What question did this study set out to answer?

This study aims to explore how AI application influences employees' job insecurity and the effect of self-efficacy and transformational leadership.

June 6, 2026Open Access

The impact of artificial intelligence application on employees' job insecurity: the moderating roles of self-efficacy and transformational leadership

Key Points

This study aims to explore how AI application influences employees' job insecurity and the effect of self-efficacy and transformational leadership.
Mixed online and offline questionnaire distribution, with 453 questionnaires distributed and 411 valid responses retained.
Data collected through employee self-reported measures to assess job insecurity.
Investigated the relationship between AI application and job insecurity with a focus on moderating factors.
A U-shaped relationship observed: moderate AI application decreases job insecurity, while excessive application increases it.
Self-efficacy negatively moderates the relationship, enhancing security from moderate AI and reducing insecurity from excessive AI.
Transformational leadership also moderates the effect, suggesting supportive leadership mitigates insecurity related to AI application.

Abstract

Introduction Against the backdrop of the intelligent era, the widespread application of artificial intelligence (AI) has fundamentally reshaped the internal and external developmental ecosystems of organizations, exerting a profound impact on employees' work-related psychological states. Drawing on the Conservation of Resources Theory and the Cognitive Appraisal Theory of Stress, this study empirically explores the underlying mechanism through which AI application influences employees' job insecurity. Methods Data for this study were collected using a mixed online and offline distribution method, with all measures administered through employee self-reported questionnaires. A total of 453 questionnaires were distributed, including 242 online and 211 offline. We received 449 questionnaires, with 242 from the online channel and 207 offline. Following a stringent validity screening process conducted by the research team, 411 valid questionnaires were retained for analysis (219 online and 192 offline), resulting in an effective response rate of 90.73%. Results There is a significant U-shaped relationship between AI application and employees' job insecurity: moderate AI application reduces insecurity, whereas excessive application heightens it. Self-efficacy negatively moderates this relationship by strengthening the insecurity-reducing effect of moderate AI application and weakening the insecurity-enhancing effect of excessive application. Transformational leadership similarly exerts a negative moderating effect, suggesting that both individual psychological resources and supportive leadership can buffer employees' insecurity responses to varying levels of AI application in digitality transforming organizational contexts more effectively. Discussion This study advances research on AI-enabled workplace changes by revealing a U-shaped effect of AI application intensity on employees' job insecurity. It explains this relationship through the dual mechanisms of resource gain and resource threat. It further incorporates self-efficacy and transformational leadership as boundary conditions, thereby clarifying when AI application alleviates or intensifies employees' job insecurity. These findings enrich the theoretical understanding of employees' job insecurity within the context of AI application, and offer empirical insights for managing employee wellbeing and refining human resource strategies during organizational digital transformation.

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