Network Position, Research Funding and Interdisciplinary Collaboration among Nanotechnology Scientists: An Application of Social Network Analysis
|Periodical||Solid State Phenomena (Volumes 121 - 123)|
|Main Theme||Nanoscience and Technology|
|Edited by||Chunli BAI, Sishen XIE, Xing ZHU|
|Citation||Ricky Leung, 2007, Solid State Phenomena, 121-123, 1347|
|Online since||March, 2007|
|Keywords||Science Studies, Social Network Analysis (SNA), Sociology, Technology Studies|
The social dimensions of nanotechnology have aroused widespread interests in recent years. In US, for example, the National Nanotechnology Initiative (NNI) mandates that a large amount of funding resources to be allocated for studying the societal implications of nanotechnology. Since NNI took effect in 2001, teaching and research in the social dimensions of nanotechnology have grown tremendously . Advances in social network analysis have opened up new research opportunities to understand nanotechnology research and development. In particular, understanding scientific collaboration can help researchers and entrepreneurs better strategize and exploit research opportunities in nanotechnology. Moreover, social network analysis relies heavily on quantitative measures. This feature may serve as a bridge between natural and social scientists to jointly investigate the future directions of nanotechnology research and development (R&D). Using a social network analysis framework, this paper examines the patterns of intra- and inter-disciplinary collaborations among nanotechnology scientists. As an exploratory study, I discuss three methodological issues after reporting some descriptive results. First, the collaborative density used in this study is only one structural measure among many others. When investigating network positions, researchers can utilize other network measures according to specific purposes. Second, generalization may be methodologically problematic for network data. Accordingly, researchers should ascertain the plausibility of probability assumptions. Finally, Bayesian estimates allow researchers to combine beliefs about prior distribution and sample likelihood. Assuming a beta-binomial model, I present a set of Bayesian estimates.