AI partnerships with industry reshape universities’ knowledge assets and boost innovation — structural, not just human, capital does the heavy lifting; governance arrangements matter more than raw adoption levels.

Intellectual Capital Reconfiguration through University-Industry AI Collaboration

Shiyu Huang, Yi Huang, Ming-Chia Chen · August 11, 2026 · International Journal of Advanced Multidisciplinary Research and Studies

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University-industry AI collaboration is associated with enhanced human, structural, and relational intellectual capital, with structural capital the strongest mediator of AI-driven innovation performance, and governance configuration amplifying these benefits more than adoption intensity alone.

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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.

Summary

Main Finding

University-industry AI collaboration substantially reconfigures universities’ intellectual capital (human, structural, relational). Collaborative AI adoption intensity increases all three IC dimensions, which each partially mediate the link from AI collaboration to IC-driven innovation performance. Structural capital is the strongest mediator. Configurational complementarity among IC dimensions (no single dimension is sufficient) and governance design (distributed vs. centralized) critically shape whether AI adoption produces sustained innovation gains; governance configuration matters more than adoption intensity alone. Research-intensive universities derive larger structural-capital gains from AI collaboration than teaching-oriented institutions.

Key Points

Collaborative AI adoption intensity => significant increases in:
- Human capital (new AI skills, epistemic reframing, meta‑competencies)
- Structural capital (AI-embedded routines, platforms, data governance)
- Relational capital (trust-building, shared digital artefacts, boundary-spanning routines)
Mediation: human, structural, and relational capital each partially mediate the AI → innovation relationship; structural capital has the largest mediating effect.
Complementarity: fsQCA shows multiple distinct IC configurations (three paths) can produce high innovation outcomes—combinations of IC dimensions, not any single dimension, are decisive.
Moderation by university type: research‑intensive universities extract greater IC (notably structural capital) from AI collaborations than teaching-oriented ones.
Network dynamics: ERGM reveals AI-mediated ties predict denser IC exchanges across university networks.
Governance: counterfactual simulations (500 iterations) indicate governance configuration (distributed vs. centralized) substantially moderates AI’s IC benefits; distributed governance amplifies gains more than increasing adoption intensity alone.
Conceptual contribution: extends IC theory to AI-mediated university-industry collaboration and challenges technology-deterministic views by emphasizing institutional and governance conditions.

Data & Methods

Research paradigm: critical realism.
Mixed-methods, three-phase design:
Qualitative multiple-case study: 48 semi-structured interviews across six Taiwanese universities + document analysis; thematic analysis informed model design.
Quantitative analysis: survey (n = 386) analyzed with PLS-SEM to test hypotheses; fuzzy-set qualitative comparative analysis (fsQCA) to identify configurational paths to high innovation performance.
Network analysis: exponential random graph models (ERGM) of knowledge-flow networks across the six universities; counterfactual simulations (500 iterations) to test governance scenarios (centralized vs distributed).
Key quantitative results: positive paths from Collaborative AI Adoption Intensity (CAAI) to Human Capital Development (HCD), Structural Capital Formation (SCF), and Relational Capital Creation (RCC); HCD, SCF, RCC each partially mediate the CAAI → IC-driven Innovation Performance (ICIP) pathway; interaction effects by university type observed.

Implications for AI Economics

Economic returns to AI investments depend on complementary intangible capital:
- Structural capital (platforms, data governance, integrated routines) produces the largest multiplier on innovation output; investments here may yield higher ROI than adoption-focused spending alone.
- Human and relational capital remain necessary complements—neglecting either reduces the productivity of AI investments.
Governance as a multiplier: institutional design and distributed governance materially affect the conversion of AI adoption into economic value. Policy and institutional funding that prioritize governance structures (data-sharing rules, co‑ownership arrangements, joint platforms) can increase social returns to AI.
Heterogeneous effects across institutions:
- Research-intensive institutions capture greater structural-capital benefits, implying uneven regional returns and the need for tailored funding or capacity-building in teaching-oriented institutions to avoid widening disparities.
Market structure and potential failures:
- Dependence on vendor-owned AI infrastructure (part of structural capital) introduces risks of lock-in and asymmetric bargaining power; economic policy should address competition, interoperability, and data-rights to protect public-value extraction.
Measurement and evaluation:
- Economic assessments of AI should incorporate metrics for structural and relational capital (platform durability, governance quality, network density) not just adoption counts or headcounts of trained staff.
Policy recommendations:
- Prioritize investments that build durable structural capital (shared platforms, institutional data governance).
- Support distributed governance models and interoperable infrastructure to maximize collective innovation value.
- Fund human capital investments emphasizing meta‑competencies (AI evaluation, ethics, integration) and incentives for collaborative knowledge production.
- Design regional policies to reduce inequality in AI-derived IC between research-intensive and teaching-oriented institutions.
Research directions for AI economics:
- Quantify returns-to-scale and complementarities between AI adoption and IC components (longitudinal/causal designs).
- Cross-country/sector comparisons to test generalizability beyond the Taiwan context.
- Model market-level effects of vendor lock-in and governance regimes on innovation diffusion and welfare.

