, Volume 90, Issue 2, pp 561–579 | Cite as

Modeling the dynamic relation between science and technology in nanotechnology

  • Qingjun Zhao
  • Jiancheng GuanEmail author


Nanotechnology is a promising research domain with potential and enormous economic value. It is widely acknowledged that nanotechnology, as an emerging and rapidly evolving field with the multidisciplinary nature, is perceived as proximate fields of science and technology. This study provides a further description of the relationship between science and technology at macro-level. The core objective in this paper is to qualify and assess the dynamic associations between scientific activity and technological output. We attempt to illustrate how science and technology relate one another in the case of innovation system. In this paper, we take advantage of the simultaneous equations model to analyze the reciprocal dependence between science and technology. Previous studies about the relationship between science and technology infrequently adopt this model. Our result shows that there is no significant connection between R&D expenditures and actual practices of research in terms of publications and patents for the universities in zone 1 and 2. Our results provoke questions about whether policy-makers should appropriately reallocate scientific and technological resources and other R&D expenditures so as to obtain optimal allocation for resource and achieve maximum results with little effort for scientific research and innovation performance.


Nanotechnology Simultaneous equations model Dynamic relation 



This research is funded by the National Natural Science Foundation of China (Project no. 70773006), the National Social Science Foundation of China (Project no. 10zd&014), key discipline excellent doctoral research funded projects in Fudan University and Introduce talents project in Chongqing University of Arts and Sciences.


