Assessment of commercialization potential of innovative projects for improving university technology transfer

Purpose: to form a model for assessing the commercialization potential of innovative university projects as a tool for improving technology transfer.

Methods: based on traditional methods of analysis and synthesis, methods of correlation and regression analysis were applied to identify the success factors of projects. Machine learning methods and the random forest method were used to form a model for evaluating the potential of projects.

Results: the approaches to assessing the potential of project commercialization are analyzed and a proprietary approach is proposed taking into account the specifics of university innovation projects at an early stage of development that are in search of financing. An integrated assessment of the commercialization potential based on 22 parameters from 5 integrated assessment blocks (commercial readiness, technical readiness, expertise, compliance with regulatory standards, resources) was tested on 16 innovative projects of ITMO University. Correlation analysis revealed the factors influencing the success of an innovative project in obtaining financing or sales: the expertise of the team (0.72) and the level of commercial readiness of the project (0.59). A direct weak relationship was revealed by the technical readiness factor of the project (0.37) which indicates the importance of the team's qualifications and the presence of a market demand (explicit or implicit) and reflects the specifics of financing early-stage projects, which often do not have a ready-made prototype. Using machine learning and the random forest method, a predictive regression model for assessing the potential of commercialization was built and tested which confirmed its applicability in investment readiness assessment.

Conclusions and Relevance: the research results can be used to create an automated tool for making investment decisions and managing innovative projects of technology transfer centers and university accelerators in order to increase the level of project readiness and the effectiveness of university technology transfer.

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