知识库
延展性
知识管理
基础(拓扑)
知识抽取
计算机科学
星团(航天器)
业务
数学
数据挖掘
人工智能
计算机安全
加密
数学分析
密文
程序设计语言
作者
Sai Yayavaram,Gautam Ahuja
标识
DOI:10.2189/asqu.53.2.333
摘要
We use patent data from the worldwide semiconductor industry from 1984 to 1994 to study the effect of the structure of organizational knowledge bases, or the patterns of coupling between their elements of technical knowledge, on the usefulness of inventions and knowledge-base malleability. We argue that organizational variations in coupling patterns between knowledge elements can be reflected in a spectrum of knowledge-base structures—varying from fully decomposable (the knowledge base is composed of distinct clusters of knowledge elements coupled together with no significant ties between clusters) through nearly decomposable (knowledge clusters are discernable but are connected through cross-cluster couplings) to non-decomposable (no knowledge clusters emerge, as the couplings are pervasively distributed)—and that organizations may differ in the way they use their knowledge because of variations in their knowledge-base structure, rather than because of differences in the knowledge elements themselves. Results show that a nearly decomposable knowledge base increases the usefulness of the inventions generated from it, as measured by patent citations, and also the knowledge base's malleability or capacity for change.
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