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The theory of technological parasitism for the measurement of the evolution of technology and technological forecasting

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  • Coccia, Mario

Abstract

How to measure the evolution of technology in order to predict innovations that grow rapidly? This study suggests a new perspective based on the theory of technological parasitism, which can measure and assess the dynamics of technological evolution for technological forecasting. In particular, the evolution of technology is modelled here in terms of interaction between a host technology (system) and a parasitic technology (subsystem). The coefficient of evolutionary growth of the model here indicates the evolution of parasitic technology in relation to host technology, suggesting the evolutionary pathway of overall system of technology over time (i.e., underdevelopment, growth and development). This approach is illustrated with realistic examples using empirical data of of product and process technologies: farm tractor, freight locomotive, electricity generation and smartphone technology. Overall, then, the proposed model, based on the theory of technological parasitism, can be useful for bringing a new perspective to explain and generalize properties of the evolution of technology and predict which innovations are likely to evolve rapidly in society.

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  • Coccia, Mario, 2019. "The theory of technological parasitism for the measurement of the evolution of technology and technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 289-304.
  • Handle: RePEc:eee:tefoso:v:141:y:2019:i:c:p:289-304
    DOI: 10.1016/j.techfore.2018.12.012
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    Cited by:

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    6. Coccia, Mario, 2019. "Why do nations produce science advances and new technology?," Technology in Society, Elsevier, vol. 59(C).
    7. Coccia, Mario, 2020. "Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence," Technology in Society, Elsevier, vol. 60(C).
    8. Mario Coccia, 2021. "Evolution and structure of research fields driven by crises and environmental threats: the COVID-19 research," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9405-9429, December.
    9. Koopo Kwon & Sungchan Jun & Yong-Jae Lee & Sanghei Choi & Chulung Lee, 2022. "Logistics Technology Forecasting Framework Using Patent Analysis for Technology Roadmap," Sustainability, MDPI, vol. 14(9), pages 1-30, April.

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    More about this item

    Keywords

    Measurement of technology; Technometrics; Technological evolution; Technological change; Coevolution; Nature of technology; Host technology; Parasitic technology; Technological parasitism; Technological innovation; Technological forecasting; Technology assessment; Technological progress;
    All these keywords.

    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

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