Generative AI tools and career burnout: The chain mediating effect of technology stress perception on turnover intention among young tech talents

Yiru Jiang (), Bity Salwana Alias () and Bo Li ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 6, 1513-1529

Abstract: This study investigates the chain mediating effect of technology stress perception and career burnout on the relationship between generative AI tools usage and turnover intention among young tech talents. A cross-sectional survey design was employed with 683 technology professionals aged 21-35, using structural equation modeling to analyze the proposed sequential pathway. Generative AI tools usage significantly influences turnover intention through a sequential pathway: AI tools usage positively affects technology stress perception (β = .36, p < .001), which contributes to career burnout (β = .53, p < .001), ultimately increasing turnover intention (β = .49, p < .001). This chain mediation effect was significant (indirect effect = .094, 95% CI [.071, .124]), explaining 67.1% of the total effect. The findings extend technostress theory to generative AI contexts and establish that technology self-efficacy and organizational support function as protective factors by mitigating technology stress and burnout, respectively. Practical implications: organizations implementing AI technologies should adopt strategic approaches focusing on reducing technology stress and preventing burnout to maintain workforce stability during technological transitions.

Keywords: Career burnout; Chain mediation; Generative AI; Technology stress; Turnover intention. (search for similar items in EconPapers)
Date: 2025
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