Open Access

The Learning Ecosystem approach is gaining popularity as it helps organizations adapt flexibly to changing environmental conditions and the demands of the VUCA (volatile, uncertain, complex, and ambiguous) world. The strength of its organizational support thereby increases with the maturity of the Learning Ecosystem. However, no comprehensive model that adequately represents this maturity currently exists. The aim of this thesis is to fill this gap by developing a comprehensive Learning Ecosystem Maturity Model, which is based on the Learning Ecosystem approach by Schmitz and Foelsing, and leverages various existing maturity models. Ten different design dimensions are examined and for each, five levels of maturity operationalized. The Learning Ecosystem Maturity Model enables organizations not only to identify the current development status of their Learning Ecosystem but also to derive potential areas for improvement and measure the progress. During the development of the model, specific design criteria such as clear problem definition, practitioner relevance and consideration of existing models were taken into account to ensure the model's quality. The maturity model is initially assessed through a web-based survey with an exploratory nature. This assessment and its descriptive analysis provide first insights into the current state of development of organizations, allowing for some preliminary implications. It was observed that there is significant potential for improvement, particularly in the dimensions of structures, leadership, data architecture, and analytics. Due to the identified correlation of maturity across all dimensions, they have a constraining impact on the growth of the entire Learning Ecosystem. Overall, only 15% of the participants organizations work within a mature Learning Ecosystems, which is reached with level 4 of the maturity model. Certain limitations of the model and the study were identified, which should be considered in future research. Suggestions for future research include refining the model further, incorporating additional perspectives, weightings, and actionable recommendations.