ENHANCING UNIVERSITY MANAGEMENT: STRATEGIES FOR EFFECTIVE TRANSFORMATION OF SCIENTIFIC AND TECHNOLOGICAL ACHIEVEMENTS
DOI:
https://doi.org/10.52152/801615Keywords:
University Management, Scientific and Technological Achievements, Technology Transfer, Higher Education Innovation, Statistical Analysis, ChinaAbstract
This study investigates the management strategies of Tsinghua University and Nanchang University in China, focusing on their approaches to the transformation of scientific and technological achievements (STAs). While both universities are key players in the national innovation ecosystem, their contrasting institutional frameworks highlight different pathways of innovation governance. A mixed-methods design was employed, combining qualitative document analysis with quantitative survey data collected from 120 participants (faculty, staff, and postgraduate students). The study addresses four research questions and tests four hypotheses concerning the influence of management strategy, collaboration intensity, intellectual property (IP) governance, and digital infrastructure on STA transformation success. Statistical analysis, including t-tests, chi-square tests, correlation, and multiple regression analysis, reveals that Tsinghua significantly outperforms Nanchang across all dimensions, with digital infrastructure (β = 0.42, p < 0.01) and IP governance (β = 0.37, p < 0.05) emerging as the strongest predictors of STA transformation efficiency. These results provide empirical evidence that robust institutional governance and professional IP management are critical enablers of innovation, while reactive and partnership-dependent approaches limit sustainable outcomes. A conceptual framework is proposed, emphasizing the mediating role of stakeholder integration, policy coherence, and talent development. The findings contribute to higher education innovation research and offer policy recommendations for universities seeking to strengthen their role in national and global innovation systems.
All students provided explicit, informed consent for their data to be used.
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