How team organization influences the ability to solve automation failures
an experimental study on human–AI decision-making in teams
Publication date
2025-12-02
Document type
Forschungsartikel
Author
Organisational unit
Scopus ID
Publisher
Springer
Series or journal
AI & Society
ISSN
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
Artificial intelligence
Human-AI teaming
Human-autonomy teaming
Team performance
Teamwork
Abstract
As production environments become increasingly automated and AI-assisted, managing automation failures is a growing challenge. This study examines how team organization—hierarchical versus self-managed—affects team performance in resolving such failures. Using a laboratory experiment simulating a realistic industrial setting, teams operated automated machinery supported by AI-based assistance. We hypothesize that communication mediates the relationship between team organization and performance outcomes (productivity and quality). The results show that self-managed teams communicate more frequently and with higher quality than hierarchical teams, leading to higher productivity and fewer errors. Structural equation modeling confirms that the effect of team organization on performance is fully mediated by communication. These findings highlight the importance of team communication and suggest that revisiting team organization in AI-driven production—by favoring self-management or enhancing communication in hierarchies—may improve performance. The study contributes to human–AI teaming research by integrating organizational design into experimental analysis.
Description
This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Version
Online first
Access right on openHSU
Metadata only access
Open Access Funding
Springer Nature (DEAL)