文献类型：期刊文献

英文题名：Modeling knowledge diffusion in scientific innovation networks: an institutional comparison between China and US with illustration for nanotechnology

作者：Liu, Xuan[1];Jiang, Shan[2];Chen, Hsinchun[2];Larson, Catherine A.[2];Roco, Mihail C.[3]

机构：[1]E China Univ Sci & Technol, Sch Business, Dept Management Sci & Engn, Shanghai 200237, Peoples R China;[2]Univ Arizona, Eller Coll Management, Dept Management Informat Syst, Artificial Intelligence Lab, Tucson, AZ 85721 USA;[3]Natl Sci Fdn, Arlington, VA 22230 USA

年份：2015

卷号：105

期号：3

起止页码：1953

外文期刊名：SCIENTOMETRICS

收录：;WOS:【SSCI(收录号:WOS:000365130100031)，SCI-EXPANDED(收录号:WOS:000365130100031)】；

基金：This study was supported by the US National Science Foundation (CMMI-1057624 and CMMI-1249210), and the National Natural Science Foundation of China (Grants Nos. 71471064,71101053, 61104139, 71171131, and 71103021), and also sponsored by Shanghai Pujiang Program (Grant No. 15PJC019). The last co-author was supported by the Directorate of Engineering, NSF. The literature data was purchased from Thomson ISI and we thank them for their support.

语种：英文

外文关键词：Knowledge diffusion; Social network analysis; Knowledge flow characteristics; Cox regression model; Boundary spanning position; Nanoscale science and engineering

摘要：Knowledge is a crucial asset in organizations and its diffusion and recombination processes can be affected by numerous factors. This study examines the influence of the status of individual researchers in social networks on the knowledge diffusion and recombination process. We contend that knowledge diversity, random diffusion, and parallel duplication are three primary factors characterizing diffusion paths in knowledge networks. Using multiple social network measures, we investigate how individuals in the respective institutional collaboration networks influence knowledge diffusion through scientific papers. Scientific publication data and citation data from six prolific institutions in China (Chinese Academy of Sciences) and the United States (University of California at Berkeley, University of Illinois, Massachusetts Institute of Technology, Northwestern University, and Georgia Institute of Technology) in nanotechnology field in the interval 2000-2010 were used for empirical analysis, and the Cox regression model was leveraged to analyze the temporal relationships between knowledge diffusion and social network measures of researchers in these leading institutions. Results show that structural holes and degree centrality are the most effective measures to explain the knowledge diffusion process within these six institutions. Knowledge recombination is mainly achieved through parallel duplication within groups and recombination of diverse knowledge across different groups. The results are similar for all six institutions except for Bonacich power and eigenvector measures, which may posit cultural difference across countries and institutions.