Impact of task decomposability and workflow analyzability on employee-AI multitasking parallel mode: A moderated mediation model with organizational AI support

Against the backdrop of the widespread application of AI technology in organizations, employees’ work modes have transitioned from single-task processing to the employee-AI multitasking parallel mode. Based on the task-technology fit theory, this study explores the influence mechanism of task decomposability (a task structure characteristic) and workflow analyzability (a technology system characteristic) on the adoption of the employee-AI multitasking parallel mode, while examining the mediating role of employee-AI collaboration readiness and the moderating role of organizational AI support. This study collected a total of 408 valid samples through three rounds of questionnaire surveys among employees from 9 enterprises in Shanghai. The results show that both task decomposability and workflow analyzability significantly enhance employee-AI collaboration readiness, thereby significantly promoting the adoption of the employee-AI multitasking parallel mode. When the level of organizational AI support is high, the above positive impacts and indirect effects are more significant; when the level of organizational AI support is low, they are relatively weaker. This study reveals the adoption mechanism of the employee-AI multitasking parallel mode, provides a new theoretical perspective for exploring the dynamic interactive adoption process of this mode, and offers practical implications for organizations to enhance task decomposability and workflow analyzability, as well as to construct AI support mechanisms and dynamic employee-AI collaboration systems.