Journal Paper Published

Abstract

AI-induced job insecurity has become a salient work stressor amid rapid AI adoption in the workplace. While existing research has predominantly focused on its quantitative dimension, which reflects employees' concerns about potential job loss by AI, AI-induced qualitative job insecurity, which refers to employees' worries about potential deterioration of important job features due to AI, remains underexplored. Drawing on the cognitive appraisal theory of stress, this study investigates how AI-induced qualitative job insecurity influences knowledge workers' career commitment via their threat and challenge appraisals. We further examine the moderating roles of social support and learning adaptability. Data from 395 knowledge workers in the language service industry, a sector at the forefront of AI disruption, are primarily analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that AI-induced qualitative job insecurity indirectly reduces career commitment by simultaneously activating threat appraisal and suppressing challenge appraisal, tentatively positioning it as a threat stressor. Moreover, social support attenuates the activating effect of AI-induced qualitative job insecurity on threat appraisal, whereas learning adaptability weakens its suppressive impact on challenge appraisal. Our research investigates an understudied construct of AI-induced qualitative job insecurity and expands relevant scholarly scopes to career-level outcomes, offering implications for human-centered AI adoption in the workplace and sustainable professional development.

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