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A simple interpretation of the growth of scientific/technological research impact leading to hype-type evolution curves

Author

Listed:
  • Marco Campani

    (CNR Istituti SPIN, IOM e NANO)

  • Ruggero Vaglio

    (Università di Napoli Federico II, Complesso Universitario di Monte S. Angelo)

Abstract

The empirical and theoretical justification of Gartner “hype curves” is a very relevant open question in the field of Technological Life Cycle analysis. The scope of the present paper is to introduce a simple model describing the growth of scientific/technological research impact, in the specific case where science is the main source of a new idea driving a technological development, leading to “hype-type” evolution curves. The main idea of the model is that, in a first stage, the growth of the scientific interest of a new specific field (as can be measured by publication numbers) basically follows the classical “logistic” growth curve. At a second stage, starting at a later trigger time, the technological development based on that scientific idea (as can be measured by patent deposits) can be described as the integral (in a mathematical sense) of the first curve, since technology is based on the overall accumulated scientific knowledge. The model is preliminary tested through a bibliometric analysis of the publication and patent deposit rate for organic light emitting diodes scientific research and technology.

Suggested Citation

  • Marco Campani & Ruggero Vaglio, 2015. "A simple interpretation of the growth of scientific/technological research impact leading to hype-type evolution curves," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 75-83, April.
    • Handle: RePEc:spr:scient:v:103:y:2015:i:1:d:10.1007_s11192-015-1533-6
    DOI: 10.1007/s11192-015-1533-6
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    References listed on IDEAS

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    2. repec:fth:harver:1473 is not listed on IDEAS
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    4. Luís M. A. Bettencourt & David I. Kaiser & Jasleen Kaur & Carlos Castillo-Chávez & David E. Wojick, 2008. "Population modeling of the emergence and development of scientific fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(3), pages 495-518, June.
    5. Zvi Griliches, 1998. "Patent Statistics as Economic Indicators: A Survey," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 287-343, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Chang, Shu-Hao & Fan, Chin-Yuan, 2016. "Identification of the technology life cycle of telematics: A patent-based analytical perspective," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 1-10.
    2. René Lezama-Nicolás & Marisela Rodríguez-Salvador & Rosa Río-Belver & Iñaki Bildosola, 2018. "A bibliometric method for assessing technological maturity: the case of additive manufacturing," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1425-1452, December.
    3. Stephen Fox, 2018. "Irresponsible Research and Innovation? Applying Findings from Neuroscience to Analysis of Unsustainable Hype Cycles," Sustainability, MDPI, vol. 10(10), pages 1-16, September.
    4. Jesús Cebrino & Silvia Portero de la Cruz, 2020. "A worldwide bibliometric analysis of published literature on workplace violence in healthcare personnel," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-16, November.

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