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How the analysis of transitionary references in knowledge networks and their centrality characteristics helps in understanding the genesis of growing technology areas

A Correction to this article was published on 05 June 2018

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Abstract

Since early 1960s, there has been a growing interest in the emergence and development of new technologies accompanied by a strong wish from decision makers to govern related processes at the corporate and national levels. One of the key categories that appeared to set up analytical and regulatory frameworks was the ‘advanced technology’ category. Primarily associated with computer electronics and microelectronics, it soon had new meanings derived from a variety of professional discussions primarily in the social sciences. Later in a new term, ‘emerging technologies’, appeared to highlight the speed of change in a wide range of promising research areas. This paper focuses on the evolution of academic discussions concerning advanced and emerging technologies in social sciences literature for the period from 1955 until 2015. In order to identify whether studies in these areas constitute separate research fields, the paper studies the evolution of co-citation networks and the centrality characteristics of transitionary references. It was shown that social studies in emerging technologies demonstrate better consistency in background in literature. However, an analysis of transitionary references and their centrality characteristics can hardly confirm the existence of separate research fields in both cases. The suggested method for the identification and tracking of papers mediating ongoing discussions in a selected knowledge network may be helpful in understanding the evolution of weakly conceptualized and growing research areas.

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Change history

  • 05 June 2018

    In the original publication of the article, the project ID was published incorrectly in the Acknowledgements section. The correct Acknowledgement is given in this correction.

Notes

  1. VOSviewer is open-access software for mapping bibliometric data available at: http://www.vosviewer.com.

References

  • Abercrombie, R., Udoeyop, A., & Schlicher, B. (2012). A study of scientometric methods to identify emerging technologies via modelling of milestones. Scientometrics, 91(2), 327–342.

    Article  Google Scholar 

  • Anderson, P., & Tushman, M. L. (1990). Technological discontinuities and dominant designs: A cyclical model of technological change. Administrative Science Quarterly, 35(4), 604–633.

    Article  Google Scholar 

  • Ault, G. (1968). Engineering mechanisms and materials. Selected technology for electric power industry. In Proceedings of the NASA SP-5057, Cleveland, OH, USA.

  • Bainbridge, W. (2002). Public attitudes toward nanotechnology. Journal of Nanoparticle Research, 4, 561–570.

    Article  Google Scholar 

  • Baldwin, J., & Da Pont, M. (1996). Innovation in Canadian manufacturing enterprises: Survey of innovation and advanced technology 1993. Cat. No. 88-513-XPB, Statistics Canada, Ottawa.

  • Baldwin, J. R., & Sabourin, D. (2002). Advanced technology use and firm performance in Canadian manufacturing in the 1990s. Industrial and Corporate Change, 11(4), 761–789.

    Article  Google Scholar 

  • Bavelas, A. (1948). A mathematical model for group structures. Human Organization, 7, 16–30.

    Article  Google Scholar 

  • Bavelas, A. (1950). Communication patterns in task oriented groups. Journal of the Acoustical Society of America, 22, 271–282.

    Article  Google Scholar 

  • Beck, U. (1992). Risk society: Towards a new modernity. London: Sage.

    Google Scholar 

  • Borgatti, S. P., & Everett, M. G. (2006). A graph-theoretic perspective on centrality. Social Networks, 28(4), 466–484.

    Article  Google Scholar 

  • Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks. London: Sage.

    Google Scholar 

  • Bowman, D., & Hodge, G. (2006). Nanotechnology: Mapping the wild regulatory frontier. Futures, 38, 1060–1073.

    Article  Google Scholar 

  • Boyer, K., Leong, G., Ward, P., & Krajewski, L. (1997). Unlocking the potential of advanced manufacturing technologies. Journal of Operations Management, 15, 331–347.

    Article  Google Scholar 

  • Burt, R. S. (1992). Structural holes. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Burt, R. S. (2002). The social capital of structural holes. In M. F. Guillén, R. Collins, P. England, & M. Russell (Eds.), New directions in economic sociology (pp. 203–247). Thousand Oaks, CA: Sage Foundation.

    Google Scholar 

  • Cobb, M., & Macoubrie, J. (2004). Public perceptions about nanotechnology: Risks, benefits and trust. Journal of Nanoparticle Research, 6(4), 395–405.

    Article  Google Scholar 

  • Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152.

