IDEAS home Printed from https://ideas.repec.org/p/dar/wpaper/158981.html

Team Climate in Team-AI Collaboration: Exploring the Role of Decisional Ownership and Perceived AI Team Membership

Author

Listed:
  • Zercher, Désirée
  • Jussupow, Ekaterina
  • Heinzl, Armin

Abstract

Generative AI has advanced capabilities, enabling these systems to participate as teammates in human teams. Yet, the potential consequences of including an AI teammate for team climate have yet to be explored. Thus, we investigate how shared decisional ownership between humans and AI, as well as the perception of AI as a teammate affect team climate (including its subdimensions). We conducted an experiment with 85 participants in 35 teams collaborating with a generative AI teammate on a team decision-making task. We demonstrate that human decisional ownership improves team climate, while AI decisional ownership has a non-significant negative impact. However, when AI is perceived as a teammate, its decisional ownership also enhances team climate. The qualitative analysis provides additional insights into how these perceptions emerge. Our findings provide a nuanced understanding of the mechanisms of team-AI collaboration that shape team climate and offer practical guidance for fostering a positive team climate.

Suggested Citation

  • Zercher, Désirée & Jussupow, Ekaterina & Heinzl, Armin, 2025. "Team Climate in Team-AI Collaboration: Exploring the Role of Decisional Ownership and Perceived AI Team Membership," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 158981, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    • Handle: RePEc:dar:wpaper:158981
    Note: for complete metadata visit http://tubiblio.ulb.tu-darmstadt.de/158981/
    as

    Download full text from publisher

    File URL: https://authorconnect.aisnet.org/conferences/ECIS2025/papers/ECIS2025-1181
    Download Restriction: no
    File URL: https://aisel.aisnet.org/ecis2025/human_ai/human_ai/8/
    Download Restriction: no
    ---><---

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:dar:wpaper:158981. 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: Dekanatssekretariat (email available below). General contact details of provider: https://edirc.repec.org/data/ivthdde.html .

    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.