  1. Albert, M. (2003). Universities and the market economy: The differential impact on knowledge production in sociology and economics. Higher Education, 45(2), 147–182.CrossRefGoogle Scholar
  2. Auranen, O., & Nieminen, M. (2010). University research funding and publication performance—an international comparison. Research Policy, 39, 822–834.CrossRefGoogle Scholar
  3. Bai, C. L. (2005). Ascent of nanoscience in China. Science, 309(5731), 61–63.CrossRefGoogle Scholar
  4. Becker, B., & Pain, N. (2003). What Determines Industrial R&D Expenditure in the UK? National Institute of Economic and Social Research (NIESR) discussion paper 211.Google Scholar
  5. Bernardes, A., & Albuquerque, E. (2003). Cross-over, thresholds and interaction between science and technology: Lessons for less-developed countries. Research Policy, 32(5), 865–885.CrossRefGoogle Scholar
  6. Bradford, S. C. (1934). Sources of information on specific subjects. Engineering, 137, 85–86.Google Scholar
  7. Braun, T., Schubert, A., & Zsindely, S. (1997). Nanoscience and nanotechnology on the balance. Scientometrics, 38(2), 321–325.CrossRefGoogle Scholar
  8. Brusoni, S., Prencipe, A., & Pavitt, K. (2001). Knowledge specialization, organizational coupling, and the boundaries of the firm: Why do firms know more than they make? Administrative Science Quarterly, 46(4), 597–621.CrossRefGoogle Scholar
  9. Butler, L. (2003). Explaining Australia’s increased share of ISI publications—the effects of a funding formula based on publication counts. Research Policy, 32(1), 143–155.CrossRefGoogle Scholar
  10. Carpenter, M. P., Cooper, M., & Narin, F. (1980). Linkage between basic research and patents. Research Management, 23, 30–35.Google Scholar
  11. Chaves, C. V., & Moro, S. (2007). Investigating the interaction and mutual dependence between science and technology. Research Policy, 36, 1204–1220.CrossRefGoogle Scholar
  12. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35, 128–152.CrossRefGoogle Scholar
  13. Da Luz, M. P., Marques-Portella, C., Mendlowicz, M., Gleiser, S., Coutinho, E. S., & Figueira, I. (2008). Institutional h-index: The performance of a new metric in the evaluation of Brazilian psychiatric post-graduation programs. Scientometrics, 77(2), 361–368. doi: 10.1007/s11192-007-1964-9.CrossRefGoogle Scholar
  14. Debackere, K., & Veugelers, R. (2005). The role of academic technology transfer organizations in improving industry science link. Research Policy, 34, 321–342.CrossRefGoogle Scholar
  15. Dosi, G. (1988). Sources, procedures and microeconomic effects of innovation. Journal of Economic Literature, 27, 1126–1171.Google Scholar
  16. Etzkowitz, H., & Leydesdorff, L. (1997). Universities and the global knowledge economy: A triple helix of university-industry-government relations. London: Cassell Academic.Google Scholar
  17. Evenson, R., & Kislev, Y. (1975). Agricultural research and productivity. New Haven: Yale University Press.Google Scholar
  18. Evenson, R., & Kislev, Y. (1976). A stochastic model of applied research. Journal of Political Economy, 84(2), 265–281.CrossRefGoogle Scholar
  19. Fleming, L., & Sorenson, O. (2004). Science as a map in technological search. Strategic Management Journal, 25(8–9), 909–928.CrossRefGoogle Scholar
  20. Geuna, A. (1999). The economics of knowledge production: Funding and the structure of university research. Cheltenham: Edward Elgar.Google Scholar
  21. Geuna, A., & Nesta, L. J. J. (2006). University patenting and its effects on academic research: The emerging European evidence. Research Policy, 35(6), 790–807.CrossRefGoogle Scholar
  22. Griliches, Z. (1990). Patent statistics as economic indicators: A survey. Journal of Economic Literature, 28(4), 1661–1707.Google Scholar
  23. Grossman, G., & Helpman, E. (1991). Quality ladders in the theory of growth. Review of Economic Studies, 58(1), 43–61.CrossRefMathSciNetGoogle Scholar
  24. Grupp, H. (1994). The dynamics of science-based innovation reconsidered: Cognitive models and statistical findings. In O. Granstrand (Ed.), Economics of technology (pp. 223–251). Amsterdam: Elsevier.Google Scholar
  25. Grupp, H. (1996). Spillover effects and the science-based of innovations reconsidered: An empirical approach. Journal of Evolutionary Economics, 6, 175–197.CrossRefGoogle Scholar
  26. Grupp, H., & Schmoch, U. (1992). Perception of scientification of innovation as measured by referencing between patents and paper-dynamics in science-based fields of technology. In H. Grupp (Ed.), Dynamics in science-based innovation (pp. 73–128). Berlin: Springer.Google Scholar
  27. Guan, J. C., & Ma, N. (2007). China’s emerging presence in nanoscience and nanotechnology: A comparative bibliometric study of several nanoscience ‘giants’. Research Policy, 36(6), 880–886.CrossRefGoogle Scholar
