Comparing the efficiency of regional knowledge innovation and technological innovation: a case study of China
Abstract
The combination of knowledge innovation and technology innovation provides vitality for social science and technology innovation. China leaps into the front ranks of the world in the 2021 Global Innovation Index (GII). Therefore, this research takes China's theoretical-application innovation as the research object and empirically analyzes measure the innovation efficiency of knowledge innovation dominated by universities and technological innovation dominated by enterprises in China, as well as the gravity-center migration trajectory. The results show that the ranking of overall efficiency of theoretical innovation-application innovation is eastern region > central region > western region. Knowledge innovation presents a drag on overall efficiency, while technology innovation offers a contribution to overall efficiency. In the analysis of PIE (R&D personnel of industrial enterprises above a designated size) variables, the efficiency value is relatively low. The peak value of kernel density increases in the eastern, central and western regions, namely the concentration degree of theoretical innovation-application innovation efficiency in China has risen. The gravity center of each stage migrates to the eastern region, meaning the efficiency value of China’s theoretical innovation and application innovation increases more significantly in the eastern region. From the perspective of knowledge innovation and technology innovation, this paper puts forward suggestions for China and provides some references for other developing countries.
First published online 10 August 2022
Keyword : PEBM model, theoretical innovation stage, application innovation stage, efficiency
How to Cite
Shi, Z., Wu, Y., Chiu, Y.- ho, Shi, C., & Na, X. (2022). Comparing the efficiency of regional knowledge innovation and technological innovation: a case study of China. Technological and Economic Development of Economy, 28(5), 1392–1418. https://doi.org/10.3846/tede.2022.17125
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Cassiman, B., & Veugelers, R. (2006). In search of complementarity in innovation strategy: Internal R&D and external knowledge acquisition. Management Science, 52(1), 68–82. https://doi.org/10.1287/mnsc.1050.0470
Castelli, L., Pesenti, R., & Ukovich, W. (2010). A classification of DEA models when the internal structure of the Decision Making Units is considered. Annals of Operations Research, 173(1), 207–235. https://doi.org/10.1007/s10479-008-0414-2
Chandran, V. G. R., Nourani, M., Selvarajan, S. K., & Baskaran, A. (2021). Selective research funding policy and catching up the ladder in university research performance in Malaysia. Managerial and Decision Economics, 42(3), 539–550. https://doi.org/10.1002/mde.3252
Chiang, K., & Shiuh-Nan, H. (2010). Efficiency measurement for network systems: IT impact on firm performance. Decision Support Systems, 48(3), 437–446. https://doi.org/10.1016/j.dss.2009.06.002
Cu, S. S. (2020). Effects of corporate governance on R&D investment in marine technology enterprises. Journal of Coastal Research, 110, 167–170. https://doi.org/10.2112/JCR-SI110-040.1
Ding, C. H., Liu, C., Zheng, C. Y., & Li, F. (2022). Digital economy, technological innovation and high-quality economic development: Based on spatial effect and mediation effect. Sustainability, 14(1), 216. https://doi.org/10.3390/su14010216
Färe, R., Grosskopf, S., & Whittaker, G. (2007). Network DEA. In J. Zhu & W. D. Cook (Eds.), Modeling data irregularities and structural complexities in data envelopment analysis (pp. 209–240). Springer. https://doi.org/10.1007/978-0-387-71607-7_12
Guan, H. J., Zhang, Z., Zhao, A. W., & Guan, S. (2019). Simulating environmental innovation behavior of private enterprise with innovation subsidies. Complexity, 2019(9), 1–12. https://doi.org/10.1155/2019/4629457
Guironnet, J. P., & Peypoch, N. (2018). The geographical efficiency of education and research: The ranking of US universities. Socio-Economic Planning Sciences, 62, 44–55. https://doi.org/10.1016/j.seps.2017.07.003
Ho, M. H. C., Liu, J. S., Lu, W. M., & Huang, C. C. (2014). A new perspective to explore the technology transfer efficiencies in US universities. Journal of Technology Transfer, 39(2), 247–275. https://doi.org/10.1007/s10961-013-9298-7
Hu, J. L., & Wang, S. C. (2006). Total-factor energy efficiency of regions in China. Energy Policy, 34(17), 3206–3217. https://doi.org/10.1016/j.enpol.2005.06.015
Kao, C. (2009). Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research, 192(3), 949–962. https://doi.org/10.1016/j.ejor.2007.10.008
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Kihombo, S., Ahmed, Z., Chen, S. S., Adebayo, T. S., & Kirikkaleli, D. (2021). Linking financial development, economic growth, and ecological footprint: What is the role of technological innovation? Environmental Science and Pollution Research, 28(43), 61235–61245. https://doi.org/10.1007/s11356-021-14993-1
