Development of Evaluation Model for Characteristics of Researchers Contributing to the Growth of University-based Startups

Abstract

The growth of university-based deep-tech startups critically depends on the active involvement of researchers with outstanding research achievements that form the core of the business. This study analyzes the relationship between researchers' background information (such as academic publications and research funding) and the fundraising amounts of university spin-offs using data from Japan's "University Venture Database" managed by the Ministry of Economy, Trade, and Industry. By employing machine learning techniques, the study aimed to develop a model that evaluates the characteristics of researchers contributing to the growth of these startups. By analyzing the model, it was confirmed that researchers who have achieved certain thresholds in paper evaluations or research funding contribute to the growth of university-originated startups.