  28. Guan, J. C., & Wang, G. B. (2010). A comparative study of research performance in nanotechnology for China’s inventor–authors and their non-inventing peers. Scientometrics, 84, 331–343.CrossRefGoogle Scholar
  29. Hassan, M. (2005). Small things and big changes in the developing world. Science, 309(5731), 65–66.CrossRefGoogle Scholar
  30. Hausman, J., Hall, B., & Griliches, Z. (1984). Econometric models for count data with an application to the patents-R&D relationship. Econometrica, 52, 909–938.CrossRefGoogle Scholar
  31. Hullmann, A., & Meyer, M. (2003). Publications and patents in nanotechnology: An overview of previous studies and the state of the art. Scientometrics, 58(3), 507–527.CrossRefGoogle Scholar
  32. Jin, B., Rousseau, R., & Sun, X. (2005). Key labs and open labs in the Chinese scientific research system: Qualitative and quantitative evaluation indicators. Research Evaluation, 14(2), 103–109.CrossRefGoogle Scholar
  33. Klevorick, A., Levin, R., Nelson, R., & Winter, S. (1995). On the sources and significance of inter-industry differences in technological opportunities. Research Policy, 24, 185–205.CrossRefGoogle Scholar
  34. Kline, S. J., & Rosenberg, N. (1986). An overview of innovation. In R. Laudan & N. Rosenberg (Eds.), The positive sum strategy, harnessing technology for economic growth (pp. 275–306). Washington: National Academy Press.Google Scholar
  35. Kondo, M. (1990). Japanese R&D in robotics and genetic engineering. In J. Sigurdson (Ed.), Measuring the dynamics of technological change (pp. 130–145). London: Pinter.Google Scholar
  36. Krahmer, M., & Schmoch, U. (1998). Science-based technologies: University-industry interactions in four fields. Research Policy, 27, 835–851.CrossRefGoogle Scholar
  37. Kumaresan, N., & Miyazaki, K. (1999). An integrated network approach to systems of innovation: The case of robotics in Japan. Research Policy, 28, 563–585.CrossRefGoogle Scholar
  38. Laudel, G. (2006). The art of getting funded: How scientists adapt to their funding conditions. Science and Public Policy, 33(7), 489–504.CrossRefGoogle Scholar
  39. Leydesdorff, L., & Meyer, M. (2003). The triple helix of university-industry-government relations. Scientometrics, 58(2), 191–203.CrossRefGoogle Scholar
  40. Leydesdorff, L., & Zhou, P. (2005). Are the contribution of China and Korea upsetting the world system of science? Scientometric, 63(3), 617–630.CrossRefGoogle Scholar
  41. Leydesdorff, L., & Zhou, P. (2007). Nanotechnology as a field of science: Its delineation in terms of journals and patents. Scientometrics, 70(3), 693–713.CrossRefGoogle Scholar
  42. Mcmillan, S., Narin, F., & Deeds, D. (2000). An analysis of the critical role of public science in innovation: The case of biotechnology. Research Policy, 29, 1–8.CrossRefGoogle Scholar
  43. Meyer, M. (2006). Are patenting scientists the better scholars? An exploratory comparison of inventor–authors with their non-inventing peers in nanoscience and technology. Research Policy, 35(10), 1646–1662.CrossRefGoogle Scholar
  44. Meyer, M., Debackere, K., & Glanzel, W. (2010). Can applied science be ‘good science’? Exploring the relationship between patent citations and citation impact in nanoscience. Scientometrics, 85(2), 527–539.CrossRefGoogle Scholar
  45. Ministry of Education. (1999). The regulation regarding the protection and management of intellectual properties in higher education institutions. Act 3, No. 8120.Google Scholar
  46. Mogoutov, A., & Kahane, B. (2007). Data search strategy for science and technology emergence: A scalable and evolutionary query for nanotechnology tracking. Research Policy, 36(6), 893–903.CrossRefGoogle Scholar
  47. Mowery, D. C. (1983). Industrial research and firm size, survival, and growth in American manufacturing, 1921–1946: An assessment. Journal of Economic History, 43(4), 953–980.CrossRefGoogle Scholar
  48. Narin, F., & Breitzman, A. (1995). Inventive productivity. Research Policy, 24, 507–519.CrossRefGoogle Scholar
  49. Narin, F., & Noma, E. (1985). Is technology becoming science? Scientometrics, 7, 369–381.CrossRefGoogle Scholar
  50. Nelson, R. (1982). The role of knowledge in R&D efficiency. Quarterly Journal of Economics, 97, 453–470.CrossRefGoogle Scholar
  51. Nelson, R., & Rosenberg, N. (1993). Technical innovation and national systems. In R. Nelson (Ed.), National innovation systems: A comparative analysis (pp. 3–21). Oxford: Oxford University Press.Google Scholar
  52. Nightingale, P. (1998). A cognitive model of innovation. Research Policy, 27, 689–702.CrossRefGoogle Scholar