    Article  Google Scholar 

  • Cohen, W., & Levinthal, D. (1990b). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152.

    Article  Google Scholar 

  • Cole, S. (1983). The hierarchy of the sciences? The American Journal of Sociology, 89, 111–139.

    Article  Google Scholar 

  • Daim, T., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73, 981–1012.

    Article  Google Scholar 

  • Dangayach, G., & Deshmukh, S. (2004). Advanced manufacturing technologies: Evidences from Indian automobile companies. International Journal of Manufacturing Technology and Management, 6(5), 426–433.

    Article  Google Scholar 

  • Davis, F., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.

    Article  Google Scholar 

  • De Bellis, N. (2009). Bibliometrics and citation analysis: From the science citation index to cybermetrics. Lanham, Maryland, Toronto, Plymouth, UK: Scarecrow Press.

    Google Scholar 

  • Dernis, H., Squicciarini, M., & de Pinho, R. (2016). Detecting the emergence of technologies and the evolution and co-development trajectories in science (DETECTS): a ‘burst’analysis-based approach. The Journal of Technology Transfer, 41(5), 930–960.

    Article  Google Scholar 

  • DiMaggio, P., & Powell, W. W. (1983). The iron cage revisited: Collective rationality and institutional isomorphism in organizational fields. American Sociological Review, 48(2), 147–160.

    Article  Google Scholar 

  • Dosi, G. (1982). Technological paradigms and technological trajectories. Research Policy, 11, 147–162.

    Article  Google Scholar 

  • Fagerberg, J., Fosaas, M., & Sapprasert, K. (2012a). Innovation: Exploring the knowledge base. Research Policy, 41(7), 1132–1153.

    Article  Google Scholar 

  • Fagerberg, J., Landström, H., & Martin, B. R. (2012b). Exploring the emerging knowledge base of ‘the knowledge society’. Research Policy, 41(7), 1121–1131.

    Article  Google Scholar 

  • Fagerberg, J., & Verspagen, B. (2009). Innovation studies—The emerging structure of a new scientific field. Research Policy, 38(2), 218–233.

    Article  Google Scholar 

  • Frickel, S., & Gross, N. (2005). A general theory of scientific/intellectual movements. American Sociological Review, 70(2), 204–232.

    Article  Google Scholar 

  • Garcia, R., & Calantone, R. (2002). A critical look at technological innovation typology and innovativeness terminology: A literature review. Journal of Product Innovation Management, 19(2), 110–132.

    Article  Google Scholar 

  • Glänzel, W. (1996). A bibliometric approach to social sciences. National research performances in 6 selected social science areas, 1990–1992. Scientometrics, 35(3), 291–307.

    Article  Google Scholar 

  • Glanzel, W., & Schoepflin, U. (1995). A bibliometric study on ageing and reception processes of scientific literature. Journal of Information Science, 21(1), 37–53.

    Article  Google Scholar 

  • Gmür, M. (1973). Co-citation analysis and the search for invisible colleges: A methodological evaluation. Scientometrics, 51(1), 27–57.

    Google Scholar 

  • Gokhberg, L., Fursov, K., Miles, I., & Perani, G. (2013). Developing and using indicators of emerging and enabling technologies. In F. Gault (Ed.), Handbook of innovation indicators and measurement (pp. 349–380). Cheltenham: Edward Elgar.

    Google Scholar 

  • Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17(S2), 109–122.

    Article  Google Scholar 

  • Halaweh, M. (2013). Emerging technology: What is it? Journal of Technology Management and Innovation, 8(3), 108–115.

    Article  Google Scholar 

  • Hung, S.-C., & Chu, Y.-Y. (2006). Stimulating new industries from emerging technologies: Challenges for the public sector. Technovation, 26(1), 104–110.

    Article  Google Scholar 

  • Kadyrova, A., & Fursov, K. (2016). Evolution of advanced technology studies: Searching for a communication core. In Supplementary proceedings of the 5th international conference on analysis of images, social networks and texts (AIST-SUP 2016), Yekaterinburg, Russia, April 79 (pp. 51–61).

  • Keller, W. (2004). International technology diffusion. Journal of economic literature, 42(3), 752–782.

    MathSciNet  Article  Google Scholar 

  • Latour, B. (1987). Science in action: How to follow scientists and engineers through society. Cambridge: Harvard University Press.