Limitations to note for economic interpretation: sample and network analysis are Taiwan-centered and cross-sectional in key parts; causal inference is limited despite triangulation—longitudinal and cross-jurisdictional studies would strengthen generalizability.

Assessment

Paper Typecorrelational Evidence Strengthmedium — The paper uses multiple complementary empirical methods (qualitative interviews, a reasonably sized survey n=386, fsQCA, ERGM, and simulations), which strengthens confidence in observed associations and mechanisms; however, the cross-sectional observational design, likely self-reported measures, limited sampling frame (six universities in Taiwan), and absence of plausibly exogenous variation weaken causal inference. Methods Rigormedium — Strengths include a mixed-methods design that triangulates qualitative process evidence with PLS-SEM mediation tests, configurational fsQCA, and network ERGM, plus simulations; weaknesses are cross-sectional data limiting mediation causality, potential common-method bias, unclear sampling/response-rate details, and limited transparency in measurement validity and robustness checks (e.g., endogeneity tests, longitudinal checks). SampleMixed sample: Phase 1 — 48 semi-structured interviews across six Taiwanese universities (faculty, administrators and partners described qualitatively); Phase 2 — cross-sectional survey analyzed with PLS-SEM, n = 386 (respondents involved in university-industry AI collaboration, exact sampling frame and response rate not reported); Phase 3 — network data on knowledge-flow ties across the same six universities used for ERGM analysis; counterfactual governance simulations (500 iterations). Themeshuman_ai_collab innovation org_design adoption IdentificationObservational, multi-method triangulation: cross-sectional survey analyzed with PLS-SEM for mediation and moderation tests, configurational analysis using fsQCA, network inference with ERGM, and counterfactual simulations (500 iterations) to explore governance scenarios; no exogenous variation, instrument, or experimental design is reported, so causal claims rest on theoretical priors and robustness across methods rather than quasi-experimental identification. GeneralizabilitySingle-country study (Taiwan) limits cross-national generalizability, Analysis focuses on six universities, so institutional heterogeneity is limited, Likely non-random or convenience sampling of respondents limits population representativeness, Cross-sectional, self-reported survey measures constrain causal inference and may suffer from common-method bias, Findings pertain to entrepreneurial universities and may not extend to non-entrepreneurial or purely teaching-focused institutions, Specific AI platforms, industries, and project types are not comprehensively detailed, limiting applicability to other AI collaboration modalities

Claims (9)

Claim	Direction	Outcome	Confidence & Evidence	Details
University-industry AI collaboration intensity significantly enhances human capital development in entrepreneurial universities. Skill Acquisition	positive	Human capital development, including faculty knowledge and AI-related skills	Reading fidelity high Study strength medium	n=386 0.3
University-industry AI collaboration intensity significantly enhances structural capital formation. Organizational Efficiency	positive	Structural capital formation, including organizational systems, routines, protocols, databases, and AI-enabled infrastructure	Reading fidelity high Study strength medium	n=386 0.3
University-industry AI collaboration intensity significantly enhances relational capital creation. Team Performance	positive	Relational capital, including trust, collaboration, and knowledge-exchange relationships between universities and industry	Reading fidelity high Study strength medium	n=386 0.3
Human, structural, and relational capital each partially mediate the relationship between university-industry AI collaboration and IC-driven innovation performance. Innovation Output	positive	IC-driven innovation performance	Reading fidelity high Study strength medium	n=386 0.3
Structural capital is the strongest of the three intellectual-capital mediation pathways linking AI collaboration to innovation performance. Innovation Output	positive	Innovation performance mediated through structural capital formation	Reading fidelity high Study strength medium	n=386 0.3
Research-intensive universities benefit more than teaching-oriented universities in structural capital formation associated with AI collaboration. Organizational Efficiency	positive	Structural capital formation by university type	Reading fidelity high Study strength medium	n=386 0.3
The study identifies three configurational paths to high innovation performance based on combinations of intellectual-capital dimensions. Innovation Output	positive	High innovation performance	Reading fidelity high Study strength medium	n=386 3 paths 0.3
AI-mediated knowledge ties predict greater intellectual-capital exchange density in university-industry knowledge networks. Organizational Efficiency	positive	Density of intellectual-capital exchange in knowledge networks	Reading fidelity high Study strength medium	n=6 0.3
Governance configuration is more important than AI adoption intensity alone in determining intellectual-capital benefits. Governance And Regulation	positive	Intellectual-capital outcomes under alternative AI governance configurations	Reading fidelity high Study strength low	n=500 500 iterations 0.15