Described as a complex problem, the dynamic of learning occurs as a non-linear phenomenon. Therefore, this study develops an agent-based model to examine and investigate, as a complex system, the impact on firms' performance of two main possible strategies of learning-from-failure, i.e. (1) the leveraging of the own experience and (2) the use of external resources. The findings suggest that embracing a learning-from-failure strategy in the innovation process enhances the firms' performance. In addition, the innovation intensity of the sector influences the impact of the strategy chosen. Comparing the use of internal vs external resources, the former seems to be a better strategy for enhancing the company's performance.

Suggested Citation

  • Ponta, Linda & Puliga, Gloria & Manzini, Raffaella & Cincotti, Silvano, 2024. "Reacting and recovering after an innovation failure. An agent-based approach," Technovation, Elsevier, vol. 129(C).
    • Handle: RePEc:eee:techno:v:129:y:2024:i:c:s0166497223001955
    DOI: 10.1016/j.technovation.2023.102884
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0166497223001955
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.technovation.2023.102884?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.

    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:techno:v:129:y:2024:i:c:s0166497223001955. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/01664972 .

    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.