基于主成分分析与空间计量模型的中国高校 R&D 资源配置研究
##plugins.themes.bootstrap3.article.sidebar##
##plugins.themes.bootstrap3.article.main##
摘要
在全球知识经济时代，高校 R&D 资源的合理配置对于提升科技创新能力和区域经济发展具有重要意义。本文基于 2019-2023 年中国大陆省级行政区高校数据，采用主成分分析法构建 R&D 投入指数（RDI）与产出指数（RDO ，衡量高校科研资源配置与创新能力，并结合空间计量模型分析其空间分布特征。研究发现，东部地区高校普遍具有较高的 RDI 与 RDO，西部地区则整体偏低，区域间科技创新能力存在明显不均衡。空间自相关分析结果表明，高校 R&D 资源未形成显著的空间集聚效应，Moran’s I 统计量整体较低，空间溢出效应有限；但 OLS 回归结果显示，RDI 对 RDO 具有显著正向影响（回归系数 > 0.92，p < 0.001），科研投入仍是推动创新产出的关键因素。部分省份 RDI 与 RDO 波动较大，反映出科研投入不稳定与科技成果转化的周期性问题。研究建议通过区域协同创新优化高校 R&D 资源配置，推动东部发达地区科技资源向西部地区有序扩散，强化产学研合作，以提升科研投入效益，并为高等教育科研资源配置与区域创新政策提供理论支持与实证依据。
##plugins.themes.bootstrap3.article.details##
Chinese Journal of Social Science and Management Editorial Division
The Office of Research and Development, Panyapiwat Institute of Management
85/1 Moo 2, Chaengwattana Rd., Bang Talat, Pakkred, Nonthaburi 11120, Thailand
Tel. 02 855 01048 E-mail: cjssm@pim.ac.th
参考
Aksoy, A. (2019). The effects of exclusivity and company size on university research commercialisation. Semanticscholar. https://url.in.th/dZpOW
Anselin, L. (1988). Spatial econometrics: Methods and models. Springer Netherlands. https://books.google.co.th/books?id=3dPIXClv4YYC
Arbia, G., & Baltagi, B. H. (2009). Spatial econometrics: Methods and applications. Physica-Verlag. https://doi.org/10.1007/978-3-7908-2070-6
Belu, C., & Manescu, C. (2013). Strategic corporate social responsibility and economic performance. Applied Economics, 45(19), 2751-2764. https://doi.org/10.1080/00036846.2012.676734
Chais, C., Ganzer, P. P., & Olea, P. M. (2018). Technology transfer between universities and companies: Two cases of Brazilian universities. Innovation & Management Review, 15(1), 20-38. https://doi.org/10.1108/INMR-02-2018-002
Cheng, J. (2023). Technological progress, higher education quality and China’s industrial upgrading: An empirical study based on the spatial Dubin Model. In Proceedings of the 2023 4th International Conference on Education, Knowledge and Information Management (ICEKIM 2023) (pp. 312- 320). Atlantis Press. https://doi.org/10.2991/978-94-6463-172-2_34
Gao, L., & Lu, Y. (2016). Performance evaluation and countermeasure research on R&D input-output in universities based on DEA method: A case study of 29 universities in Hebei Province. Economics and Management, 30(2), 37-42. [in Chinese]
Hall, B. H., Mairesse, J., & Mohnen, P. (2010). Chapter 24—Measuring the returns to R&D. Handbook of the Economics of Innovation, 2, 1033-1082. https://doi.org/10.1016/S0169-7218(10)02008-3
He, X. -Y. (2023). R&D input-output efficiency of colleges and universities: Analysis based on two-stage DEA-VRS model and super efficiency model. Journal of Wuhan University of Technology (Social Science Edition), 36(5), 136-145. [in Chinese]
Hu, T. (2023). Research on R&D activities of colleges and universities—Based on statistical analysis of data from 2013 to 2021. Henan Science and Technology, 42(22), 153-158. [in Chinese]
Jiao, C. H., & Chen, Y. F. (2018). R&D resource allocation, spatial correlation, and the improvement of regional total factor productivity. Studies in Science of Science, 36(1), 81-92. https://doi.org/10.16192/j.cnki.1003-2053.2018.01.010
Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202. https://doi.org/10.1098/rsta.2015.0202
Kuzior, A., Samoilikova, A., Valúch, M., & Bába, É. B. (2024). Impact of university-industry R&D collaboration on innovation transfer and startup performance. Journal of International Studies, 17(3), 164-181. https://doi.org/10.14254/2071-8330.2024/17-3/9
Liu, X. (2022). Research on the inter-university differences in the allocation of competitive research funding in China: An analysis based on the national natural science foundation data. Advances in Education, 12(10), 3732-3738. https://doi.org/10.12677/AE.2022.1210570 [in Chinese]
Lü, H., Chi, R., & Hua, X. (2017). Spatial linkage of innovation resources and regional economic growth: An empirical analysis based on provincial data in China. Geographical Science, 37(11), 1649-1658. https://doi.org/10.13249/j.cnki.sgs.2017.11.006 [in Chinese]
Ma, S. Z., Li, Y. J., & Wu, G. J. (2015). Analysis of factors affecting the flow of technological innovation resources in China based on spatial autocorrelation. Economist, 11, 15-25. https://doi.org/10.16158/j.cnki.51-1312/f.2015.11.003 [in Chinese]
Papazoglou, M. E., & Spanos, Y. E. (2018). Bridging distant technological domains: A longitudinal study of the determinants of breadth of innovation diffusion. Research Policy, 47(9), 1713-1728. https://doi.org/10.1016/j.respol.2018.06.006
Robertson, P., & Jacobson, D. (2011). Knowledge transfer and technology diffusion. Edward Elgar Publishing.
