IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v141y2025ics0140988324007576.html
   My bibliography  Save this article

The effects of Artificial intelligence orientation on inefficient investment: Firm-level evidence from China's energy enterprises

Author

Listed:
  • Zhai, Minhan
  • Wu, Wenqing
  • Tsai, Sang-Bing

Abstract

The development of Artificial Intelligence (AI) has brought both opportunities and challenges for energy enterprises to make investment decisions. This paper considers an Artificial intelligence orientation (AIO) indicator that reflects AI introduction and deployment to analyze whether and how AIO affects inefficient investment in energy enterprises. By using machine learning methods to construct AIO indicators, this paper finds that AIO can effectively alleviate ineffective investments in energy enterprises. Furthermore, this paper explores the moderating effects of the absorbed slack resources and conducts heterogeneity analysis based on enterprises ownership and lifecycle. The research results indicate that absorbed slack resources can weaken the alleviating effect of AIO on investment inefficiency. Besides, heterogeneity analysis also reveals that AIO can significantly alleviate investment inefficiency in the non-state-owned energy enterprises and those in the growth stage. These findings are important for energy enterprises to adopt and deploy AI technologies.

Suggested Citation

  • Zhai, Minhan & Wu, Wenqing & Tsai, Sang-Bing, 2025. "The effects of Artificial intelligence orientation on inefficient investment: Firm-level evidence from China's energy enterprises," Energy Economics, Elsevier, vol. 141(C).
    • Handle: RePEc:eee:eneeco:v:141:y:2025:i:c:s0140988324007576
    DOI: 10.1016/j.eneco.2024.108048
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988324007576
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2024.108048?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:eneeco:v:141:y:2025:i:c:s0140988324007576. 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.elsevier.com/locate/eneco .

    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.