Identifying researcher characteristics driving growth in Japanese University-originated deep-tech startups: A machine learning approach

Abstract

Active involvement of researchers with strong outputs is essential for growth of deep-tech university-originated startups. We used the University-Originated Venture Database to link total funding amount with researchers’ publication metrics and KAKENHI grant records. Classification models were constructed to distinguish growing and non-growing startups, followed by feature-importance and breakdown-tree analyses to interpret which researcher attributes drive growth. We then applied these defined characteristics to evaluate researcher profiles at top Japanese universities to assess their entrepreneurial engagement. This process revealed a disconnect between high-quality research achievements and actual startup activity. These insights underpin an identification framework that can guide investment and policy decisions in Japan’s deep-tech ecosystem.