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Innovation signals: leveraging machine learning to separate noise from news

Author

Listed:
  • Christian Mühlroth

    (Friedrich-Alexander-Universität Erlangen-Nürnberg)

  • Laura Kölbl

    (Friedrich-Alexander-Universität Erlangen-Nürnberg)

  • Michael Grottke

    (Friedrich-Alexander-Universität Erlangen-Nürnberg
    GfK SE)

Abstract

The early detection of and an adequate response to meaningful signals of change have a defining impact on the competitive vitality and the competitive advantage of companies. For this strategically important task, companies apply corporate foresight, aiming to enable superior company performance. With the growing dynamics of global markets, the amount of data to be analyzed for this purpose is constantly increasing. As a result, these analyses are often performed with an unreasonably high investment of financial and human resources, or are even not performed at all. To address this challenge, this paper presents a machine-learning-based approach to help companies identify early signals of change with a higher level of automation than before. For this, we combine a newly-proposed quantitative approach with the existing qualitative approaches by Cooper (stage-gate model) and by Rohrbeck (corporate foresight process). After a search field of interest has been defined, the related data is collected from web news sites, early signals are identified and selected automatically, and domain experts then assess these signals with respect to their relevance and novelty. Once it has been set up, the approach can be executed iteratively at regular time intervals in order to continuously scan for new signals of change. By means of three case studies supported by domain experts we demonstrate the effectiveness of our approach. After presenting our findings and discussing possible limitations of the approach, we suggest future research opportunities to further advance this field.

Suggested Citation

  • Christian Mühlroth & Laura Kölbl & Michael Grottke, 2023. "Innovation signals: leveraging machine learning to separate noise from news," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2649-2676, May.
  • Handle: RePEc:spr:scient:v:128:y:2023:i:5:d:10.1007_s11192-023-04672-y
    DOI: 10.1007/s11192-023-04672-y
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    References listed on IDEAS

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    More about this item

    Keywords

    Weak signals; Strong signals; Corporate foresight; Innovation management; Machine learning; Artificial intelligence; Trend scouting; Technology scouting; Startup scouting;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • M1 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration
    • M19 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Other
    • 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

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