The impact of challenge and hindrance demands on work-related burnout and job performance among employees in technology enterprises in China: the moderating effect of AI usage.

BACKGROUND: Employees in technology enterprises face escalating job demands that profoundly shape their performance and wellbeing. While the Job Demands-Resources model and the Challenge-Hindrance Stressor Framework are well-established, the role of AI usage in moderating the stressor-performance pathway remains largely unknown, particularly in the high-pressure Chinese technology sector. METHODS: Drawing on a sample of 442 employees from technology enterprises in Beijing, this study employed partial least squares structural equation modeling (PLS-SEM) to test a moderated mediation model examining the relationships among challenge demands, hindrance demands, work-related burnout, job performance, and AI usage. RESULTS: Challenge demands positively predicted job performance (β = 0.297, p < 0.001), whereas hindrance demands negatively predicted it (β = -0.376, p < 0.001). Both types of demands increased work-related burnout (β = 0.410, p < 0.001; β = 0.514, p < 0.001), which in turn impaired performance (β = -0.407, p < 0.001). AI usage strengthened the positive effect of challenge demands on performance (β = 0.183, p < 0.001) and buffered the negative effect of burnout on performance (β = 0.252, p < 0.001), but did not mitigate the negative effect of hindrance demands on performance (β = 0.015, p = 0.729). CONCLUSION: This study extends the JD-R and CHSF frameworks by identifying AI as a dual-role job resource-enhancing the benefits of challenge demands while compensating for burnout-related resource depletion. However, AI cannot substitute for organizational interventions needed to address hindrance demands. These findings offer actionable guidance for strategically deploying AI alongside targeted organizational reforms to optimize employee performance.