Mapping the conceptual structure of research on open innovation in university–industry collaborations: a bibliometric analysis

Abstract

Introduction Open innovation has become a central mechanism for enhancing university–industry collaboration (UIC), fostering the co-development of innovative and socially responsive solutions. As organizations increasingly embrace openness and knowledge-sharing practices, understanding the evolution of open innovation in university–industry collaboration (OIUIC) is critical amid accelerating digitalization and mounting sustainability imperatives. Methods This review maps the conceptual structure of OIUIC research from 2003 to 2024 by applying co-word analysis and social network mapping to a dataset of 2,601 articles indexed in Scopus. We extracted and standardized 5,269 unique keywords, constructed co-word networks to identify thematic clusters, and deployed network metrics to reveal patterns of scholarly collaboration and influence. Results The analysis uncovered five dominant keyword clusters: “technology transfer,” “university–industry knowledge transfer (UIKT),” “knowledge transfer,” “academic entrepreneurship,” and “university,” which collectively define the field's conceptual architecture. Geographically, the United Kingdom leads in publication output, while Research Policy and The Journal of Technology Transfer emerge, respectively, as the most cited and the most prolific journals. Network metrics further highlight key author and institution hubs that bridge thematic communities. Discussion By synthesizing major themes and research clusters, this review provides a comprehensive overview of the OIUIC intellectual landscape. Our findings offer critical insights for researchers and policymakers, suggesting priority areas for future inquiry, such as digital transformation, sustainability integration and cross-regional partnership models, and informing evidence-based policy development to strengthen inclusive and adaptive innovation ecosystems.

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Papers that cite the same references — the relational fallback while embeddings are still backfilling.