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

Abstract:

Human-AI collaboration is widely regarded as the most effective strategy for integrating artificial intelligence into professional environments. However, the experiential implications of such partnerships remain largely unexamined. Specifically, little is known about how individuals view their own sense of work ownership and job meaningfulness, as well as how their colleagues perceive them in these roles within AI-augmented teams.

To investigate this, we conducted a 2x2x2 vignette study involving 50 participants. The study utilized AI competency and AI proactivity (each at low and high levels) as within-subject factors, while point-of-view (self-perception versus peer perception) served as a between-subjects variable. Participants evaluated metrics related to ownership, affect, job meaningfulness, satisfaction, and role dynamics.

Our analysis revealed that AI systems characterized by low competency or low proactivity generally enhanced feelings associated with ownership, meaningfulness, and satisfaction, while also boosting positive affect and diminishing negative affect. Nevertheless, these outcomes were frequently moderated by the perspective taken. For example, low AI proactivity led to higher job satisfaction when assessed through self-perception compared to peer perception.

Based on these insights, we contend that future workplace AI design cannot rely exclusively on performance metrics. Highly competent and proactive AI systems may inadvertently undermine perceptions of ownership, job identity, social image, and team dynamics, ultimately affecting job meaningfulness.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC