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Comparing public and scientific discourse in the context of innovation systems

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  • Kayser, Victoria

Abstract

Innovation as a systemic process is not only driven by science and technology but has diverse sources. While there are (numeric) indicators to map S&T developments such as patents, publications or standards, new indicators are required to map other areas of the innovation system. In this regard, one option is the examination of news reporting. News is a recognized channel for innovation diffusion and plays an important role in informing society. To contrast changes and developments in science and society, specifically the link between both is addressed in this article by comparing the content of news articles and scientific publications. Thus, the aim of this article is to first argue the benefit of integrating the media in the innovation system debate because of its recognized role in innovation diffusion and to develop a methodology to automatically compare scientific and media discourses. To process the volume of textual data according to a common analytical scheme, a text mining framework has been developed. The results offer valuable input for examining the present state of themes and technologies and, thereby, support future planning activities.

Suggested Citation

  • Kayser, Victoria, 2017. "Comparing public and scientific discourse in the context of innovation systems," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 348-357.
    • Handle: RePEc:eee:tefoso:v:115:y:2017:i:c:p:348-357
    DOI: 10.1016/j.techfore.2016.08.005
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    Cited by:

    1. Mejía, Cristian & Kajikawa, Yuya, 2019. "Technology news and their linkage to production of knowledge in robotics research," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 114-124.
    2. Ozcan Saritas & Pavel Bakhtin & Ilya Kuzminov & Elena Khabirova, 2021. "Big data augmentated business trend identification: the case of mobile commerce," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1553-1579, February.
    3. Andersen, Per Dannemand & Johnston, Ron & Saritas, Ozcan, 2017. "FTA and Innovation Systems," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 236-239.
    4. Dhar, Suparna & Tarafdar, Pratik & Bose, Indranil, 2022. "Understanding the evolution of an emerging technological paradigm and its impact: The case of Digital Twin," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    5. Reischauer, Georg, 2018. "Industry 4.0 as policy-driven discourse to institutionalize innovation systems in manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 26-33.
    6. Weiss, Daniel & Nemeczek, Fabian, 2021. "A text-based monitoring tool for the legitimacy and guidance of technological innovation systems," Technology in Society, Elsevier, vol. 66(C).
    7. Kayser, Victoria & Blind, Knut, 2017. "Extending the knowledge base of foresight: The contribution of text mining," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 208-215.

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