Intellectual Capital Reconfiguration through University-Industry AI Collaboration

This study examines how university-industry artificial intelligence (AI) collaboration reconfigures intellectual capital (IC) in entrepreneurial universities. It focuses on the mediating roles of human, structural, and relational capital between AI adoption and IC-driven innovation performance, exploring configurational pathways across university types. Using a critical realism paradigm, a three-phase mixed-methods design was employed: (1) 48 semi-structured interviews at six Taiwanese universities; (2) quantitative analysis with partial least squares structural equation modelling (PLS-SEM, n = 386) and fuzzy-set qualitative comparative analysis (fsQCA); and (3) network analysis via exponential random graph models (ERGM). Counterfactual simulations (500 iterations) assessed governance configurations. AI collaboration intensity significantly enhances human, structural, and relational capital. All three IC dimensions partially mediate the AI-innovation link, with structural capital having the strongest effect. Research-intensive universities benefit more in structural capital formation. fsQCA identified three paths to high innovation performance, while ERGM showed AI-mediated knowledge ties predict IC exchange density. Simulations highlight governance as more critical than adoption intensity alone. This study extends IC theory into the AI university-industry collaboration context, reveals complementarity among IC dimensions, and challenges technology determinism by emphasizing distributed governance’s role in amplifying AI’s IC benefits. Findings offer practical guidance for university entrepreneurial transformation.