信息资源管理学报2025，Vol.15Issue(6)：20-36,17.DOI:10.13365/j.jirm.2025.06.020
科学向技术转移的动因与阻力:一项基于指数随机图的实证研究
Drivers and Barriers in Science-to-Technology Transfer:An Empirical Study Based on Exponential Random Graph Model
摘要
Abstract
Investigating the mechanism of knowledge flow from science to technology helps understand how scientific progress drives technological innovation.Thus,this paper first constructed a"science-technol-ogy"knowledge transfer network composed of keyword citation.Then,using exponential random graph models,we integrated knowledge attributes with the knowledge transfer process in a modeling approach that simultaneously considered endogenous network structures.Finally,we conducted an empirical analysis based on scientific papers and patent data in the gene editing field from 1990 to 2018.We find that the high economic value of scientific and technological knowledge inhibits knowledge transfer,as rational actors tend to engage in exploitative innovation based on existing high-value knowledge.However,the convergence of economic value facilitates the transfer process by helping to reduce transfer barriers through moderate cogni-tive distance.The academic value of knowledge contributes to advancing the knowledge transfer process,but this effect is not statistically significant.Under the influence of homogeneity effects,knowledge novelty and geographic proximity have a positive impact on the formation of knowledge transfer relationships from science to technology.Meanwhile,comparison with random networks demonstrates that the citation behavior of technological knowledge toward scientific knowledge may not be influenced by semantic proximity or knowledge potential.These results demonstrate consistency across knowledge network simulation models in different time periods.关键词
知识转移网络/指数随机图模型/科技关联/关键词引用/内生网络结构Key words
Knowledge transfer network/Exponential random graph model/Technology connection/Keyword citations/Endogenous network structure分类
社会科学引用本文复制引用
马铭,毛进,邹当逸,李纲..科学向技术转移的动因与阻力:一项基于指数随机图的实证研究[J].信息资源管理学报,2025,15(6):20-36,17.基金项目
本文系国家自然科学基金面上项目"基于'问题-方法'关联识别的科学知识创新探测与协同演化分析"(72174154)的成果之一.(This study is supported by the National Natural Science Foundation of China titled"Detecting Scientific Knowledge Innovation and Its Co-evolutionary Analysis Based on'Question-Method'Association Identification"(72174154).) （72174154）
