Scientometrics

, Volume 112, Issue 3, pp 1691–1720 | Cite as

Tracing the knowledge-building dynamics in new stem cell technologies through techno-scientific networks

Article

Abstract

This study assesses the knowledge-building dynamics of emerging technologies, their participating country-level actors, and their interrelations. We examine research on induced pluripotent stem (iPS) cells, a recently discovered stem cell species. Compared to other studies, our approach conflates the totality of publications and patents of a field, and their references, into single “techno-scientific networks” across intellectual bases (IB) and research fronts (RF). Diverse mapping approaches—co-citation, direct citation, and bibliographic coupling networks—are used, driven by the problems tackled by iPS cell researchers. Besides the study of the field of iPS cells as a whole, we assessed the roles of relevant countries in terms of “knowledge exploration,” “knowledge nurturing,” “knowledge exploitation,” and cognitive content. The results show that a fifth of nodes in IB and edges in RF interconnect science (S) and technology (T). S and T domains tell different, yet complementing stories: S overstresses upstream activities, and T captures the increasing influential role of application domains and general technologies. Both S and T reflect the path-dependent nature of iPS cells in embryonic stem cell technologies. Building on the feedback between IB and RF, we examine the dominating role of the United States. Japan, the pioneer, falls behind in quantity, yet its global influence remains intact. New entrants, such as China, are advancing rapidly, yet, cognitively, the bulk of efforts are still upstream. Our study demonstrates the need for bibliometric assessment studies to account for S&T co-evolution. The multiple data source-based, integrated bibliometric approaches of this study are initial efforts toward this direction.

Keywords

Techno-scientific networks Knowledge-building Dynamics Emerging technologies Stem cells 

Notes

Acknowledgements

We thank the Editor and anonymous reviewers for their helpful comments. This work was financially supported by MEXT/JSPS World Premier International Research Center (WPI) Initiative [AAR] and by MEXT/JSPS Kakenhi Grant No. 26301022 [AAR, SS] (Project leader Prof. Shintaro Sengoku). Initial stages of this study were supported by Cabinet Office of Japan/JSPS Funding Program for World-Leading Next-Generation Innovative R&D on Science and Technology (NEXT Program, Grant Number LZ009) [AAR, SS]. An earlier version of this manuscript was presented at the Portland International Center for Management of Engineering and Technology (PICMET) 2014 conference (Portland, US). All remaining errors are our own.

