IDEAS home Printed from https://ideas.repec.org/a/igg/rmj000/v39y2026i1p1-17.html

Moral Decision Training Platform AI and Psychological Modeling for Information Management

Author

Listed:
  • Jianming Zhang

    (Mental Health Education Center, Zhejiang Industry Polytechnic College, China)

  • Shu Zhang

    (Discipline Inspection Office, Jiyang College, Zhejiang A&F University, China)

Abstract

The integration of artificial intelligence (AI) into organizational decision-making has turned AI-driven moral judgment into a strategic information resource, while its effective management remains underexplored. In this study, the authors constructed a moral decision-making training platform based on psychological modeling, focusing on three dimensions: responsibility attribution, intention cognition, and decision rationality. By analyzing human attitudes toward AI behavior in organizational scenarios, the authors built a recursive psychological model and verified it with empirical data, where the dynamic weight neural model achieved a high goodness of fit (R2 = 0.82). The platform realizes adaptive feedback management of moral decision information resources, which helps optimize organizational decision efficiency and ethical governance. The results show that the platform's application is scenario dependent, and its long-term value needs further verification in diverse organizational contexts. This study provides a practical tool for information resource management in the ethical AI era.

Suggested Citation

  • Jianming Zhang & Shu Zhang, 2026. "Moral Decision Training Platform AI and Psychological Modeling for Information Management," Information Resources Management Journal (IRMJ), IGI Global Scientific Publishing, vol. 39(1), pages 1-17, January.
    • Handle: RePEc:igg:rmj000:v:39:y:2026:i:1:p:1-17
    as

    Download full text from publisher

    File URL: https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IRMJ.404755
    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:igg:rmj000:v:39:y:2026:i:1:p:1-17. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.