Abstract Book of the VI International Conference on Quality, Innovation and Sustainability - ICQIS2025

This paper presents a framework for diagnosing Small and Medium-sized Enterprises (SMEs) within the innovation ecosystem fostered by the Grant Office Service of the University of Foggia (GOS-Unifg). Building upon best practices and experience gained by GOS-Unifg and similar frameworks used within the Italian university system, this proposed framework utilizes three questionnaires: assessment of industrial property, propensity for investment and Artificial Intelligence adoption, and managerial performance related to SME innovation capability. The framework identifies key indicators and managerial aspects of innovation, culminating in a composite coefficient of SME innovation propensity. This diagnostic tool can be used to evaluate an SME's potential for participation in the GOS-Unifg innovation ecosystem. The resulting diagnosis informs the development of tailored managerial tools, including recommendations, company policies, managerial objectives and strategies, and training and capacity-building initiatives. This approach represents the foundation for a qualitative-quantitative Observatory leveraging advanced data analysis tools (e.g., artificial intelligence, machine learning) to develop predictive analyses. These analyses aim to optimize the effectiveness and efficiency of the implemented managerial tools. Future research will expand the framework to include an initial company scouting phase and a final phase focused on fostering long-term engagement within the innovation ecosystem. Further research will also involve testing the framework and determining appropriate weightings for the identified managerial aspects and related tools.