Mechanism of Performance Differentiation in Digital Government Responsiveness Driven by Attention Allocation: An Empirical Study Based on Big Data of Government-Citizen Interaction

In the digital era, the scarcity and differential allocation of attention resources have emerged as critical variables constraining governance effectiveness. Adopting an attention allocation perspective, this study utilizes panel data from the "Message Board for Leaders" on People's Daily Online and annual government work reports to empirically analyze the driving mechanisms of government-citizen attention interaction regarding digital government responsiveness performance. The findings indicate a significant asymmetry in the performance impact of government-citizen attention allocation: public attention, primarily focused on livelihood issues, exhibits a trade-off characteristic of prioritizing quality over speed. Conversely, government attention, centered on "data element-driven governance," can transcend traditional resource constraints, achieving a dual enhancement in both response speed and quality. Further analysis reveals that problem attributes serve as a contextual factor shaping attention conversion efficacy; routine problem contexts amplify the performance gap resulting from heterogeneous attention, whereas complex contexts generate a convergence effect.These conclusions underscore the necessity of transforming digital governance from "tool empowerment" to "data-driven" approaches, offering decision-making support for constructing a precision governance ecosystem characterized by complementary and mutually beneficial government-citizen attention.