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Phenomena like the digital transformation lead to constantly changing economic requirements for companies and their underlying business models. The concept of an innovation capability maturity model is a possible approach to adapt to these changes by presenting solutions for the improvement of innovation potential, which in turn leads to higher turnover. Since existing one-size-fits-all conceptual development approaches of maturity models cannot depict the characteristics for a specific class (size and sector) of companies, we apply a data-driven development approach for the class of small industrial companies. Divided into a knowledge and organizational dimension, our findings reveal a total of 21 factor combinations, representing different levels of innovation capability maturity. By applying a set-theoretic approach for the identification of factor configurations leading to low, medium, and high innovation capability maturity, we furthermore propose the conceptual approach for an empirically driven set-theoretic maturity model development, yielding a design science method artifact.



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