  53. Palmberg, C., Dernis, H., & Miguet, C. 2009 Nanotechnology: An overview based on indicators and statistics [EB/OL]. OECD Science, Technology and Industry working papers, 2009/7. OECD Publishing. doi: 10.1787/223147043844.
  54. Porter, A. L., Youtie, J., Shapira, P., & Schoeneck, D. J. (2008). Refining search terms for nanotechnology. Journal of Nanoparticle Research, 10(5), 715–728.CrossRefGoogle Scholar
  55. Price, D. J. D. (1965). Is technology historically independent of science—a study in statistical historiography. Technology and Culture, 6(4), 553–568.CrossRefGoogle Scholar
  56. Rao, I. K. R., & Srivastava, D. (2010). Growth of journals, articles and authors in malaria research. Journal of Informetrics, 4(3), 249–256.CrossRefMathSciNetGoogle Scholar
  57. Rip, A. (1992). Science and technology as dancing partners. In P. Kroes & M. Bakker (Eds.), Technological development and science in the industrial age (pp. 231–270). Dordrecht: Kluwer.Google Scholar
  58. Rosenberg, N. (1982). How exogenous is science? In N. Rosenberg (Ed.), Inside the black box: Technology and economics (pp. 141–159). Cambridge, MA: Cambridge University.Google Scholar
  59. Sampson, R. (2004). The cost of misaligned governance in R&D alliances. Journal of Law, Economics, and Organization, 20, 484–526.CrossRefGoogle Scholar
  60. Schilling, M. A., & Phelps, C. C. (2007). Interfirm collaboration networks: The impact of large-scale network structure on firm innovation. Management Science, 53(7), 1113–1126.CrossRefGoogle Scholar
  61. Schmoch, U. (1997). Indicators and the relations between science and technology. Scientometrics, 38, 103–116.CrossRefGoogle Scholar
  62. Schummer, J. (2004). Multidisciplinarity, interdisciplinarity, and patterns of research collaboration in nanoscience and nanotechnology. Scientometrics, 59(3), 425–465.CrossRefGoogle Scholar
  63. Stankiewics, R. (1992). Technology as an autonomous, socio-cognitive system. In H. Grupp (Ed.), Dynamics of science based innovation (pp. 19–44). Berlin: Springer.Google Scholar
  64. Stefano, B., & Christian, C. (2010). Tracing the links between science and technology: An exploratory analysis of scientists’ and inventors’ networks. Research Policy, 39, 14–26.CrossRefGoogle Scholar
  65. Stuart, T. E. (2000). Interorganizational alliances and the performance of firms: A study of growth and innovation rates in a high technology industry. Strategic Management Journal, 21, 791–812.CrossRefGoogle Scholar
  66. Tseng, F. M., Hsieh, C. H., Peng, Y. N., & Yi-Wei Chua, Y. W. (2011). Using patent data to analyze trends and the technological strategies of the amorphous silicon thin-film solar cell industry. Technological Forecasting and Social Change, 78(2), 332–345.CrossRefGoogle Scholar
  67. Vanlooy, B., et al. (2003). Do science–technology interactions pay off when developing technology? An exploratory investigation of 10 science-intensive technology domains. Scientometrics, 57(3), 355–367.CrossRefGoogle Scholar
  68. Vanlooy, B., et al. (2006). Scientific capabilities and technological performance of national innovation systems: An exploration of emerging industrial relevant. Scientometrics, 66(2), 295–310.CrossRefGoogle Scholar
  69. Wang, G. B., & Guan, J. C. (2010). The role of patenting activity for scientific research: A study of academic inventors from China’s nanotechnology. Journal of Informetrics, 4, 338–350.CrossRefMathSciNetGoogle Scholar
  70. Wong, C. Y., & Goh, K. L. (2009). Modeling the dynamics of science and technology diffusion of selected Asian countries using a logistic growth function. Asian Journal of Technology Innovation, 17(1), 75–100.CrossRefGoogle Scholar
  71. Wong, C. Y., & Goh, K. L. (2010). Modeling the behaviour of science and technology: Self-propagating growth in the diffusion process. Scientometrics, 84, 669–686.CrossRefGoogle Scholar
  72. Zhao, W., & Watanabe, C. (2008). A comparison of institutional systems affecting software advancement in China and India: The role of outsourcing from Japan and the US. Technology in Society, 30, 429–436.CrossRefGoogle Scholar
  73. Ziman, J. (1996). “Post-academic science”: Constructing knowledge with networks and norms. Science Studies, 9(1), 67–80.Google Scholar
  74. Zitt, M., & Bassecoulard, E. (2006). Delineating complex scientific fields by an hybrid lexical-citation method: An application to nanosciences. Information Processing & Management, 42(6), 1513–1531.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2011

Authors and Affiliations

  1. 1.School of ManagementFudan UniversityShanghaiPeople’s Republic of China
  2. 2.School of ManagementGraduate University, Chinese Academy of SciencesBeijingPeople’s Republic of China

Personalised recommendations