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Technology maturity assessment based on blog analysis

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  • Albert, Till
  • Moehrle, Martin G.
  • Meyer, Stefan

Abstract

R&D managers make strategic decisions regarding technologies that are crucial for the long term success of their companies. Standardized technology intelligence reports on the basis of informetric approaches are an outstanding means of providing them with a sound information basis; one important yet scientifically less considered aspect of informetrics is technology maturity. The portrayal of a technology in text media changes with its maturity; this change can be measured. Our paper focuses on blog analysis as one part of informetrics. It represents a proof of concept for an approach based on changes induced by technology maturity in blog texts. We operationalize the technology life cycle model by Sommerlatte and Deschamps (1986) and measure the utilization of terms specifically used in the context of pacing technologies and basic technologies, both concepts stemming from the selected technology life cycle model. We test this approach on six technologies and make use of fuzzy logic to assess a technology's maturity. We triangulate the results from blog based analysis with results from an expert survey. Our method provides a quick orientation regarding technologies; it helps compare their maturity, monitor development speed as well as maturity state changes, and even identify re-emerging basic technologies. So far, R&D managers have rated the resulting information as a valuable and intuitive supplement to information sources available.

Suggested Citation

  • Albert, Till & Moehrle, Martin G. & Meyer, Stefan, 2015. "Technology maturity assessment based on blog analysis," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 196-209.
    • Handle: RePEc:eee:tefoso:v:92:y:2015:i:c:p:196-209
    DOI: 10.1016/j.techfore.2014.08.011
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    References listed on IDEAS

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    Cited by:

    1. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    2. Momeni, Abdolreza & Rost, Katja, 2016. "Identification and monitoring of possible disruptive technologies by patent-development paths and topic modeling," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 16-29.
    3. Rammer, Christian & Es-Sadki, Nordine, 2023. "Using big data for generating firm-level innovation indicators - a literature review," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    4. 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.
    5. Kristóf Gyódi & Łukasz Nawaro & Michał Paliński & Maciej Wilamowski, 2023. "Informing policy with text mining: technological change and social challenges," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 933-954, February.

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