Purpose Facilitating university patent transfer has become a critical priority amid intensifying global innovation competition. While intellectual capital is recognized as pivotal for innovation, its configurational role in university patent transfers remains underexplored. Grounded in intellectual capital theory, this study investigates how multidimensional conditions—human, structural, and relational capital in China's “Double First-Class” universities combine to form effective patent transfer pathways. Design/methodology/approach The study samples 42 Chinese “Double First-Class” universities and constructs a three-dimensional, multi-element configurational analysis framework based on intellectual capital. We adopt a fuzzy-set Qualitative Comparative Analysis methodology, and data were sourced from the national intellectual property administration, university websites, statistical yearbooks, and academic databases. Findings No single necessary condition drives university patent transfer. Instead, six effective configurational pathways are identified, which can be categorized into three models: (1) the “Knowledge-Collaboration” dual-core driven type, reliant on profound depth of knowledge and close university-enterprise collaboration; (2) the “Balanced Support” type, featuring synergistic interaction of multidimensional capitals; (3) the “External-Relations Pull” type, with government support and university-enterprise collaboration at its core, which can compensate for deficiencies in internal conditions. The results reveal complementarity and substitution relationships among human, structural, and relational capital, and highlight the pivotal role of university-enterprise collaboration in most pathways. Originality/value This study extends intellectual capital theory to the field of university patent transfer by constructing an integrated analytical framework incorporating the three types of capital. From a configurational perspective, this reveals multiple concurrent transfer pathways that transcend the limitations of traditional linear analysis. Practically, it enables universities to select context-appropriate pathways, advises managers on how to optimize resource orchestration, and urges policymakers to implement classified support mechanisms.
Wang et al. (Wed,) studied this question.