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How the Human–Artificial Intelligence (AI) Collaboration Affects Cyberloafing: An AI Identity Perspective

Jin-Qian Xu,Tung-Ju Wu,Wen Duan,X. Cui

Published 2025 in Behavioral Science

ABSTRACT

Collaboration with artificial intelligence (AI) not only improves employees’ work efficiency but also provides them with opportunities to participate in other behaviors. Among the various behaviors that have garnered the attention of organizations, cyberloafing has historically been a focus. Drawing from social identity theory (SIT), this research examines how human–AI collaboration diminishes cyberloafing by fostering AI identity (dependence, emotional energy, relatedness). A three-wave study (N = 381) revealed that AI collaboration strengthened employees’ AI identity, enabling them to recognize their identity as AI collaborators and focus on in-role tasks, thereby reducing cyberloafing. Moreover, the research suggested that openness served as a moderating factor, further amplifying the positive relationship between human–AI collaboration and AI identity. Specifically, employees who exhibit higher levels of openness are more likely to demonstrate heightened AI identity and reduced cyberloafing. Conversely, employees with low openness exhibit less AI identity and more cyberloafing. This research employs SIT in the context of AI collaboration, thereby providing a theoretical foundation and practical guidance for reducing employee cyberloafing in the workplace and promoting organizational development.

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