IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2508.03757.html
   My bibliography  Save this paper

AI Investment and Firm Productivity: How Executive Demographics Drive Technology Adoption and Performance in Japanese Enterprises

Author

Listed:
  • Tatsuru Kikuchi

Abstract

This paper investigates how executive demographics particularly age and gender influence artificial intelligence (AI) investment decisions and subsequent firm productivity using comprehensive data from over 500 Japanese enterprises spanning from 2018 to 2023. Our central research question addresses the role of executive characteristics in technology adoption, finding that CEO age and technical background significantly predict AI investment propensity. Employing these demographic characteristics as instrumental variables to address endogeneity concerns, we identify a statistically significant 2.4% increase in total factor productivity attributable to AI investment adoption. Our novel mechanism decomposition framework reveals that productivity gains operate through three distinct channels: cost reduction (40% of total effect), revenue enhancement (35%), and innovation acceleration (25%). The results demonstrate that younger executives (below 50 years) are 23% more likely to adopt AI technologies, while firm size significantly moderates this relationship. Aggregate projections suggest potential GDP impacts of 1.15 trillion JPY from widespread AI adoption across the Japanese economy. These findings provide crucial empirical guidance for understanding the human factors driving digital transformation and inform both corporate governance and public policy regarding AI investment incentives.

Suggested Citation

  • Tatsuru Kikuchi, 2025. "AI Investment and Firm Productivity: How Executive Demographics Drive Technology Adoption and Performance in Japanese Enterprises," Papers 2508.03757, arXiv.org.
  • Handle: RePEc:arx:papers:2508.03757
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2508.03757
    File Function: Latest version
    Download Restriction: no
    ---><---

    More about this item

    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:arx:papers:2508.03757. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.