References

  1. Alkemade, F., & Suurs, R. A. (2012). Patterns of expectations for emerging sustainable technologies. Technological Forecasting and Social Change, 79(3), 448–456.CrossRefGoogle Scholar
  2. Anaya-Ruiz, M., & Perez-Santos, M. (2015). Innovation status of gene therapy for breast cancer. Asian Pacific Journal of Cancer Prevention, 16(9), 4133–4136.CrossRefGoogle Scholar
  3. Arthur, W. B. (2009). The nature of technology: What it is and how it evolves. New York: Simon and Schuster.Google Scholar
  4. Ávila-Robinson, A. (2013). Understanding the dynamics of emerging technologies through knowledge structures: The case of micro/nanotechnologies. Tokyo Institute of Technology (unpublished dissertation).Google Scholar
  5. Ávila-Robinson, A., & Miyazaki, K. (2013a). Evolutionary paths of change of emerging nanotechnological innovation systems—The case of ZnO nanostructures. Scientometrics, 95(3), 829–849.CrossRefGoogle Scholar
  6. Ávila-Robinson, A., & Miyazaki, K. (2013b). Dynamics of scientific knowledge bases as proxies for discerning technological emergence—The case of MEMS/NEMS technologies. Technological Forecasting and Social Change, 80(6), 1071–1084.CrossRefGoogle Scholar
  7. Ávila-Robinson, A., & Miyazaki, K. (2014). Assessing nanotechnology potentials: interplay between the paths of knowledge evolution and the patterns of competence building. International Journal of Technology Intelligence and Planning, 10(1), 1–28.CrossRefGoogle Scholar
  8. Ávila-Robinson, A., & Sengoku, S. (2017). Multilevel exploration of the realities of interdisciplinary research centers for the management of knowledge integration. Technovation. doi: 10.1016/j.technovation.2017.01.003.
  9. Barfoot, J., Kemp, E., Doherty, K., Blackburn, C., Sengoku, S., van Servellen, A., et al. (2013). Stem cell research: Trends and perspectives on the evolving international landscape. Amsterdam: Elsevier BV.Google Scholar
  10. Bengisu, M., & Nekhili, R. (2006). Forecasting emerging technologies with the aid of science and technology databases. Technological Forecasting and Social Change, 73(7), 835–844.CrossRefGoogle Scholar
  11. Bergek, A., Hekkert, M., Jacobsson, S., Markard, J., Sandén, B., & Truffer, B. (2015). Technological innovation systems in contexts: Conceptualizing contextual structures and interaction dynamics. Environmental Innovation and Societal Transitions, 16, 51–64.CrossRefGoogle Scholar
  12. Birkinshaw, J., Bessant, J., & Delbridge, R. (2007). Finding, forming, and performing: Creating networks for discontinuous innovation. California Management Review, 49(3), 67–84.CrossRefGoogle Scholar
  13. Björk, B.-C., & Solomon, D. (2013). The publishing delay in scholarly peer-reviewed journals. Journal of Informetrics, 7(4), 914–923.CrossRefGoogle Scholar
  14. Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). UCINET for windows: Software for social network analysis. Harvard, MA: Analytic Technologies.Google Scholar
  15. Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks. Thousand Oaks, CA: SAGE Publications Limited.Google Scholar
  16. Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37(1), 179–255.CrossRefGoogle Scholar
  17. Bousfield, D., McEntyre, J., Velankar, S., Papadatos, G., Bateman, A., & Cochrane, G., et al. (2016). Patterns of database citation in articles and patents indicate long-term scientific and industry value of biological data resources. F1000Research. doi: 10.12688/f1000research.7911.1.
  18. Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology, 61(12), 2389–2404.CrossRefGoogle Scholar
  19. Breschi, S., & Catalini, C. (2010). Tracing the links between science and technology: An exploratory analysis of scientists’ and inventors’ networks. Research Policy, 39(1), 14–26.CrossRefGoogle Scholar
  20. Breschi, S., Malerba, F., & Orsenigo, L. (2000). Technological regimes and schumpeterian patterns of innovation. The Economic Journal, 110(463), 388–410.CrossRefGoogle Scholar
  21. Callaert, J., Grouwels, J., & Van Looy, B. (2012). Delineating the scientific footprint in technology: Identifying scientific publications within non-patent references. Scientometrics, 91(2), 383–398.CrossRefGoogle Scholar
  22. Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377.CrossRefGoogle Scholar
  23. Chen, S.-H., Huang, M.-H., & Chen, D.-Z. (2012). Identifying and visualizing technology evolution: A case study of smart grid technology. Technological Forecasting and Social Change, 79(6), 1099–1110.CrossRefGoogle Scholar
