Machine Learning Analysis of The Factors Influencing University-Industry Collaborations

In the dynamic arena of innovation, the relations between academia and industry are a keystone for breakthroughs and practical applications. Yet, the groundwork of these pivotal University-Industry (U-I) partnerships remains covered in complexity. This paper delves into these intricate relations, unraveling the factors that help successful collaborations. Grounded in the Resource-Based Theory, our study transcends traditional analytical boundaries, leveraging a neural network model to understand a comprehensive dataset from the UK's Higher Education Statistics Agency, SCIMAGO Rankings, and Clarivate Publications. This novel approach helps to make clear the interplay of academic load, administrative support, scientific output, and university rank in sculpting U-I collaboration dynamics. Our findings suggest that reduced academic load and robust administrative support significantly bolster U-I collaborations. However, the influence of scientific output and university ranking is more nuanced, challenging the common belief. High scientific output, while indicative of expertise, doesn't always align with industry goals. Similarly, while higher-ranked universities could attract more collaborations, the benefits are not universal. This paper not only contributes to a deeper understanding of U-I collaborations, but also provides actionable insights for university administrators, policymakers, and industry leaders. In a world where innovation is key, understanding these collaborative dynamics is crucial for fostering partnerships that push the boundaries of research and practical application.