    Google Scholar 

  • Leavitt, H. (1951). Some effects of communication patterns on group performance. Journal of Abnormal and Social Psychology, 46, 38–50.

    Article  Google Scholar 

  • Leydesdorff, L. (2007). Betweenness centrality as an indicator of the interdisciplinarity of Scientific Journals. Journal of the American Society for Information Science and Technology, 58(9), 1303–1319.

    Article  Google Scholar 

  • Malerba, F. (2002). Sectoral systems of innovation and production. Research Policy, 31, 247–264.

    Article  Google Scholar 

  • Manyika J., Chui M., Bughin J., Dobbs R., Bisson P., & Marrs, A. (2013). Disruptive technologies: Advances that will transform life, business, and the global economy. McKinsey Global Institute. http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/disruptive-technologies.

  • Marshakova-Shaikevich, I. (1973). Sistema svyazey mezhdu documentami, postroennaya na osnove ssylok: po dannym Science citation index. Nauchno-Tehnicheskaya Informatsiya, 2(6), 3–8 (in Russian).

    Google Scholar 

  • Martin, B. (1995). Foresight in science and technology. Technology Analysis & Strategic Management, 7(2), 139–168.

    Article  Google Scholar 

  • Merton, R. (1988). The Matthew effect in science, 2: Cumulative advantage and the symbolism of intellectual property. ISIS, 79(299), 606–623.

    Article  Google Scholar 

  • Mullins, N. C. (1972). The development of a scientific specialty: The phage group and the origins of molecular biology. Minerva, 10(1), 51–82.

    Article  Google Scholar 

  • Mullins, N. C. (1973). The development of specialties in social science: The case of ethnomethodology. Science Studies, 3(3), 245–273.

    Article  Google Scholar 

  • OECD. (1994). Frascati manual 1993: Proposed standard practice for surveys of research and experimental development. Paris: OECD Publishing.

    Google Scholar 

  • OECD. (2013). OECD science, technology and industry scoreboard 2013: Innovation for growth. Paris: OECD Publishing.

    Book  Google Scholar 

  • Porter, A. L., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81(3), 719–745.

    Article  Google Scholar 

  • Porter, A. L., Roessner, J. D., Jin, X.-Y., & Newman, N. C. (2002). Measuring national emerging technology capabilities. Science and Public Policy, 29(3), 189–200.

    Article  Google Scholar 

  • Porter, A. L., Youtie, J., Shapira, P., & Schoeneck, D. J. (2008). Refining search terms for nanotechnology. Journal of Nanoparticle Research, 10(5), 715–728.

    Article  Google Scholar 

  • Powell, W. W., & DiMaggio, P. J. (Eds.). (2012). The new institutionalism in organizational analysis. Chicago: University of Chicago Press.

    Google Scholar 

  • Renn, O., & Roco, M. (2006). Nanotechnology and the need for risk governance. Journal of Nanoparticle Research, 8, 153–191.

    Article  Google Scholar 

  • Robinson, A. L. (1974). Energy storage. II. Developing advanced technologies. Science, 184(4139).

  • Robinson, D., & Propp, T. (2008). Multi-path mapping for alignment strategies in emerging science and technologies. Technological Forecasting and Social Change, 75(4), 517–538.

    Article  Google Scholar 

  • Rotolo, D., Hicks, D., & Martin, B. (2015). What is an emerging technology? Research Policy, 44(10), 1827–1843.

    Article  Google Scholar 

  • Scheufele, D., & Lewenstein, B. (2005). The public and nanotechnology: How citizens make sense of emerging technologies. Journal of Nanoparticle Research, 7, 659–667.

    Article  Google Scholar 

  • Schummer, J. (2004). Multidisciplinarity, interdisciplinarity, and patterns of research collaboration in nanoscience and nanotechnology. Scientometrics, 59(3), 425–465.

    Article  Google Scholar 

  • Scott, C. (1973). Health care delivery and advanced technology. Science, 180(4039), 1339–1342.

    Article  Google Scholar 

  • SEC. (2009). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the committee of the regions. In Preparing for our future: Developing a common strategy for key enabling technologies in the EU (1257). http://eur-lex.europa.eu/legalcontent/EN/TXT/?uri=CELEX:52009DC0512.

  • 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. Technology Forecasting and of Social Change, 78(2), 274–282.

