Resource Orchestration for Sustainable Innovation Capacity Development Among University Students: A Configurational Analysis

The rapid development of artificial intelligence has intensified the need for higher education institutions to cultivate students’ capacity to engage with complex and changing innovation problems. Existing research has largely examined the net effects of individual educational resources, leaving less understood how heterogeneous institutional and student resources combine across universities. Drawing on Resource Orchestration Theory, this study examines configurations associated with students’ perceived and self-reported innovation capacity. Survey responses from 14,034 students were aggregated to 126 Chinese universities after tests of aggregation reliability, and the university-level data were analyzed using fuzzy-set qualitative comparative analysis (fsQCA). No single antecedent met the conventional necessity threshold. The analysis identified five configurations associated with high self-reported innovation capacity and three configurations associated with low self-reported innovation capacity, which were summarized into three overarching high-outcome patterns. Additional checks varying consistency, frequency, and calibration choices indicated substantial, although not complete, configurational stability. The study extends resource-orchestration reasoning to higher education while emphasizing that the findings represent cross-sectional set-theoretic associations rather than longitudinal or experimental causal effects.