, Volume 83, Issue 3, pp 765–781 | Cite as

An advanced diffusion model to identify emergent research issues: the case of optoelectronic devices

  • Edgar Schiebel
  • Marianne Hörlesberger
  • Ivana Roche
  • Claire François
  • Dominique Besagni


Scientific progress in technology oriented research fields is made by incremental or fundamental inventions concerning natural science effects, materials, methods, tools and applications. Therefore our approach focuses on research activities of such technological elements on the basis of keywords in published articles. In this paper we show how emerging topics in the field of optoelectronic devices based on scientific literature data from the PASCAL-database can be identified. We use Results from PROMTECH project, whose principal objective was to produce a methodology allowing the identification of promising emerging technologies. In this project, the study of the intersection of Applied Sciences as well as Life (Biological & Medical) Sciences domains and Physics with bibliometric methods produced 45 candidate technological fields and the validation by expert panels led to a final selection of 10 most promising ones. These 45 technologies were used as reference fields. In order to detect the emerging research, we combine two methodological approaches. The first one introduces a new modelling of field terminology evolution based on bibliometric indicators: the diffusion model and the second one is a diachronic cluster analysis. With the diffusion model we identified single keywords that represent a high dynamic of the mentioned technology elements. The cluster analysis was used to recombine articles, where the identified keywords were used to technological topics in the field of optoelectronic devices. This methodology allows us to answer the following questions: Which technological aspects within our considered field can be detected? Which of them are already established and which of them are new? How are the topics linked to each other?


Emerging research issues Emerging technologies Science dynamics Diffusion model Migration of terms Diffusion stages Diachronic cluster analysis Optoelectronic devices Evolution of a technological field 



This work was carried out thanks to a European Union funding: Project No. 15615 (NEST)—6th Research and Development Framework Plan. The project acronym is PROMTECH, and the project full title is “Identification and Assessment of Promising and Emerging Technological Fields in Europe”. The Consortium was composed by the Austrian Research Centers GmbH—ARC (Vienna, Austria), the Fraunhofer Institut für Systemtechnik und Innovationsforschung (Karlsruhe, Germany) and the Institut de l’Information Scientifique et Technique—INIST-CNRS (Nancy, France). We would also to warmly thank our colleagues from ARC and INIST-CNRS, particularly Mrs. Nathalie Vedovotto, that took part very actively to the different project steps by bringing us their scientific and documentary expertises.


  1. Armstrong, J. S., & Green, K. C. (2007).
  2. Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting & Social Change, 73, 981–1012.CrossRefGoogle Scholar
  3. Ferber, R. (2003). Information retrieval. Suchmodelle und Data-Mining-Verfahren für Textsammlungen und das Web, Heidelberg: dpunkt. Google Scholar
  4. Kajikawa, Y., Yoshikawa, J., Takeda, Y., & Matushima, K. (2008). Tracking emerging technologies in energy research: Toward a roadmap for sustainable energy. Technological Forecasting & Social Change, 75, 771–782.CrossRefGoogle Scholar
  5. Lancaster, F. W., & Lee, J. L. (1985). Bibliometric techniques applied to issues management: A case study. Journal of the American Society for Information Science, 36(8), 389–397.CrossRefGoogle Scholar
  6. Lelu, A. (1993). Modèles neuronaux pour l’analyse de données documentaires et textuelles. PhD Dissertation, Université de Paris 6.Google Scholar
  7. Lelu, A., & François, C. (1992). Hypertext paradigm in the field of information retrieval: A neural approach. 4th ACM conference on hypertext, Milano, November 30th–December 4th.Google Scholar
  8. Mogoutov, A., Cambrosio, A., Keating, P., & Mustar, P. (2008). Biomedical innovation at the laboratory, clinical and commercial interface: A new method for mapping research projects, publications and patents in the field of microarrays. Journal of Informetrics, 2, 341–353.CrossRefGoogle Scholar
  9. Mogoutov, A., & Kahane, B. (2007). Data search strategy for science and technology emergence: A scalable and evolutionary query for nanotechnology tracking. Research Policy, 36, 893–903.CrossRefGoogle Scholar
  10. Noyons, E. (2004). Science maps within a science policy context. In H. F. Moed, W. Glänzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research (pp. 237–255). London: Kluwer Academic Publishers.Google Scholar
  11. Polanco, X., François, C., Royauté, J., Besagni, D., & Roche, I. (2001). Stanalyst®: An integrated environment for clustering and mapping analysis on science and technology. In: Proceedings of the 8th ISSI, Sydney, July 16th–20th.Google Scholar
  12. Robertson, S. (2004). Understanding inverse document frequency: On theoretical arguments for IDF. Journal of Documentation, 60(5), 503–520.CrossRefGoogle Scholar
  13. Roche, I., Besagni, D., François, C., Hörlesberger, M., & Schiebel, E. (2008). Identification and characterisation of technological topics in the field of Molecular Biology. To be published.Google Scholar
  14. Salerno, M., Landoni, P, & Verganti, R. (2006). The role of funded projects content analysis in early stage disciplines exploration: The case of nanotechnology. Paper presented at the SPRU 40th anniversary conference—The future of science, technology and innovation policy.Google Scholar
  15. Schiebel, E., & Hörlesberger, M. (2007). About the identification of technology specific keywords in emerging technologies: The case of “Magnetoelectronics”. In: D. Torres-Salinas & H. F. Moed (Eds.), Proceedings of ISSI 2007, 11th international conference of the International Society for Scientometrics and Informetrics (pp. 691–69). Madrid, June 25th–27th.Google Scholar
  16. Spärck, J. K., & Robertson, S. (2006). Inverse Document Frequency—The IDF page. Retrieved November 22, 2006 from
  17. van Rijsbergen, C. J. (1979). Information retrieval. London: Butterworths.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2010

Authors and Affiliations

  • Edgar Schiebel
    • 1
  • Marianne Hörlesberger
    • 1
  • Ivana Roche
    • 2
  • Claire François
    • 2
  • Dominique Besagni
    • 2
  1. 1.AIT Austrian Institute of Technology GmbH, Tech Gate ViennaWienAustria
  2. 2.INIST-CNRSVandoeuvre-lès-NancyFrance

Personalised recommendations