The Innotechrate project has implemented two main scientific activities: (1) stakeholder analysis and (2) design of a conceptual framework for an AI-based and data-driven digital platform.
(1) The stakeholder analysis aimed to gain a deeper understanding of the needs and expectations of potential users of the technology rating tool. By understanding the investor selection process, the Innotechrate project identified criteria and parameters involved in decision-making to support innovation projects. This analysis helps address information asymmetry and complements the conceptual framework of the digital platform.
The analysis reveals that VC firms prioritize the management team's ability over the importance of technology when making investment decisions. This finding is supported by two recent academic studies, which highlight the belief of a substantial portion of European VC firms that a competent management team is the most critical factor for investment success. The challenges faced by private investors with limited funds and higher interest rates, along with decreasing public funding, emphasize the need for an informed approach to guide both public and private investors in selecting technology-based innovation projects. This situation, coupled with the rapid proliferation of artificial intelligence tools across various economic sectors, including in the VC area, presents an opportune moment for technology rating supported by a data-driven approach and AI.
(2) The design of the conceptual framework is the foundation of AI based technology rating and the digital platform for technology rating. The framework's main objective is to establish a solid foundation for processing diverse data from both internal and external sources of a project. This is a unique approach, since other valuation tools are based not on the project but on accounting and financial data, which is not very effective for disruptive start-ups. This function is facilitated by implementing an original innovation matrix presented in the conceptual framework. The matrix components are fueled by information and data related to the innovation project itself, primarily provided by the innovator and enriched by extracted external data sources.