    Article  Google Scholar 

  • Slovic, P., & Weber, E. U. (2002). Perception of risk posed by extreme events. The Conference on risk management strategies in an uncertain world, April 12–13, 2002, Palisades, New York, 1–21.

  • Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(2), 265–269.

    Article  Google Scholar 

  • Small, H. (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science, 50(9), 799–813.

    Article  Google Scholar 

  • Small, H. (2004). On the shoulders of Robert Merton: Towards a normative theory of citation. Scientometrics, 60(1), 71–79.

    Article  Google Scholar 

  • Small, H., Boyack, K. W., & Klavans, R. (2014). Identifying emerging topics in science and technology. Research Policy, 43(8), 1450–1467.

    Article  Google Scholar 

  • Smith, S. (1950). Communication pattern and the adaptability of task-oriented groups: An experimental study. Cambridge, MA: Group Networks Laboratory, Research Laboratory of Electronics, Massachusetts Institute of Technology.

    Google Scholar 

  • Solo, R. (1966). The capacity to assimilate an advanced technology. American Economic Review, 56, 91–97.

    Google Scholar 

  • Teece, D. (1986). Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy, 15(6), 285–305.

    Article  Google Scholar 

  • Tushman, M. L., & Anderson, P. (1986). Technological discontinuities and organizational environments. Administrative Science Quarterly, 31(3), 439–465.

    Article  Google Scholar 

  • Utterback, J. (1994a). Mastering the dynamics of innovation. Boston: Harvard Business School Press.

    Google Scholar 

  • Utterback, J. M. (1994b). Mastering the dynamics of innovation: How companies can seize opportunities in the face of technological change. Boston, MA: Harvard Business School Press.

    Google Scholar 

  • van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.

    Article  Google Scholar 

  • Venables, P. (1962). The colleges of advanced technologies. Chemistry & Industry, 36, 1596–1599.

    Google Scholar 

  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.

    Article  Google Scholar 

  • Von Hippel, E. (1988). The sources of innovation. New York: Oxford University Press.

    Google Scholar 

  • Wang, J. (2013). Citation time window choice for research impact evaluation. Scientometrics, 94, 851–872.

    Article  Google Scholar 

  • Wang, L., Notten, A., & Surpatean, A. (2013). Interdisciplinarity of nano research fields: A keyword mining approach. Scientometrics, 94(3), 877–892.

    Article  Google Scholar 

  • Wernimont, P., & Campbell, J. (1968). Signs, samples and criteria. Journal of Applied Psychology, 55(5), 372–376.

    Article  Google Scholar 

  • Whitley, R. (2000). The intellectual and social organization of the sciences. Oxford University Press on Demand.

  • Yan, E., & Ding, Y. (2009). Applying centrality measures to impact analysis: A coauthorship network analysis. Journal of the American Society for Information Science and Technology, 60(10), 2107–2118.

    Article  Google Scholar 

  • Youssef, M. (1992). Getting to know advanced manufacturing technologies. Industrial Enginerering, 24(2), 40–42.

    Google Scholar 

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Acknowledgements

The research leading to these results was supported by the Ministry of Education and Science of the Russian Federation (Project ID: 14.602.21.0017). We thank Prof. Robert Tijssen for his reflections on the initial idea of this study, Prof. Thomas Thurner and Ms. Ekaterina Dyachenko for the fruitful discussions on the discussed issues and two anonymous referees for their critical notes and comments that greatly improved the manuscript.

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Correspondence to Konstantin Fursov.

Appendix: Descriptive statistics of references citation values

Appendix: Descriptive statistics of references citation values

Period Min Max Average SD
Advanced technology
 Before 1990 2 2 2.00 0
 1991–2010 2 3 2.07 0.26
 2001–2010 2 6 2.15 0.51
 2011–2016 2 6 2.60 1.05
Emerging technology
 Before 1990 2 4 2.64 0.84
 1991–2010 2 2 2.00 0
 2001–2010 2 8 2.27 0.64
 2011–2016 2 8 2.40 0.8

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Fursov, K., Kadyrova, A. How the analysis of transitionary references in knowledge networks and their centrality characteristics helps in understanding the genesis of growing technology areas. Scientometrics 111, 1947–1963 (2017). https://doi.org/10.1007/s11192-017-2340-z

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Keywords

  • Advanced technology
  • Emerging technology
  • Graph analysis
  • Bibliometric analysis
  • Co-citation
  • Betweenness centrality