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A Moderated Mediation Model of AI-Driven Identity Threats and Employee Cyberloafing: The Role of AI-Inclusive Identity

Submitted:

21 January 2026

Posted:

22 January 2026

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Abstract
This study is intended to examine how Human-AI collaboration-based identity threat appraisals in the form of loss of autonomy and loss of skill, triggers professional identity that fosters cyberloafing. Based on social identity theory, this study applied a three-wave survey design with 507 employees. The proposed research model was tested using partial least squares structural equation modeling (PLS-SEM) with SmartPLS 4, which enabled the assessment of both measurement and structural models Perceived loss of skill and loss of autonomy are positively associated with professional identity threat, which mediates their relationships with cyberloafing. AI-inclusive identity strengthens these associations for loss of autonomy suggesting employees high in AI-inclusive identity exhibit stronger professional identity threat and higher cyberloafing under autonomy loss. This study used self-reported data from a single cultural context, which may limit generalizability. The counterintuitive effect of AI-inclusive identity highlights the need for future research to examine when it serves as a protective versus a risk-enhancing factor. When integrating AI, organizations should mitigate autonomy and skill-erosion appraisals through participatory design, role redesign, and communication that emphasizes unique human contributions. Supporting healthy AI–human identity integration may reduce counterproductive behaviors such as cyberloafing. By positioning identity threat appraisals as Human-AI collaboration–driven antecedents of professional identity threat and cyberloafing, this study extends social identity theory to human–AI contexts. It further demonstrates that over-identification with AI may heighten professional identity threats by diminishing the value of uniquely human contributions.
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Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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