范围(计算机科学)
选择(遗传算法)
政府(语言学)
知识管理
业务
描述性知识
组织学习
知识创造
公共关系
政治学
营销
计算机科学
哲学
人工智能
语言学
程序设计语言
下游(制造业)
作者
José M. Barrutia,Carmen Echebarria
标识
DOI:10.1080/14719037.2023.2231962
摘要
Collaborative public innovation literature suggests that collaboration with multiple partners may provide the necessary learning to respond to the complex problems facing city governments. However, scarce research has been developed to (1) scrutinize the nature of the new knowledge needed by city governments, and (2) understand how the different types of partners contribute to government’s learning. This study shows that a government’s election of different types of partners influences the scope of the knowledge developed (knowledge breadth), the level of refinement of that knowledge (knowledge depth), and the extent to which knowledge is difficult to document in writing (knowledge tacitness).
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