Enterprise AI Analysis

The study primarily draws on the Job Demand-Resource (JD-R) model and the Conservation of Resources (COR) theory. The JD-R model posits that high-intensity workloads and performance pressures (job demands) deplete resources, leading to negative emotions like emotional exhaustion. AI is identified as a significant job demand, increasing technostress and demands. The COR theory suggests that resource loss (e.g., job insecurity, time for family) leads to stress and defensive strategies, further depleting resources and contributing to emotional exhaustion. These frameworks help explain how AI awareness acts as a stressor, leading to job insecurity and work-family conflict, and ultimately, emotional exhaustion.

The study found that AI awareness positively predicts emotional exhaustion (H1 supported). This is mediated by two factors: job insecurity (H2 supported) and work interference with family (H3 supported). Specifically, higher AI awareness leads to greater job insecurity, which then increases emotional exhaustion. Similarly, higher AI awareness leads to more work interference with family, which also increases emotional exhaustion. Crucially, job insecurity and work interference with family serially mediate the association between AI awareness and emotional exhaustion (H4 supported). This means that AI awareness first exacerbates job insecurity, which then contributes to increased work interference with family, and both combined lead to higher emotional exhaustion. The results emphasize AI as a significant workplace stressor.

The findings highlight the importance for company managers to address job security concerns and support work-life balance to mitigate AI-related stress and promote employee well-being. Organizations should prioritize transparent communication about AI integration and its implications for job security. Implementing support systems like job retraining programs and counseling services can help mitigate feelings of job insecurity. To reduce work interference with family, organizations should promote work-life balance through flexible work arrangements and wellness initiatives. These practices can minimize negative emotional status associated with AI, fostering a healthier and more productive workforce.

Limitations include the sample consisting solely of Chinese participants, which limits cultural generalizability; the cross-sectional data preventing verification of causal relationships (suggesting future longitudinal studies); and the need for further exploration of familiarity and trust in AI. The limited sample size also suggests the findings may not be efficient to draw strong conclusions. Despite these limitations, the study sheds light on the mechanism underlying the association between AI awareness and emotional exhaustion.