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Transforming Talent Development: a Reflective Analysis of the Innovative Government-School Cooperation Model Under the Paradigm of Knowledge Innovation

Author

Listed:
  • Shu Zhang

    (Yangzhou Polytechnic Institute)

  • Ziwen Sun

    (City University of Macau)

  • Zhongyi Fan

    (Wenzhou University)

  • Shiyang Weng

    (Wenzhou University)

Abstract

In the fiercely competitive academic landscape, universities face the imperative to thrive through governmental support and cooperative models for talent development. This trilateral collaboration, involving government, enterprise, and educational institutions, synchronizes talent development with industry standards, resolves talent shortages, and fosters mutual benefits. Proactive collaboration with advanced enterprises is essential to stimulate students’ proactiveness and elevate employment rates. The deepening collaboration among government, businesses, and educational institutions enhances employment opportunities for graduates. This study advocates for talent cultivation within government-business-educational institution cooperative universities, aligning with societal development needs. It offers valuable insights into academia-industry dynamics in the knowledge economy. Our research introduces cognitive graphs as a dynamic tool for knowledge comprehension and memorization, contributing significantly to the scholarly discourse. The cognitive graph-based approach introduces a structured framework for talent cultivation, enhancing students’ learning experiences. Recommendations include raising stakeholder awareness and deeper integration of cognitive graphs into pedagogical strategies. This research enriches the existing literature and offers practical recommendations for universities and educational institutions. In the context of China’s economic restructuring, the imperative lies in the cultivation of talent through a tripartite partnership. Cognitive graphs serve as invaluable tools, guiding students along the correct learning trajectory. The talent cultivation model of government-enterprise-school cooperative universities, integrating cognitive graphs, yields students with heightened learning abilities while maintaining a cost-effective structure. Theoretical implications highlight the importance of innovative teaching methodologies and personalized learning. The successful application of cognitive graphs as a knowledge integration tool underscores their value in enhancing students’ ability to think systematically and creatively. The analysis of shortcomings in traditional cooperation models emphasizes the need for a comprehensive theoretical framework. Managerial implications stress the importance of adopting a cognitive graph-based approach in collaborative talent cultivation. Enterprises can benefit from actively participating in collaborative talent development. Enhancing cooperation awareness and commitment among stakeholders is crucial for creating a conducive environment for talent development.

Suggested Citation

  • Shu Zhang & Ziwen Sun & Zhongyi Fan & Shiyang Weng, 2024. "Transforming Talent Development: a Reflective Analysis of the Innovative Government-School Cooperation Model Under the Paradigm of Knowledge Innovation," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 15176-15201, September.
    • Handle: RePEc:spr:jknowl:v:15:y:2024:i:3:d:10.1007_s13132-023-01677-z
    DOI: 10.1007/s13132-023-01677-z
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    References listed on IDEAS

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    1. Kolade, Oluwaseun & Adegbile, Abiodun & Sarpong, David, 2022. "Can university-industry-government collaborations drive a 3D printing revolution in Africa? A triple helix model of technological leapfrogging in additive manufacturing," Technology in Society, Elsevier, vol. 69(C).
    2. Brown, Phillip & Hesketh, Anthony, 2004. "The Mismanagement of Talent: Employability and Jobs in the Knowledge Economy," OUP Catalogue, Oxford University Press, number 9780199269549.
    3. Kerstin Fink & Christian Ploder, 2009. "Knowledge Management Toolkit for SMEs," International Journal of Knowledge Management (IJKM), IGI Global, vol. 5(1), pages 46-60, January.
    4. Fatima, Samar & Desouza, Kevin C. & Dawson, Gregory S., 2020. "National strategic artificial intelligence plans: A multi-dimensional analysis," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 178-194.
    5. Erjia Yan & Ying Ding, 2009. "Applying centrality measures to impact analysis: A coauthorship network analysis," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(10), pages 2107-2118, October.
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