The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace

2026-05-29

AI Summary

This study investigates how AI capability and proactiveness in human-AI collaboration influence employees’ perceptions of work ownership, meaningfulness, and affective experiences—both regarding themselves and their colleagues. Employing a 2×2×2 scenario-based experiment with a mixed within- and between-subjects design, the research measures multidimensional perceptual outcomes using validated scales. Findings reveal that AI systems exhibiting high capability or high proactiveness can undermine human workers’ sense of professional identity and social standing, thereby challenging prevailing performance-centric paradigms in workplace AI design. Conversely, AI characterized by low capability or low proactiveness generally enhances perceived work ownership, positive affect, and job satisfaction. Notably, these effects are significantly moderated by observational perspective—whether evaluations concern the self or others.

Abstract

Human-AI collaboration is considered the most promising way to incorporate AI in the workplace. What remains unexplored are the experiential consequences of this teaming. More specifically, in a team with AI, how humans perceive themselves (self-perception) and how they are perceived by their coworkers (peer perception) in terms of work ownership and job meaningfulness. In a 2x2x2 vignette study (n=50), participants rated perceptions of ownership, affect, job meaningfulness and satisfaction, and role dynamics across two levels (low/high) of AI proactivity and AI competency as within-subject factors, with point-of-view (self perception/peer perception) as between-subjects. Our results showed that AI with low competency or low proactivity generally improved feelings related to ownership, meaningfulness, satisfaction, and role dynamics, and also increased positive affect while reducing negative affect. However, these effects were often influenced by point-of-view. For instance, low AI proactivity resulted in higher job satisfaction from self-perception rather than peer perception. Based on our findings, we argue that designing AI for the future of work solely around performance metrics may not be adequate. Highly competent and proactive AI-driven systems can have undesirable impacts on perceptions of ownership, job identity, social image and team dynamics, and consequently, job meaningfulness.

Problem

• human-AI collaboration
• self-perception
• peer perception
• job meaningfulness
• work ownership

Innovation

• human-AI collaboration
• AI proactivity
• AI competency
• work ownership
• job meaningfulness