Assessing the intellectual maturity of industrial ecosystems: Configurational approach

Introduction. Proliferation of digital technologies makes industrial ecosystems increasingly important in the development of both industries in general and individual enterprises. Industrial ecosystems with their potential benefits, smart manufacturing, and opportunities for integrating collaborative innovation and digital servitization can significantly enhance production flexibility and efficiency, reduce time-to-market for new competitive products or services, and substantially contribute to achieving sustainable development goals. Purpose. The article aims at developing a methodological approach to construct a maturity assessment model for industrial ecosystems. This model is intended to serve as a strategic management tool for their development in the data economy, based on the integration and systematization of complementary research areas within the configurational approach. Materials and Methods. The study highlights the role of a maturity model as a tool for identifying strategic development priorities in the digital environment. It is shown that currently, there are no models available that enable a comprehensive assessment of the maturity of industrial ecosystems, taking into account the full range of aspects significant for their development, as well as the challenges and opportunities presented by the data economy. Results. The study proves the valid reference to a configurational approach to identify the most significant assessment aspects for a specific industrial ecosystem. The concept of an industrial ecosystem's intellectual maturity is defined, and a methodological approach for its assessment is developed. The characteristics and interrelationships of various ecosystem types have been examined to construct a structural-logical assessment model for smart maturity. Key principles for assessment are outlined, and the basic structure of the assessment model is defined. An IEST framework is proposed, which involves the selection of assessment blocks depending on the ecosystem's system of target priorities. Conclusions. The proposed approach will make the assessment more flexible by selecting and combining a subset of blocks from the general redundant model, taking into account the current level and development prospects of a specific industrial ecosystem.