  24. Chen, C., & Leydesdorff, L. (2014). Patterns of connections and movements in dual-map overlays: A new method of publication portfolio analysis. Journal of the Association for Information Science and Technology, 65(2), 334–351.CrossRefGoogle Scholar
  25. Chiang, S.-Y. (2012). An application of Lotka–Volterra model to Taiwan’s transition from 200 mm to 300 mm silicon wafers. Technological Forecasting and Social Change, 79(2), 383–392.CrossRefGoogle Scholar
  26. Cobo, M., López-Herrera, A., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62, 1382–1402.CrossRefMATHGoogle Scholar
  27. Consoli, D., & Ramlogan, R. (2011). Patterns of organization in the development of medical know-how: The case of glaucoma research. Industrial and Corporate Change, 21(2), 315–343.CrossRefGoogle Scholar
  28. Cozzens, S., Gatchair, S., Kang, J., Kim, K.-S., Lee, H. J., Ordóñez, G., et al. (2010). Emerging technologies: quantitative identification and measurement. Technology Analysis and Strategic Management, 22(3), 361–376.CrossRefGoogle Scholar
  29. Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73(8), 981–1012.CrossRefGoogle Scholar
  30. David, P. A. (1994). Why are institutions the ‘carriers of history’?: Path dependence and the evolution of conventions, organizations and institutions. Structural Change and Economic Dynamics, 5(2), 205–220.CrossRefGoogle Scholar
  31. David, P. A., & Foray, D. (1995). Accessing and expanding the science and technology knowledge base. STI Review, No. 16. Paris: OECD.Google Scholar
  32. Day, G. S., Schoemaker, P. J., & Gunther, R. E. (2004). Wharton on managing emerging technologies. Hoboken, NJ: Wiley.Google Scholar
  33. De Nooy, W., Mrvar, A., & Batagelj, V. (2011). Exploratory social network analysis with Pajek. New York, NY: Cambridge University Press.CrossRefGoogle Scholar
  34. Ebert, A. D., Yu, J., Rose, F. F., Mattis, V. B., Lorson, C. L., Thomson, J. A., et al. (2009). Induced pluripotent stem cells from a spinal muscular atrophy patient. Nature, 457(7227), 277–280.CrossRefGoogle Scholar
  35. Fenn, J., & Raskino, M. (2008). Mastering the hype cycle: how to choose the right innovation at the right time. Boston: Harvard Business Press.Google Scholar
  36. Franco, L. A., Meadows, M., & Armstrong, S. J. (2013). Exploring individual differences in scenario planning workshops: A cognitive style framework. Technological Forecasting and Social Change, 80(4), 723–734.CrossRefGoogle Scholar
  37. Galibert, O., Rosset, S., Tannier, X., & Grandry, F., (2010). Hybrid citation extraction from patents. In N. Calzolari, K. Choukri, B. Maegaard, J. Mariani, S. Piperidis, M. Rosner, D. Tapias (Eds.), LREC 2010, seventh international conference on language resources and evaluation, Valleta, Malta.Google Scholar
  38. Garber, K. (2015). RIKEN suspends first clinical trial involving induced pluripotent stem cells. Nature Biotechnology, 33(9), 890–891.CrossRefGoogle Scholar
  39. Hekkert, M. P., & Negro, S. O. (2009). Functions of innovation systems as a framework to understand sustainable technological change: Empirical evidence for earlier claims. Technological Forecasting and Social Change, 76(4), 584–594.CrossRefGoogle Scholar
  40. Hilgartner, S., & Lewenstein, B. (2004). The speculative world of emerging technologies (unpublished work).Google Scholar
  41. Ho, J.-Y., & O’Sullivan, E. (2017). Strategic standardisation of smart systems: A roadmapping process in support of innovation. Technological Forecasting and Social Change, 115, 301–312.CrossRefGoogle Scholar
  42. Hung, S.-C., & Chu, Y.-Y. (2006). Stimulating new industries from emerging technologies: Challenges for the public sector. Technovation, 26(1), 104–110.CrossRefGoogle Scholar
  43. Inoue, H., Nagata, N., Kurokawa, H., & Yamanaka, S. (2014). iPS cells: A game changer for future medicine. The EMBO Journal, 33(5), 409–417.CrossRefGoogle Scholar
  44. Jacobsson, S. (2008). The emergence and troubled growth of a ‘biopower’innovation system in Sweden. Energy Policy, 36(4), 1491–1508.CrossRefGoogle Scholar
  45. Jansen, D., von Görtz, R., & Heidler, R. (2010). Knowledge production and the structure of collaboration networks in two scientific fields. Scientometrics, 83(1), 219–241.CrossRefGoogle Scholar
