---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Carlos
      foaf_name: Plata, Carlos
      foaf_surname: Plata
  - foaf_Person:
      foaf_givenName: Alejandro
      foaf_name: Casallas Garcia, Alejandro
      foaf_surname: Casallas Garcia
      foaf_workInfoHomepage: http://www.librecat.org/personId=92081129-2d75-11ef-a48d-b04dd7a2385a
    orcid: 0000-0002-1988-5035
  bibo_doi: 10.5465/AMPROC.2025.54bp
  bibo_issue: '1'
  bibo_volume: 2025
  dct_date: 2025^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0065-0668
  - http://id.crossref.org/issn/2151-6561
  dct_language: eng
  dct_publisher: Academy of Management@
  dct_title: Machine learning analysis of the factors influencing university-industry
    collaborations@
...