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Paper Type
Complete
Description
Constantly evolving economic demands caused by phenomena such as digital transformation necessitate businesses and their underlying models to adapt. One possible solution for enhancing innovation potential and increasing revenue is implementing an innovation capability maturity model. This research replicates an existing maturity model development approach, which accounts for the unique characteristics of a class of companies based on their size and sector. Thus, by using a data-driven method, we provide an innovation capability maturity model for small industrial companies. We identified 21 combinations of factors that can affect innovation capability maturity, grouped into a knowledge and organizational dimension. Using a set-theoretic approach, we identified configurations of these factors that lead to low, medium, and high levels of innovation capability maturity. Based on these findings, we support an empirically driven, set-theoretic approach for developing a maturity model that can be used for higher applicability and representativity in individual classes of companies.
Paper Number
1790
Recommended Citation
Gräubig, Dix Maik and Bley, Katja, "A Configurational Set-Theoretic Approach to an Innovation Capability Maturity Model" (2023). AMCIS 2023 Proceedings. 13.
https://aisel.aisnet.org/amcis2023/sig_scuidt/sig_scuidt/13
A Configurational Set-Theoretic Approach to an Innovation Capability Maturity Model
Constantly evolving economic demands caused by phenomena such as digital transformation necessitate businesses and their underlying models to adapt. One possible solution for enhancing innovation potential and increasing revenue is implementing an innovation capability maturity model. This research replicates an existing maturity model development approach, which accounts for the unique characteristics of a class of companies based on their size and sector. Thus, by using a data-driven method, we provide an innovation capability maturity model for small industrial companies. We identified 21 combinations of factors that can affect innovation capability maturity, grouped into a knowledge and organizational dimension. Using a set-theoretic approach, we identified configurations of these factors that lead to low, medium, and high levels of innovation capability maturity. Based on these findings, we support an empirically driven, set-theoretic approach for developing a maturity model that can be used for higher applicability and representativity in individual classes of companies.
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