  46. Jarneving, B. (2007). Bibliographic coupling and its application to research-front and other core documents. Journal of Informetrics, 1(4), 287–307.CrossRefGoogle Scholar
  47. Kauffman, S., & Macready, W. (1995). Technological evolution and adaptive organizations: Ideas from biology may find applications in economics. Complexity, 1(2), 26–43.MathSciNetCrossRefGoogle Scholar
  48. Keller, J., & Heiko, A. (2014). The influence of information and communication technology (ICT) on future foresight processes—Results from a Delphi survey. Technological Forecasting and Social Change, 85, 81–92.CrossRefGoogle Scholar
  49. Kissin, I. (2015). Scientometrics of drug discovery efforts: Pain-related molecular targets. Drug Design, Development and Therapy, 9(1), 3393–3404.CrossRefGoogle Scholar
  50. Krafft, J., Quatraro, F., & Saviotti, P. P. (2011). The knowledge-base evolution in biotechnology: A social network analysis. Economics of Innovation and New Technology, 20(5), 445–475.CrossRefGoogle Scholar
  51. Kukk, P., Moors, E., & Hekkert, M. (2015). The complexities in system building strategies—the case of personalized cancer medicines in England. Technological Forecasting and Social Change, 98, 47–59.CrossRefGoogle Scholar
  52. Kuusi, O., & Meyer, M. (2007). Anticipating technological breakthroughs: Using bibliographic coupling to explore the nanotubes paradigm. Scientometrics, 70(3), 759–777.CrossRefGoogle Scholar
  53. Larédo, P., Robinson, D. K., Delemarle, A., Lagnau, A., Revollo, M., & Villard, L. (2015). Mapping and characterising the dynamics of emerging technologies to inform policy. Final Report IFRIS Institut Francilien Recherche Innovation Société, Project No. ANR-10-ORA-007.Google Scholar
  54. Lee, P.-C., & Su, H.-N. (2011). Quantitative mapping of scientific research—the case of electrical conducting polymer nanocomposite. Technological Forecasting and Social Change, 78(1), 132–151.CrossRefGoogle Scholar
  55. Leydesdorff, L., & Rafols, I. (2011). Local emergence and global diffusion of research technologies: An exploration of patterns of network formation. Journal of the American Society for Information Science and Technology, 62(5), 846–860.CrossRefGoogle Scholar
  56. Lopez, P. (2009). GROBID: Combining automatic bibliographic data recognition and term extraction for scholarship publications. In Proceedings of the 13th European conference on digital library (ECDL), Corfu, Greece.Google Scholar
  57. Lopez, P. (2010). Automatic extraction and resolution of bibliographical references in patent documents. In H. Cunningham, A. Hanbury, & S. Rüger (Eds.), Advances in multidisciplinary retrieval (pp. 120–135). Berlin: Springer.CrossRefGoogle Scholar
  58. Malerba, F. (2005). Sectoral systems: How and why innovation differs across sectors. In J. Fagerberg, D. C. Mowery, & R. R. Nelson (Eds.), The Oxford handbook of innovation. New York: Oxford University Press.Google Scholar
  59. March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87.CrossRefGoogle Scholar
  60. Markard, J., & Truffer, B. (2008). Technological innovation systems and the multi-level perspective: Towards an integrated framework. Research Policy, 37(4), 596–615.CrossRefGoogle Scholar
  61. Martínez, C. (2011). Patent families: When do different definitions really matter? Scientometrics, 86(1), 39–63.MathSciNetCrossRefGoogle Scholar
  62. McCallum, A. K. (2002). MALLET: A machine learning for language toolkit. http://mallet.cs.umass.edu.
  63. Medcof, J. W. (2010). Exploration, exploitation and technology management. International Journal of Technology Intelligence and Planning, 6(4), 301–316.CrossRefGoogle Scholar
  64. Metcalfe, J. S. (2002). Knowledge of growth and the growth of knowledge. Journal of Evolutionary Economics, 12(1–2), 3–15.CrossRefGoogle Scholar
  65. Metcalfe, J. S., James, A., & Mina, A. (2005). Emergent innovation systems and the delivery of clinical services: The case of intra-ocular lenses. Research Policy, 34(9), 1283–1304.CrossRefGoogle Scholar
  66. Meyer, M. (2000). What is special about patent citations? Differences between scientific and patent citations. Scientometrics, 49(1), 93–123.CrossRefGoogle Scholar
  67. Michel, J., & Bettels, B. (2001). Patent citation analysis. A closer look at the basic input data from patent search reports. Scientometrics, 51(1), 185–201.CrossRefGoogle Scholar
