双层学习交互:地方数字政务服务创新过程与生成机理——基于N市经验的分析  

Dual-layer learning interaction,local digital government service innovation process and generation mechanism:Analysis based on the experience of N city

作　　者：向玉琼[1] 冯钰婷 XIANG Yu-qiong;FENG Yu-ting

机构地区：[1]南京农业大学公共管理学院

出　　处：《城市问题》2023年第10期14-23,共10页Urban Problems

基　　金：江苏省习近平新时代中国特色社会主义思想研究中心重点项目(23ZXZA008)——“全过程人民民主高质量发展研究”;南京市社会科学基金项目(23YB10)——“政策过程中全过程人民民主与新型政党制度建设研究”。

摘　　要：在自主创新授权下,内生性的政策学习与主动作为是推进地方数字政务服务创新的关键因素。通过构建双层学习分析框架,对N市政务服务数字化转型进行过程追踪,研究发现,这一过程中存在两种政策学习机制,其实质是政策议题驱动理念认知性学习、目标共识引致工具调适性学习、行为纠偏强化理念认知性学习的循环运作过程,推动N市数字政务服务持续创新。进一步研究发现,双层学习打破了执行政策中的僵化逻辑,其围绕“数字化”思维与数字政务服务创新这一目标,以串联协同的形式进行动态交互,旨在为数字政务服务创新提供目标上的指引与工具上的支撑。双层学习交互超越了单一学习机制的局限性,是对中国特色政策学习理论与实践的有益补充。Under the authorization of independent innovation,endogenous policy learning and proactive action are the key variables in promoting local digital government service innovation.By constructing a dual-layer learning analysis framework to track the digital transformation of government services in City N,it is found that there are two policy learning mechanisms in the process,which are in essence a cyclical process of conceptual cognitive learning driven by policy issues,instrumental adaptive learning due to goal consensus,and conceptual cognitive learning reinforced by behavioral corrections to promote continuous innovation of digital government services in City N.Further research found that the dual-layer learning breaks the rigid logic of policy implementation,which is centered on the goal of"digital"thinking and digital government service innovation,and dynamically interacts in the form of tandem synergy,aiming to provide goal guidance and tool support for digital government service innovation.The dual-layer learning interaction goes beyond the limitations of a single learning mechanism and is a useful supplement to the theory and practice of policy learning with Chinese characteristics.

关 键 词：双层学习 理念认知性学习 工具调适性学习 学习交互 数字政务服务创新 

分 类 号：D63[政治法律—政治学] TP18[政治法律—中外政治制度]