Ru, C. (2016). Construction of industrial clusters knowledge collaborative innovation network. Information Sciences, 2016, 226. [in Chinese]
Salmenkaita, J. -P., & Salo, A. (2004). Emergent foresight processes: Industrial activities in wireless communications. Technological Forecasting and Social Change, 71(9), 897-912. https://doi.org/https://doi.org/10.1016/j.techfore.2003.09.001
Sanguansat, P. (2012). Principal component analysis. IntechOpen.
Sun, Z., & Hou, Y. (2019). A multi-perspective observation and policy response to China’s regional imbalanced development. Management World, 35(8), 1-8. https://doi.org/10.19744/j.cnki.11-1235/f.2019.0101 [in Chinese]
Tang, M., Zeng, Z., & Lan, Q. (2024). Research on allocation efficiency of higher education resources in China. Journal of Research in Vocational Education, 6(8), 1-5. https://doi.org/10.53469/jrve.2024.06(08).01
Wang, C., Wang, L., Xue, Y., & Li, R. (2022). Revealing spatial spillover effect in high-tech industry agglomeration from a high-skilled labor flow network perspective. Journal of Systems Science and Complexity, 35(3), 839-859. https://doi.org/10.1007/s11424-022-1056-1
Wang, X., Fang, H., Zhang, F., & Fang, S. (2018). The spatial analysis of regional innovation performance and industry-university-research institution collaborative innovation—An empirical study of Chinese provincial data. Sustainability, 10(4), 1243. https://www.mdpi.com/2071-1050/10/4/1243
Wolszczak-Derlacz, J. (2017). An evaluation and explanation of (in) efficiency in higher education institutions in Europe and the U.S. with the application of two-stage semi-parametric DEA. Research Policy, 46(9), 1595-1605. https://doi.org/10.1016/j.respol.2017.07.010
Zhang, H., Guo, D., & Zhang, H. (2021). Research on the efficiency of scientific and technological innovation resource allocation in universities under the background of “Double First-Class” initiative. Journal of Beijing Institute of Technology (Social Sciences Edition), 23(1), 171-179. https://doi.org/10.15918/j.jbitss1009-3370.2021.2457 [in Chinese]
Zhang, Y. Y., & Gao, Y. (2019). Can the flow of innovation elements promote the optimization of regional manufacturing structure?—Theoretical analysis and empirical test. Modern Finance & Economics (Journal of Tianjin University of Finance and Economics), 39(6), 98-113. https://doi.org/10.19559/j.cnki.12-1387.2019.06.007 [in Chinese]
Zhao, L., Zhu, B., & Gao, D. (2024). Organized scientific research in universities: Connotation, challenges, and solutions. Science and Technology Progress and Policy, 41(16), 150-160. https://url.in.th/GtzGX [in Chinese]
Zhao, S., & Li, J. (2022). Impact of innovation network on regional innovation performance: Do network density, network openness and network strength have any influence? Journal of Science and Technology Policy Management, 14(5), 982-995. https://doi.org/10.1108/jstpm-05-2022-0084
Zhou, M., Ma, S., & Kou, Z. (2019). R&D expenditure, spillover pool, and patent applications: An empirical study based on Chinese industrial listed companies. R&D Management, 31(2), 121-132. https://doi.org/10.13581/j.cnki.rdm.2019.02.012 [in Chinese]
