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注意力配置驱动数字政府回应的绩效分异机理研究——基于政民互动大数据的实证检验

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

  • 摘要: 数字时代注意力资源的稀缺性与配置差异,已成为制约政府治理效能的关键变量。研究基于注意力配置视角,利用人民网“领导留言板”与政府工作报告面板数据,实证政民注意力互动对数字政府回应绩效的驱动机理。研究发现,政民注意力配置存在显著的非对称性绩效影响,公众侧重民生领域的注意力聚焦呈现出“重质轻速”的权衡特征,而政府侧重“数据要素驱动治理”的注意力配置则能突破传统资源约束,实现回应速度与质量的双效提升。进一步地,问题属性是影响注意力转化效能的情境变量,常规性问题情境会放大异质性注意力的绩效差距,而复杂情境则会趋同。结论揭示了数字治理从“工具赋能”向“数据驱动”转型的必要性,为构建政民注意力互补增益的精准治理生态提供决策支持。

     

    Abstract: 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.

     

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