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

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Bennat, T.: 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. In: Journal of the Knowledge Economy 13 (2022), S. 1666-1691. DOI: https://doi.org/10.1007/s13132-021-00774-1

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/12369

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Sum total of downloads: 65




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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. © 2021, The Author(s).
License of this version: CC BY 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2022
Appears in Collections:Naturwissenschaftliche Fakultät

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downloads by country:

pos. country downloads
total perc.
1 image of flag of United States United States 21 32.31%
2 image of flag of Germany Germany 14 21.54%
3 image of flag of Czech Republic Czech Republic 6 9.23%
4 image of flag of Russian Federation Russian Federation 5 7.69%
5 image of flag of China China 5 7.69%
6 image of flag of No geo information available No geo information available 3 4.62%
7 image of flag of Peru Peru 2 3.08%
8 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 2 3.08%
9 image of flag of Netherlands Netherlands 1 1.54%
10 image of flag of France France 1 1.54%
    other countries 5 7.69%

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