  68. Mina, A., Ramlogan, R., Tampubolon, G., & Metcalfe, J. S. (2007). Mapping evolutionary trajectories: Applications to the growth and transformation of medical knowledge. Research Policy, 36(5), 789–806.CrossRefGoogle Scholar
  69. Miyazaki, K. (1995). Building competences in the firm: Lessons from Japanese and European Optoelectronics. New York: St. Martin’s Press.CrossRefGoogle Scholar
  70. Momeni, A., & Rost, K. (2016). Identification and monitoring of possible disruptive technologies by patent-development paths and topic modeling. Technological Forecasting and Social Change, 104, 16–29.CrossRefGoogle Scholar
  71. Morlacchi, P., & Nelson, R. R. (2011). How medical practice evolves: Learning to treat failing hearts with an implantable device. Research Policy, 40(4), 511–525.CrossRefGoogle Scholar
  72. Murray, F. (2002). Innovation as co-evolution of scientific and technological networks: Exploring tissue engineering. Research Policy, 31(8–9), 1389–1403.CrossRefGoogle Scholar
  73. Nanba, H., Anzen, N., & Okumura, M. (2008). Automatic extraction of citation information in Japanese patent applications. International Journal on Digital Libraries, 9(2), 151–161.CrossRefGoogle Scholar
  74. Neal, H. A., Smith, T. L., & McCormick, J. B. (2008). Beyond Sputnik: US Science policy in the 21st century. Ann Arbor, MI: The University of Michigan Press.CrossRefGoogle Scholar
  75. Nelson, R. R. (2004). The market economy, and the scientific commons. Research Policy, 33(3), 455–471.CrossRefGoogle Scholar
  76. Nelson, R. R., Buterbaugh, K., Perl, M., & Gelijns, A. (2011). How medical know-how progresses. Research Policy, 40(10), 1339–1344.CrossRefGoogle Scholar
  77. NIH. (2017). NIH stem cell information home page. In stem cell information [World Wide Web site]. Bethesda, MD: National Institutes of Health, U.S. Department of Health and Human Services, 2016 [cited January 19, 2017]. http://stemcells.nih.gov/info/basics/1.htm
  78. Perez-Santos, M., Anaya-Ruiz, M., & Bandala, C. (2017). Contribution of Latin American countries to cancer research and patent generation: Recent patents. Recent Patents on Anti-Cancer Drug Discovery, 12(1), 81–93.CrossRefGoogle Scholar
  79. Persson, O. (1994). The intellectual base and research fronts of JASIS 1986–1990. Journal of the American Society for Information Science, 45(1), 31–38.CrossRefGoogle Scholar
  80. Porter, A. L., & Cunningham, S. W. (2004). Tech mining: Exploiting new technologies for competitive advantage. Hoboken, NJ: Wiley.CrossRefGoogle Scholar
  81. Ramlogan, R., & Consoli, D. (2008). Knowledge, understanding and the dynamics of medical innovation. Munich Personal RePEc Archive MPRA Paper No. 9112.Google Scholar
  82. Robinson, D. K., Huang, L., Guo, Y., & Porter, A. L. (2013). Forecasting innovation pathways (FIP) for new and emerging science and technologies. Technological Forecasting and Social Change, 80(2), 267–285.CrossRefGoogle Scholar
  83. Rosenkopf, L. (2000). Managing dynamic knowledge networks. In G. S. Day, P. J. Schoemaker, & R. E. Gunther (Eds.), Wharton on managing emerging technologies (pp. 337–357). New York: Wiley.Google Scholar
  84. Rotolo, D., Hicks, D., & Martin, B. R. (2015). What is an emerging technology? Research Policy, 44(10), 1827–1843.CrossRefGoogle Scholar
  85. Saviotti, P. P. (2007). On the dynamics of generation and utilisation of knowledge: The local character of knowledge. Structural Change and Economic Dynamics, 18(4), 387–408.CrossRefGoogle Scholar
  86. Schiebel, E. (2012). Visualization of research fronts and knowledge bases by three-dimensional areal densities of bibliographically coupled publications and co-citations. Scientometrics, 91(2), 557–566.CrossRefGoogle Scholar
  87. Schmoch, U. (2007). Double-boom cycles and the comeback of science-push and market-pull. Research Policy, 36(7), 1000–1015.CrossRefGoogle Scholar
  88. Scott, C. T., McCormick, J. B., DeRouen, M. C., & Owen-Smith, J. (2011). Democracy derived? New trajectories in pluripotent stem cell research. Cell, 145(6), 820–826.CrossRefGoogle Scholar
  89. Sengoku, S. (2015). Innovation and commercialisation of induced pluripotent stem cells. In A. A. Vertès, N. Qureshi, A. I. Caplan, & E. B. Lee (Eds.), Stem cells in regenerative medicine: Science, regulation and business strategies (pp. 423–446). West Sussex, UK: Wiley.Google Scholar
