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<p>University-industry collaborations (UICs) are critical for fostering innovation and economic growth within emerging markets. Despite extensive research, prior studies have predominantly relied on linear, single-factor analyses, overlooking the configurational interdependencies that shape UIC outcomes. To address this gap, we adopt a configurational perspective grounded in fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine how combinations of firm characteristics (size, absorptive capacity, state ownership), university attributes (prestige, technology transfer office (TTO) experience), and multidimensional proximities (geographical, institutional, social) collectively influence UIC performance. Analyzing joint patent data from Chinese manufacturing firms and universities, we identify four high-performance configurations: (1) state-orchestrated resource integration, (2) policy-driven elite collaboration, (3) TTO-mediated technology collaboration, and (4) trust-embedded relational persistence. These configurations demonstrate equifinality, revealing that absorptive capacity as a foundational capability across all high-performance pathways, while state ownership, university prestige, and proximities play context-dependent roles. Conversely, five non-high-performance configurations highlight the detrimental effects of institutional isolation and resource-constrained misalignment, where the absence of state ownership and institutional proximity exacerbates collaboration challenges. Our findings challenge linear, single-factor explanations dominant in prior literature by emphasizing conjunctural causation and systemic interdependencies in UICs. They offer actionable insights for firms, universities, and policymakers to strategically align organizational resources, institutional frameworks, and relational mechanisms for enhanced collaborative outcomes. This study advances UIC literature by shifting the focus from “what matters” to “what combinations matter under what conditions,” providing a nuanced understanding of innovation ecosystems.</p>

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A configurational exploration of how organizational characteristics and multidimensional proximities influence university-industry collaborations

  • 期刊论文

  • 发表日期：

    2025年9月6日

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  • Wei Yang,
  • Wantao Chen

摘要

University-industry collaborations (UICs) are critical for fostering innovation and economic growth within emerging markets. Despite extensive research, prior studies have predominantly relied on linear, single-factor analyses, overlooking the configurational interdependencies that shape UIC outcomes. To address this gap, we adopt a configurational perspective grounded in fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine how combinations of firm characteristics (size, absorptive capacity, state ownership), university attributes (prestige, technology transfer office (TTO) experience), and multidimensional proximities (geographical, institutional, social) collectively influence UIC performance. Analyzing joint patent data from Chinese manufacturing firms and universities, we identify four high-performance configurations: (1) state-orchestrated resource integration, (2) policy-driven elite collaboration, (3) TTO-mediated technology collaboration, and (4) trust-embedded relational persistence. These configurations demonstrate equifinality, revealing that absorptive capacity as a foundational capability across all high-performance pathways, while state ownership, university prestige, and proximities play context-dependent roles. Conversely, five non-high-performance configurations highlight the detrimental effects of institutional isolation and resource-constrained misalignment, where the absence of state ownership and institutional proximity exacerbates collaboration challenges. Our findings challenge linear, single-factor explanations dominant in prior literature by emphasizing conjunctural causation and systemic interdependencies in UICs. They offer actionable insights for firms, universities, and policymakers to strategically align organizational resources, institutional frameworks, and relational mechanisms for enhanced collaborative outcomes. This study advances UIC literature by shifting the focus from “what matters” to “what combinations matter under what conditions,” providing a nuanced understanding of innovation ecosystems.