When AI-Based Agents Are Proactive: Implications for Competence and System Satisfaction in Human-AI Collaboration

Authors: C Diebel, M Goutier, M Adam, A Benlian

Published: 2025

Publication: Business & Information Systems ..., 2025 - Springer

Proactive AI-based agent assistance decreases users' competence-based self-esteem and system satisfaction, especially for users with higher AI knowledge.

Methods: Vignette-based online experiment using self-determination theory as the framework to evaluate user responses to proactive vs. reactive AI assistance.

Key Findings: Impact of proactive vs. reactive AI help on users' competence-based self-esteem and system satisfaction, moderated by users' AI knowledge levels.

Limitations: The study's findings are based on an online vignette experiment, which may not fully reflect real-world human-AI collaboration dynamics.

Institution: Technical University of Darmstadt, University of Goettingen

Research Area: Human-AI Collaboration, System Satisfaction,User Competence

Discipline: Information Systems, Human-Computer Interaction (HCI), Artificial Intelligence

Citations: 32

DOI: https://doi.org/10.1007/s12599-024-00918-y