High Innovativeness of SMEs and the Configuration of Learning-by-Doing, Learning-by-Using, Learning-by-Interacting, and Learning-by-Science: a Regional Comparison Applying Fuzzy Qualitative Comparative Analysis

Tatjana Bennat ()
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Tatjana Bennat: Leibniz University Hannover

Journal of the Knowledge Economy, 2022, vol. 13, issue 2, No 35, 1666-1691

Abstract: Abstract This paper proposes a holistic approach for investigating high innovation performance in SMEs by comparing different German regions. Invoking insights from the innovation mode concept and existing literature on regional innovation, we apply a qualitative comparative analysis (QCA) of 47 interviews with SMEs to show that high innovativeness is based on a bundle of conditions summarized as mechanisms of learning-by-doing, learning-by-using, learning-by-interacting, and learning-by-science. The results indicate that only parts of the DUI mode, in combination with the STI mode, can explain high innovativeness. This has implications for managers as well as for innovation policy, highlighting that there is no universal “best way” to become highly innovative.

Keywords: Combinatorial knowledge; DUI; Innovation mode; STI (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13132-021-00774-1

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