The Impact of Transformational Leadership on the Task Performance of Elderly Care Workers: The Mediating Role of Work Engagement and the Moderating Role of Attitudes toward Artificial Intelligence

HRMARS - Purpose: This study investigates the impact of transformational leadership (TL) on the task performance (TP) of elderly care workers in Beijing, examining the mediating role of work engagement (WE) and the moderating effect of attitudes towards artificial intelligence (AAI). Design/methodology/approach: A quantitative design was adopted, using purposive sampling of 268 elderly care workers in Beijing, China. Data were analysed using SPSS and SmartPLS, with 5,000 resampling bootstraps employed within the Job Demands-Resources (JD-R) model to test the hypothesised relationships. Findings: TL positively influenced TP through WE, indicating a full mediation effect, as the direct relationship between TL and TP was not significant. AAI significantly moderated the relationship between WE and TP, with favourable AAI strengthening this link. These findings support the motivational processes proposed by the JD-R model and demonstrate that AAI, as a personal resource, can enhance the connection between engagement and performance. Research limitations/implications: This study is confined to elderly care institutions in Beijing, which may limit the generalisability of the findings. The cross-sectional design restricts causal inference, while self-reported data may introduce common method bias. Future research may adopt longitudinal or multi-source methodologies. Practical implications: Research findings indicate that while care service providers strengthen TL and employee engagement, they should prioritise fostering positive AAI. Systematic training, organisational support, and open communication can further enhance engagement, thereby improving AI application and TP. Originality/value: Within the context of elderly care, research examining the incorporation of AAI into the JD-R model remains scarce. This study contributes to understanding how TL and AAI jointly shape employees’ WE and TP.