-
Jiang, S.; Gao, Q.; Chen, H.; Roco, M.C.: ¬The roles of sharing, transfer, and public funding in nanotechnology knowledge-diffusion networks (2015)
0.03
0.029989695 = product of:
0.08996908 = sum of:
0.08996908 = product of:
0.13495362 = sum of:
0.0992141 = weight(_text_:network in 1823) [ClassicSimilarity], result of:
0.0992141 = score(doc=1823,freq=6.0), product of:
0.19402927 = queryWeight, product of:
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.043569047 = queryNorm
0.51133573 = fieldWeight in 1823, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.046875 = fieldNorm(doc=1823)
0.035739526 = weight(_text_:29 in 1823) [ClassicSimilarity], result of:
0.035739526 = score(doc=1823,freq=2.0), product of:
0.15326229 = queryWeight, product of:
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.043569047 = queryNorm
0.23319192 = fieldWeight in 1823, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.046875 = fieldNorm(doc=1823)
0.6666667 = coord(2/3)
0.33333334 = coord(1/3)
- Abstract
- Understanding the knowledge-diffusion networks of patent inventors can help governments and businesses effectively use their investment to stimulate commercial science and technology development. Such inventor networks are usually large and complex. This study proposes a multidimensional network analysis framework that utilizes Exponential Random Graph Models (ERGMs) to simultaneously model knowledge-sharing and knowledge-transfer processes, examine their interactions, and evaluate the impacts of network structures and public funding on knowledge-diffusion networks. Experiments are conducted on a longitudinal data set that covers 2 decades (1991-2010) of nanotechnology-related US Patent and Trademark Office (USPTO) patents. The results show that knowledge sharing and knowledge transfer are closely interrelated. High degree centrality or boundary inventors play significant roles in the network, and National Science Foundation (NSF) public funding positively affects knowledge sharing despite its small fraction in overall funding and upstream research topics.
- Date
- 27. 4.2015 10:29:08
-
Chen, H.; Beaudoin, C.E.; Hong, H.: Teen online information disclosure : empirical testing of a protection motivation and social capital model (2016)
0.01
0.0063645868 = product of:
0.01909376 = sum of:
0.01909376 = product of:
0.057281278 = sum of:
0.057281278 = weight(_text_:network in 3203) [ClassicSimilarity], result of:
0.057281278 = score(doc=3203,freq=2.0), product of:
0.19402927 = queryWeight, product of:
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.043569047 = queryNorm
0.29521978 = fieldWeight in 3203, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.046875 = fieldNorm(doc=3203)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Abstract
- With bases in protection motivation theory and social capital theory, this study investigates teen and parental factors that determine teens' online privacy concerns, online privacy protection behaviors, and subsequent online information disclosure on social network sites. With secondary data from a 2012 survey (N?=?622), the final well-fitting structural equation model revealed that teen online privacy concerns were primarily influenced by parental interpersonal trust and parental concerns about teens' online privacy, whereas teen privacy protection behaviors were primarily predicted by teen cost-benefit appraisal of online interactions. In turn, teen online privacy concerns predicted increased privacy protection behaviors and lower teen information disclosure. Finally, restrictive and instructive parental mediation exerted differential influences on teens' privacy protection behaviors and online information disclosure.
-
Liu, X.; Kaza, S.; Zhang, P.; Chen, H.: Determining inventor status and its effect on knowledge diffusion : a study on nanotechnology literature from China, Russia, and India (2011)
0.01
0.0053038225 = product of:
0.015911467 = sum of:
0.015911467 = product of:
0.047734402 = sum of:
0.047734402 = weight(_text_:network in 4468) [ClassicSimilarity], result of:
0.047734402 = score(doc=4468,freq=2.0), product of:
0.19402927 = queryWeight, product of:
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.043569047 = queryNorm
0.2460165 = fieldWeight in 4468, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.0390625 = fieldNorm(doc=4468)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Abstract
- In an increasingly global research landscape, it is important to identify the most prolific researchers in various institutions and their influence on the diffusion of knowledge. Knowledge diffusion within institutions is influenced by not just the status of individual researchers but also the collaborative culture that determines status. There are various methods to measure individual status, but few studies have compared them or explored the possible effects of different cultures on the status measures. In this article, we examine knowledge diffusion within science and technology-oriented research organizations. Using social network analysis metrics to measure individual status in large-scale coauthorship networks, we studied an individual's impact on the recombination of knowledge to produce innovation in nanotechnology. Data from the most productive and high-impact institutions in China (Chinese Academy of Sciences), Russia (Russian Academy of Sciences), and India (Indian Institutes of Technology) were used. We found that boundary-spanning individuals influenced knowledge diffusion in all countries. However, our results also indicate that cultural and institutional differences may influence knowledge diffusion.
