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Measuring innovation and innovativeness: a data-mining approach

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  • Bernard Sinclair-Desgagné

    (Université Côte d’Azur (GREDEG))

Abstract

Despite substantial advances over the past decades, measuring innovation and innovativeness remains a challenge for both academic researchers and management practitioners. To address several key concerns with current indicators—such as their specialization and consequent one-sidedness, their frequent lack of theoretical foundations, and the fact that they may not really foster creativity and invention—this paper introduces some new metrics via one data-mining approach—formal concept analysis—which is increasingly used to represent and treat knowledge. This approach can adapt to particular needs and goals, incorporate various kinds of information (qualitative or quantitative) from different sources, and cope with several types of innovations. It also uncovers a logical route to novelty, which might enhance the generation of ideas and is used here to support the measurement of innovativeness.

Suggested Citation

  • Bernard Sinclair-Desgagné, 2022. "Measuring innovation and innovativeness: a data-mining approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 2415-2434, August.
    • Handle: RePEc:spr:qualqt:v:56:y:2022:i:4:d:10.1007_s11135-021-01231-6
    DOI: 10.1007/s11135-021-01231-6
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    References listed on IDEAS

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