The paper presents a methodology called hybrid documents co-citation analysis, for studying the interaction between science and technology in technology diffusion. Our approach rests mostly on patent citation, cluster analysis and network analysis. More specifically, with the patents citing Smalley RE in Derwent innovations index as the data sets, the paper implemented hybrid documents co-citation network through two procedures. Then spectrum cluster algorithm was used to reveal the knowledge structure in technology diffusion. After that, with the concordance between network properties and technology diffusion mechanisms, three indicators containing degree, betweenness and citation half-life, were calculated to discuss the basic documents in the pivotal position during the technology diffusion. At last, the paper summarized the hybrid documents co-citation analysis in practise, thus concluded that science and technology undertook different functions and acted dominatingly in the different period of technology diffusion, though they were co-activity all the time.
Alencar, M, Porter, A, Antunes, A. Nanopatenting patterns in relation to product life cycle. Technological Forecasting and Social Change 2007 74 9 1661–1680 .
Baker, S., & Aston, A. (2005). The business of nanotech. Business Week, 64–71.
Bhattacharya, S, Kretschmer, H, Meyer, M. Characterizing intellectual spaces between science and technology. Scientometrics 2003 58 2 369–390 .
Burton, R, Kebler, R. The “half-life” of some scientific and technical literatures. American Documentation 1960 11 1 18–22 .
Chang, S, Lai, K, Chang, S. Exploring technology diffusion and classification of business methods: Using the patent citation network. Technological Forecasting and Social Change 2009 76 1 107–117 .
Chen, C. Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National academy of Sciences of the United States of America 2004 101 Suppl 1 5303 .
Chen, C. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology 2006 57 3 359–377 .
Chen, C, Ibekwe SanJuan, F, Hou, J. The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis. Journal of the American Society for Information Science and Technology 2010 61 7 1386–1409 .
Chen, C, Zhang, J, Vogeley, MS. Making sense of the evolution of a scientific domain: a visual analytic study of the Sloan Digital Sky Survey research. Scientometrics 2010 83 3 669–688 .
Hullmann, A, Meyer, M. Publications and patents in nanotechnology. Scientometrics 2003 58 3 507–527 .
Jaffe, A, Trajtenberg, M, Henderson, R. Geographic localization of knowledge spillovers as evidenced by patent citations. The Quarterly Journal of Economics 1993 108 3 577–598 .
Lee, M, Kim, K, Cho, Y. A study on the relationship between technology diffusion and new product diffusion. Technological Forecasting and Social Change 2010 77 5 796–802 .
Lee, P, Su, H, Wu, F. Quantitative mapping of patented technology—The case of electrical conducting polymer nanocomposite. Technological Forecasting and Social Change 2010 77 3 466–478 .
Meyer, M, Debackere, K, Glanzel, W. Can applied science be ‘good science’? Exploring the relationship between patent citations and citation impact in nanoscience. Scientometrics 2010 85 2 527–539 .
Narin, F, Hamilton, K, Olivastro, D. The increasing linkage between US technology and public science. Research Policy 1997 26 3 317–330 .
Narin, F, Noma, E. Is technology becoming science?. Scientometrics 1985 7 3 369–381 .
Narin, F, Olivastro, D. Linkage between patents and papers: An interim EPO/US comparison. Scientometrics 1998 41 1 51–59 .
Nock, R, Nielsen, F. On weighting clustering. IEEE transactions on pattern analysis and machine intelligence 2006 28:1223–1235 .
Noh, K, Kim, W, Kwon, O, Yae, Y, Choi, H. Tracing knowledge flows using science and technology indicators. Information-Yamaguchi 2007 10 3 327.
Park, HW, Kang, J. Patterns of scientific and technological knowledge flows based on scientific papers and patents. Scientometrics 2009 81 3 811–820 .
Park, G, Park, Y. On the measurement of patent stock as knowledge indicators. Technological Forecasting and Social Change 2006 73 7 793–812 .
Roco, M. International perspective on government nanotechnology funding in 2005. Journal of Nanoparticle Research 2005 7 6 707–712 .
Small, H. Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American society for information science 1973 24 4 265–269 .
Small, H, Griffith, B. The structure of scientific literatures I: Identifying and graphing specialties. Science studies 1974 4:17–40 .
Stolpe, M. Determinants of knowledge diffusion as evidenced in patent data: the case of liquid crystal display technology* 1. Research Policy 2002 31 7 1181–1198 .
Sung, K., Kim, T., & Kong, H. (2010). Microscopic approach to evaluating technological convergence using patent citation analysis. U-and E-Service, Science and Technology (pp. 188–194).
U Von Luxburg 2007 A tutorial on spectral clustering. Statistics and Computing 17 4 395–416 .
Yoon, J, Kim, K. Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks. Scientometrics 2011 88 1 213–228 .