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

doi:10.1007/s13132-021-00774-1

Abstract

AbstractThis 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.

Highlights

Innovation is a primary source of competitive advantages and an important research topic in economic geography
Invoking insights from the innovation mode concept and economic geography literature on regional innovation, we propose that high innovation performance does not depend on specific conditions, but rather on a specific configuration of conditions—that is, 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
To define the causal “recipes” for organizational learning that lead to high innovation performance in specific regions, we applied a qualitative comparative analysis (QCA) of 47 small and medium-sized enterprises (SMEs) in three different German regions

Summary

Introduction

Innovation is a primary source of competitive advantages and an important research topic in economic geography. According to contemporary innovation concepts, the innovation process is based on many feedback loops among users, researchers, and innovators (Kline & Rosenberg, 1986). This interpretation stipulates that innovation need not be the result of scientific research-and-development (R&D) per se; rather, it occurs through co-creation with users, suppliers, or via firm-internal learning. We argue that it is worth breaking the DUI mode into its core mechanisms to learn what constitutes the causal “recipes” for organizational learning that lead to high innovation performance in a specific region

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