[口头报告]Research on the Efficiency and Influencing Factors of University Science and Technology Innovation in China — Based on the Three-stage DEA-Tobit Analysis of Panel Data from 31 Provinces
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[口头报告]Research on the Efficiency and Influencing Factors of University Science and Technology Innovation in China — Based on the Three-stage DEA-Tobit Analysis of Panel Data from 31 Provinces

Research on the Efficiency and Influencing Factors of University Science and Technology Innovation in China — Based on the Three-stage DEA-Tobit Analysis of Panel Data from 31 Provinces
编号:139 稿件编号:48 访问权限:仅限参会人 更新:2024-05-21 11:13:16 浏览:547次 口头报告

报告开始:2024年05月31日 15:00 (Asia/Shanghai)

报告时间:15min

所在会议:[S8] Resource & Energy Security and Emergency Management » [S8-2] Afternoon of May 31st

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摘要
This study, based on panel data from 31 provinces in China from 2017 to 2021, evaluates the technical efficiency, pure technical efficiency, and scale efficiency of university science and technology innovation (STI) using a three-stage DEA model, considering the characteristics of the university STI process and the impact of non-managerial factors. It further investigates the influencing factors on the efficiency of university STI through Tobit regression analysis. The results indicate an overall upward trend in the efficiency of university STI across China, with a regional disparity pattern of “highest in the northeast, followed by the central region, then the east, and lowest in the west”. Scale efficiency is identified as a primary factor affecting the technical efficiency of university STI. The economic environment and policy support negatively impact input efficiency, whereas the level of industrial structure has a positive impact. Personnel structure, talent cultivation, and internationalization level are important factors for improving the efficiency of university STI. High investment does not necessarily result in high efficiency and optimizing the funding management system is essential for government support and industry-academic cooperation.
关键字
University Science and Technology Innovation; Three-stage DEA; Tobit Regression; Influencing Factors
报告人
Yang WANG
Ph.D Candidate China University of Mining and Technology

稿件作者
Yang WANG China University of Mining and Technology
Aibin LI China University of Mining and Technology
Yuke ZHU China University of Mining and Technology
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