Koskinen, K. U., & Vanharanta, H. (2002). The role of tacit knowledge in innovation processes of small technology companies. International Journal of Production Economics, 80(1), 57–64. https://doi.org/10.1016/s0925-5273(02)00243-8
Li, T. C., Liang, L., & Han, D. R. (2018a). Research on the efficiency of green technology innovation in China’s provincial high-end manufacturing industry based on the RAGA-PP-SFA model. Mathematical Problems in Engineering, 2018(20), 1–13. https://doi.org/10.1155/2018/9463707
Li, X. F., Li, Z., Yang, G. M., & Ma, D. L. (2018b). Empirical study of the knowledge innovation efficiency of universities in different regions of China: Panel data analysis based on mSBM. Educational Sciences: Theory & Practice, 18(5), 1087–1100. https://doi.org/10.12738/estp.2018.5.011
Liao, H. W., Yang, L. P., Ma, H. N., & Zheng, J. J. (2020). Technology import, secondary innovation, and industrial structure optimization: A potential innovation strategy for China. Pacific Economic Review, 25(2), 145–160. https://doi.org/10.1111/1468-0106.12326
Lubango, L. M., & Pouris, A. (2009). Is patenting activity impeding the academic performance of South African University researchers? Technology in Society, 31(3), 315–324. https://doi.org/10.1016/j.techsoc.2009.03.011
Martinez-Campillo, A., & Fernandez-Santos, Y. (2020). Check the impact of the economic crisis on the (in)efficiency of public Higher Education institutions in Southern Europe: The case of Spanish universities. Socio-Economic Planning Sciences, 71, 100771. https://doi.org/10.1016/j.seps.2019.100771
Moncayo-Martinez, L. A., Ramirez-Nafarrate, A., & Hernandez-Balderrama, M. G. (2020). Evaluation of public HEI on teaching, research, and knowledge dissemination by Data Envelopment Analysis. Socio-Economic Planning Sciences, 69, 100718. https://doi.org/10.1016/j.seps.2019.06.003
Nam, E. Y., & Wang, X. L. (2020). Innovation space driving business growth of semiconductor enterprises: A case study of South Korean Samsung’s investment in China. Journal of Korea Trade, 24(6), 37–60. https://doi.org/10.35611/jkt.2020.24.6.37
Pei, J. M., Zhong, K. Y., Li, J. H., Xu, J. Y., & Wang, X. Y. (2021). ECNN: Evaluating a cluster-neural network model for city innovation capability. Neural Computing & Applications. https://doi.org/10.1007/s00521-021-06471-z
Sagarra, M., Mar-Molinero, C., & Agasisti, T. (2017). Exploring the efficiency of Mexican universities: Integrating Data Envelopment Analysis and Multidimensional Scaling. Omega – International Journal of Management Science, 67, 123–133. https://doi.org/10.1016/j.omega.2016.04.006
Shamohammadi, M., & Oh, D. H. (2019). Measuring the efficiency changes of private universities of Korea: A two-stage network data envelopment analysis. Technological Forecasting and Social Change, 148, 119730. https://doi.org/10.1016/j.techfore.2019.119730
Shan, S. Q., Jia, Y. W., Zheng, X. R., & Xu, X. B. (2018). Assessing relationship and contribution of China’s technological entrepreneurship to socio-economic development. Technological Forecasting and Social Change, 135, 83–90. https://doi.org/10.1016/j.techfore.2017.12.022
Tone, K., & Tsutsui, M. (2010). An epsilon-based measure of efficiency in DEA – A third pole of technical efficiency. European Journal of Operational Research, 207(3), 1554–1563. https://doi.org/10.1016/j.ejor.2010.07.014
Tsoukas, H. (2009). A dialogical approach to the creation of new knowledge in organizations. Organization Science, 20(6), 941–957. https://doi.org/10.1287/orsc.1090.0435
Wang, T., Pan, S. C., Zhu, X. Y., & Liao, B. (2022). Research on the influence of innovation ability on the level of university scientific research: A case study of the Nine-University Alliance in China. Emerging Markets Finance and Trade, 58(1), 134–144. https://doi.org/10.1080/1540496x.2019.1636227
Wang, Y., Deng, Q. M., & Zhang, Y. H. (2020). Research on the coupling and coordinated development of marine technological innovation and marine ecological economic development. Journal of Coastal Research, 99, 419–427. https://doi.org/10.2112/si99-057.1
Zhang, F. Q., Wang, Y., & Liu, W. (2020). Science and technology resource allocation, spatial association, and regional innovation. Sustainability, 12(2), 694. https://doi.org/10.3390/su12020694
Zhong, X., Song, T., & Chen, W. (2021). Can R&D internationalization improve EMNES’ innovation efficiency? The moderating effects of TMT human capital. Baltic Journal of Management, 16(2), 190–207. https://doi.org/10.1108/BJM-03-2020-0098
Zhou, X. X., Cai, Z. M., Tan, K. H., Zhang, L. L., Du, J. T., & Song, M. L. (2021). Technological innovation and structural change for economic development in China as an emerging market. Technological Forecasting and Social Change, 167, 120671. https://doi.org/10.1016/j.techfore.2021.120671
Zhou, Z. J., Wang, Y., Lu, M. M., & Zhu, J. M. (2020). Government intervention, financial support, and comprehensive efficiency of enterprise independent innovation: Empirical analysis based on the data of Chinese strategic emerging industries. Mathematical Problems in Engineering, 2020, 8723062. https://doi.org/10.1155/2020/8723062