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Scientometrics

, Volume 115, Issue 1, pp 35–49 | Cite as

An indicator of technical emergence

  • Stephen F. CarleyEmail author
  • Nils C. Newman
  • Alan L. Porter
  • Jon G. Garner
Article

Abstract

Developing useful intelligence on scientific and technological emergence challenges those who would manage R&D portfolios, assess research programs, or manage innovation. Recently, the U.S. Intelligence Advanced Research Projects Activity Foresight and Understanding from Scientific Exposition Program has explored means to detect emergence via text analyses. We have been involved in positing conceptual bases for emergence, framing candidate indicators, and devising implementations. We now present a software script to generate a family of Emergence Indicators for a topic of interest. This paper offers some background, then discusses the development of this script through iterative rounds of testing, and then offers example findings. Results point to promising and actionable intelligence for R&D decision-makers.

Keywords

Emergence indicators Technological emergence Scientific emergence Tech mining 

Notes

Acknowledgements

This material is based upon work supported by the National Science Foundation under EAGER Award #: 1645237 for a Project, “Using the ORCID ID and Emergence Scoring to Study Frontier Researchers.” Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

References

  1. Alexander, J., John, C., Newman, N. C., Porter, A. L., & Roessner, D. (2012). Emergence as a conceptual framework for understanding scientific and technological progress. In PICMET (Portland International Conference on Management of Engineering and Technology), Vancouver, https://www.researchgate.net/profile/Jeffrey_Alexander/publication/248391856_Emergence_as_a_Conceptual_Framework_for_Understanding_Scientific_and_Technological_Progress/links/550847050cf26ff55f80c7b9.pdf.
  2. Alexander, J., Murdick, D., Babko-Malaya, O., & Boyack, K. (2013). Detecting and evaluating the emergence of science and technology: Activities in foresight and understanding from scientific exposition. In Global Tech Mining Conference, Atlanta.Google Scholar
  3. An, L., Lin, X., Yu, C., & Zhang, X. (2015). Measuring and visualizing the contributions of Chinese and American LIS research institutions to emerging themes and salient themes. Scientometrics, 105(3), 1605–1634.CrossRefGoogle Scholar
  4. Arora, S., Porter, A. L., Youtie, J., & Shapira, P. (2013). Capturing new developments in an emerging technology: An updated search strategy for identifying nanotechnology research outputs. Scientometrics, 95(1), 351–370.CrossRefGoogle Scholar
  5. Carley, S. F., Porter, Alan L., Newman, N. C., & Garner, J. G. (2017). A measure of staying power: Is the persistence of emergent concepts more significantly influenced by technical domain or scale? Scientometrics, 111(3), 2077–2087.CrossRefGoogle Scholar
  6. de Haan, J. (2006). How emergence arises. Ecological Complexity, 3(4), 293–301.CrossRefGoogle Scholar
  7. Ellis, A. K. (2010). Teaching and learning elementary social studies (9th ed.). New York: Pearson.Google Scholar
  8. Foresight and Understanding from Scientific Exposition (FUSE). (2014). http://www.iarpa.gov/index.php/research-programs/fuse. Accessed March 18, 2016.
  9. Garner, J., Carley, S. F., Porter, A. L., & Newman, N. C. (2017). Technological emergence indicators using emergence scoring. In Portland International Conference on Management of Engineering and Technology (PICMET), Portland, OR.Google Scholar
  10. Goldstein, J. (1999). Emergence as a construct: History and issues. Emergence, 1(1), 49–72.MathSciNetCrossRefGoogle Scholar
  11. Guo, Y., Chen, X., Huang, L., & Porter, A. L. (2012a). Empirically informing a technology delivery system model for an emerging technology: Illustrated for dye-sensitized solar cells. R&D Management, 42(2), 133–149.CrossRefGoogle Scholar
  12. Guo, Y., Huang, L., & Porter, A. L. (2010). The research profiling method applied to nano-enhanced. Thin-film Solar Cells, R&D Management, 40(2), 195–208.Google Scholar
  13. Guo, Y., Ma, T., Porter, A. L., & Huang, L. (2012b). Text mining of information resources to inform forecasting innovation pathways. Technology Analysis & Strategic Management, 24(8), 843–861.CrossRefGoogle Scholar
  14. Ma, T., Porter, A. L., Guo, Y., Ready, J., Xu, C., & Gao, L. (2014). A technology opportunities analysis model: Applied to dye-sensitized solar cells for China, Technology Analysis and Strategic Management, 26(1), 84–107. http://www.tandfonline.com/doi/full/10.1080/09537325.2013.850155.
  15. Martin, B. R. (1995). Foresight in science and technology. Technology Analysis & Strategic Management, 7(2), 139–168.CrossRefGoogle Scholar
  16. O’Brien, J. J., Carley, S. F., & Porter, A. L. (2013). ClusterSuite [computer software], Atlanta, GA [available via VPInstute.org].Google Scholar
  17. O’Regan, B., & Gratzel, M. (1991). A low-cost, high-efficiency solar-cell based on dye-sensitized colloidal TiO2 films. Nature, 353(6346), 737–740.CrossRefGoogle Scholar
  18. Roper, T., Cunningham, S., Porter, A. L., Mason, T., Rossini, F., & Banks, J. (2011). Forecasting and management of technology (2nd ed.). New York: Wiley.CrossRefGoogle Scholar
  19. Rotolo, D., Hicks, D., & Martin, B. R. (2015). What is an emerging technology? Research Policy, 44(10), 1827–1843.CrossRefGoogle Scholar
  20. Search Technology. (2012). www.thevantagepoint.com. Accessed March 19, 2016.
  21. Small, H., Boyack, K., & Klavens, R. (2014). Identifying emerging topics in science and technology. Research Policy, 43(8), 1450–1467.CrossRefGoogle Scholar
  22. Zhang, Y., Porter, A. L., Hu, Z., Guo, Y., & Newman, N. C. (2014a). “Term clumping” for technical intelligence: A case study on dye-sensitized solar cells. Technology Forecasting & Social Change, 85, 26–39.  https://doi.org/10.1016/j.techfore.2013.12.019.CrossRefGoogle Scholar
  23. Zhang, Y., Zhou, X., Porter, A. L., Vicente Gomila, J. M., & Yan, A. (2014b). Triple helix innovation in china’s dye-sensitized solar cell industry: Hybrid methods with semantic TRIZ and technology roadmapping. Scientometric, 99(1), 55–75.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2018

Authors and Affiliations

  1. 1.Enterprise Innovation Institute, Georgia TechAtlantaUSA
  2. 2.Intelligent Information Services CorporationAtlantaUSA
  3. 3.Search Technology, IncNorcrossUSA
  4. 4.Georgia TechAtlantaUSA

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