  90. Shibata, N., Kajikawa, Y., Takeda, Y., & Matsushima, K. (2009). Comparative study on methods of detecting research fronts using different types of citation. Journal of the American Society for Information Science and Technology, 60(3), 571–580.CrossRefGoogle Scholar
  91. Shibata, N., Kajikawa, Y., Takeda, Y., Sakata, I., & Matsushima, K. (2011). Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications. Technological Forecasting and Social Change, 78(2), 274–282.CrossRefGoogle Scholar
  92. Sternitzke, C. (2009). Patents and publications as sources of novel and inventive knowledge. Scientometrics, 79(3), 551–561.CrossRefGoogle Scholar
  93. Suzuki, J., Gemba, K., Tamada, S., Yasaki, Y., & Goto, A. (2006). Analysis of propensity to patent and science-dependence of large Japanese manufacturers of electrical machinery. Scientometrics, 68(2), 265–288.CrossRefGoogle Scholar
  94. Takahashi, K., & Yamanaka, S. (2006). Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell, 126(4), 663–676.CrossRefGoogle Scholar
  95. Takeda, Y., & Kajikawa, Y. (2009). Optics: A bibliometric approach to detect emerging research domains and intellectual bases. Scientometrics, 78(3), 543–558.CrossRefGoogle Scholar
  96. Tamada, S., Naito, Y., Kodama, F., Gemba, K., & Suzuki, J. (2006). Significant difference of dependence upon scientific knowledge among different technologies. Scientometrics, 68(2), 289–302.CrossRefGoogle Scholar
  97. Tushman, M. L., & O’Reilly, C. A. (1996). The ambidextrous organizations: Managing evolutionary and revolutionary change. California Management Review, 38(4), 8–30.CrossRefGoogle Scholar
  98. Upham, S. P., & Small, H. (2010). Emerging research fronts in science and technology: Patterns of new knowledge development. Scientometrics, 83(1), 15–38.CrossRefGoogle Scholar
  99. Van Den Besselaar, P., & Heimeriks, G. (2006). Mapping research topics using word-reference co-occurrences: A method and an exploratory case study. Scientometrics, 68(3), 377–393.CrossRefGoogle Scholar
  100. Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.CrossRefGoogle Scholar
  101. Van Merkerk, R. O., & Robinson, D. K. (2006). Characterizing the emergence of a technological field: Expectations, agendas and networks in Lab-on-a-chip technologies. Technology Analysis and Strategic Management, 18(3–4), 411–428.CrossRefGoogle Scholar
  102. Van Merkerk, R. O., & Smits, R. E. (2008). Tailoring CTA for emerging technologies. Technological Forecasting and Social Change, 75(3), 312–333.CrossRefGoogle Scholar
  103. Verbeek, A., Debackere, K., Luwel, M., Andries, P., Zimmermann, E., & Deleus, F. (2002). Linking science to technology: Using bibliographic references in patents to build linkage schemes. Scientometrics, 54(3), 399–420.CrossRefGoogle Scholar
  104. Walsh, S. T. (2004). Roadmapping a disruptive technology: A case study: The emerging microsystems and top-down nanosystems industry. Technological Forecasting and Social Change, 71(1), 161–185.CrossRefGoogle Scholar
  105. Watatani, K., Xie, Z., Nakatsuji, N., & Sengoku, S. (2013). Global competencies of regional stem cell research: Bibliometrics for investigating and forecasting research trends. Regenerative Medicine, 8(5), 659–668.CrossRefGoogle Scholar
  106. Whitesides, G. (2010). Solving problems. Lab on a Chip, 10(18), 2317–2318.CrossRefGoogle Scholar
  107. Wirth, S., & Markard, J. (2011). Context matters: How existing sectors and competing technologies affect the prospects of the Swiss Bio-SNG innovation system. Technological Forecasting and Social Change, 78(4), 635–649.CrossRefGoogle Scholar
  108. Yan, E. (2014). Research dynamics: Measuring the continuity and popularity of research topics. Journal of Informetrics, 8(1), 98–110.CrossRefGoogle Scholar
  109. Ziman, J. (2003). Technological innovation as an evolutionary process. Cambridge: Cambridge University Press.Google Scholar
  110. Zitt, M., Lelu, A., & Bassecoulard, E. (2011). Hybrid citation-word representations in science mapping: Portolan charts of research fields? Journal of the American Society for Information Science and Technology, 62(1), 19–39.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2017

Authors and Affiliations

  1. 1.Institute for Integrated Cell-Material Sciences (WPI-iCeMS)Kyoto UniversityKyotoJapan
  2. 2.Graduate School of Innovation ManagementTokyo Institute of TechnologyTokyoJapan
  3. 3.Graduate School of ManagementKyoto UniversityKyotoJapan

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