Periodic Reporting for period 2 - InnoTechRate (Innovation Technology Rating - system and digital hub)
Reporting period: 2023-07-01 to 2024-03-31
When discussing an innovation project, innovators and investors do not necessarily speak the same language, yet they both have the same goal: the commercial success of the project. Indeed, although an innovator typically prioritises scientific and technological characteristics as the foundation of its project, an investor focuses on systemic risks and commercial possibilities. This information asymmetry is even exacerbated when it comes to an innovation that holds both high commercial potential and a technological risk.
To this end, non-financial rating systems for innovative companies and comparative sectoral analysis methods have been developed and some work has also been carried out on disruptive innovation projects to measure their risks and potential based on a data-driven approach.
A comprehensive analysis of an innovation results in a more sophisticated and relevant assessment of its risks and potential. This can be achieved by incorporating technology and market data, and going beyond the analysis of accounting and financial data typically carried out at the company level.
The objective of the Innotechrate project was consequently to further reduce cognitive biases in the evaluation of innovation, and thus achieve a higher level of confidence in the analyses and processing of the underlying data to encourage public and private investment in breakthrough innovations.
To this end, non-financial rating systems for innovative companies and comparative sectoral analysis methods have been developed and some work has also been carried out on disruptive innovation projects to measure their risks and potential based on a data-driven approach.
A comprehensive analysis of an innovation results in a more sophisticated and relevant assessment of its risks and potential. This can be achieved by incorporating technology and market data, and going beyond the analysis of accounting and financial data typically carried out at the company level.
The objective of the Innotechrate project was consequently to further reduce cognitive biases in the evaluation of innovation, and thus achieve a higher level of confidence in the analyses and processing of the underlying data to encourage public and private investment in breakthrough innovations.
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.
(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.