科研管理 ›› 2024, Vol. 45 ›› Issue (3): 122-132.DOI: 10.19571/j.cnki.1000-2995.2024.03.013

• 论文 • 上一篇    下一篇

地理邻近对高校科技成果转化效率的影响研究

梁爽   

  1. 上海交通大学安泰经济与管理学院国家战略研究院,上海200030
  • 收稿日期:2022-09-04 修回日期:2023-12-19 出版日期:2024-03-20 发布日期:2024-03-11
  • 通讯作者: 梁爽
  • 基金资助:
    上海市哲学社会科学规划教育学青年项目:“高校郊区化对我国城郊协调发展的影响研究”(B2003,2020—2023);上海交通大学决策咨询课题:“高水平院校科技成果转化效率研究及优化建议”(JCZXZGC2022-11,2022—2024)。

Research on the impact of geographical proximity on the transformation efficiency of scientific and technological achievements in universities

Liang Shuang   

  1. Institute of Strategic Studies, Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China
  • Received:2022-09-04 Revised:2023-12-19 Online:2024-03-20 Published:2024-03-11

摘要:     科技成果转化是高校服务经济社会高质量发展的重要举措,而其效率如何提升是亟待解决的现实问题。本文在邻近性与创新研究框架下,基于京津冀、长三角、珠三角城市群2006—2017年高校与城市数据,采用Bootstrap-DEA模型估计高校科技成果转化的知识生产和产业转化两阶段效率,在此基础上,运用样本选择模型实证检验了城市群内部与城市群之间的地理邻近度对高校科技成果转化的分阶段效率影响。研究发现:三大城市群高校的知识生产和产业转化效率均偏低,其中知识生产阶段表现略好,产业转化阶段更为薄弱;考虑互联网因素后,城市群内部空间联系对高校科技成果转化两阶段效率均表现出显著正效应,线上联通不能完全替代地理邻近;其他影响因素中,高级职称人员与“双一流”建设显著提高知识产出效率却不利于产业转化,知识生产阶段企事业单位经费促进效率提升,政府经费反而带来效率损失,产业转化阶段两者均具有正效应。研究结论对于推动城市群深度一体化建设、打造创新空间,以及引导知识产出阶段多元化经费投入、对接产业,探索产业转化阶段全方位政策支持、跨主体合作具有一定的现实启示。

关键词: 科技成果转化效率, 地理邻近, Bootstrap-DEA, 样本选择模型

Abstract:     Transformation of scientific and technological achievements is a crucial undertaking for universities to contribute to China′s economic and social high-quality development. How to enhance the transformation efficiency stands as an urgent need to study. Within the research framework of proximity and innovation, this paper made an estimation of the efficiency of knowledge production and industrial transformation, which are two stages of university′s transformation of scientific and technological achievements. To this end, the bootstrap DEA model was employed, using the data of universities and cities within urban agglomerations of the Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Pearl River Delta from 2006 to 2017. Subsequently, the sample selection model was used to analyze the influence of geographical proximity of both the intra- and inter-urban agglomerations on universities′ efficiency, with regards to two stages of scientific and technological achievements transformation.The results of this study are as follows. Both knowledge production and industrial transformation in universities are inefficient within the three urban agglomerations. The universities perform better at the stage of knowledge production but face more challenges when they come to the stage of industrial transformation. After considering the evolution of the Internet, the spatial connection of intra-urban agglomeration has a significant positive effect on the transformation efficiency of scientific and technological achievements at both stages, which means geographical proximity cannot be wholly replaced with Internet connection. In addition to other factors, the number of associate professors and professors, as well as the universities with the prestigious title of "first-class universities and disciplines of the world", improve knowledge production efficiency significantly. However, they are not conducive to industrial transformation. At the stage of knowledge production, the funds allocated by enterprises and institutions promote universities′ efficiency significantly, whereas government grants produce the opposite effect. Nonetheless, at the stage of industrial transformation, both types of funding have a positive impact on universities′ efficiency. The findings will bear significant practical implications for advancing the seamless integration of urban agglomerations, fostering innovative environments, directing diverse investment and focusing on industry demand at the knowledge production stage, as well as developing holistic policy and interdisciplinary collaboration during the stage of industrial transformation.

Key words: transformation efficiency of scientific and technological achievements, geographical proximity, Bootstrap-DEA, sample selection model