The regional disparity of influencing factors of technological innovation in China: evidence from high-tech industry
Abstract
Accurate analysis of technological innovation mechanism in different regions is the key to promoting China’s technological innovation, economic transformation and upgrading. This paper collected statistical data of high-tech enterprises in 27 provinces in China from 2009 to 2016, established a novel PSO-GRNN model, and applied sensitivity analysis to explore the influencing factors and regional differences of enterprise technological innovation in Eastern, Central and Western China. The empirical results showed that the influencing factors were innovation investment, market environment, government support and foreign technology spillover sorting by impact size. Innovation investment was the decisive factor of technological innovation, but innovation resources mainly concentrated on Eastern China, severely insufficient in Central and Western China. Market environment was favorable to Eastern and Central China, but unfavorable to Western China, which restricted greatly the development of Western China. The principalagent problem of state-owned enterprise and the crowding out effect of government research and development funds jointly led to the negative influence of government support on technological innovation. Foreign technology spillover had significant positive effects on technological innovation in Western China. This paper clarifies some disputes about influencing factors of technological innovation and provides a new research perspective for related issues.
First published online 27 May 2021
Keyword : technological innovation, regional disparity, high-tech industry, PSO-GRNN
How to Cite
Zhang, Y. (2021). The regional disparity of influencing factors of technological innovation in China: evidence from high-tech industry. Technological and Economic Development of Economy, 27(4), 811-832. https://doi.org/10.3846/tede.2021.14828
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Koc, T., & Ceylan, C. (2007). Factors impacting the innovative capacity in large-scale companies. Technovation, 27(3), 105–114. https://doi.org/10.1016/j.technovation.2005.10.002
Ladlani, I., Houichi, L., Djemili, L., Heddam, S., & Belouz, K. (2012). Modeling daily reference evapotranspiration (ET 0) in the north of Algeria using generalized regression neural networks (GRNN) and radial basis function neural networks (RBFNN): A comparative study. Meteorology and Atmospheric Physics, 118(3–4), 163–178. https://doi.org/10.1007/s00703-012-0205-9
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Li, G. S., & Shen, K. R. (2011). Study on technological introduction, independent R&D and innovation performance in China. Journal of Finance and Economics, 11.
Li, K., Qu, J., Pan, W., Ai, H., & Jia, P. (2020). Modelling technological bias and productivity growth: A case study of China’s three urban agglomerations. Technological and Economic Development of Economy, 26(1), 135–164. https://doi.org/10.3846/tede.2020.11329
Lin, J., & Zhang, P. (2006). Appropriate technology, technological selection, and economic growth in developing countries. China Economic Quarterly, 5(4), 985.
Luo, W., & Fu, Z. (201)3. Application of generalized regression neural network to the agricultural machinery demand forecasting. Applied Mechanics and Materials, 278–280, 2177–2182. https://doi.org/10.4028/www.scientific.net/AMM.278-280.2177
Marchioni, A., & Magni, C. A. (2018). Investment decisions and sensitivity analysis: NPV-consistency of rates of return. European Journal of Operational Research, 268(1), 361–372. https://doi.org/10.1016/j.ejor.2018.01.007
Mei, L., & Shao, W. (2016). The effect of firm size on regional innovation efficiency in China. Modern Economy, 7(10), 1035–1049. https://doi.org/10.4236/me.2016.710106
Noesselt, N. (2017). Governance change and patterns of continuity: Assessing China’s “New Normal”. Journal of Chinese Political Science, 22(3), 341–355. https://doi.org/10.1007/s11366-017-9487-6
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Wan, L., Luo, B., Li, T., Wang, S., & Liang, L. (2015). Effects of technological innovation on ecoefficiency of industrial enterprises in China. Nankai Business Review International, 6(3), 226–239. https://doi.org/10.1108/NBRI-01-2015-0003
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Wang, Y., Ning, L., Li, J., & Prevezer, M. (2016). Foreign direct investment spillovers and the geography of innovation in Chinese regions: The role of regional industrial specialization and diversity. Regional Studies, 50(5), 805–822. https://doi.org/10.1080/00343404.2014.933800
Wu, J., Ma, Z., & Zhuo, S. (2017). Enhancing national innovative capacity: The impact of high-tech international trade and inward foreign direct investment. International Business Review, 26(3), 502–514. https://doi.org/10.1016/j.ibusrev.2016.11.001
Wu, Y. B. (2008). The determinants of innovation – Empirical study based on Chinese manufacturing industry. The Journal of World Economy, 2, 46–58.
Xiao, W., Pan, J. D., & Liu, L. Y. (2018). China’s industrial structure upgrade in the “New Normal”: Empirical test and determinants. The Singapore Economic Review, 63(04), 1037–1058. https://doi.org/10.1142/S021759081742005X
Zhang, H., & Shi, J. (2011). On the technical efficiency of new product in Chinese provincial industry. Economic Research Journal, 1, 83–96.
Zhong, W., Yuan, W., Li, S. X., & Huang, Z. (2011). The performance evaluation of regional R&D investments in China: An application of DEA based on the first official China economic census data. Omega, 39(4), 447–455. https://doi.org/10.1016/j.omega.2010.09.004
Zhu, Y., Wang, Z., Qiu, S., & Zhu, L. (2019). Effects of environmental regulations on technological innovation efficiency in China’s industrial enterprises: A spatial analysis. Sustainability, 11(7), 2186. https://doi.org/10.3390/su11072186
Alarcón, S., & Sánchez, M. (2013). External and internal R&D, capital investment and business performance in the Spanish agri-food industry. Journal of Agricultural Economics, 64(3), 654–675. https://doi.org/10.1111/1477-9552.12015
Alfaro, L. (2016). Gains from foreign direct investment: Macro and micro approaches. The World Bank Economic Review, 30(Suppl. 1), S2–S15. https://doi.org/10.1093/wber/lhw007
An, J., He, G., Qin, F., Li, R., & Huang, Z. (2018). A new framework of global sensitivity analysis for the chemical kinetic model using PSO-BPNN. Computers & Chemical Engineering, 112, 154–164. https://doi.org/10.1016/j.compchemeng.2018.02.003
Cao, M. S., Pan, L. X., Gao, Y. F., Novák, D., Ding, Z. C., Lehký, D., & Li, X. L. (2017). Neural network ensemble-based parameter sensitivity analysis in civil engineering systems. Neural Computing and Applications, 28(7), 1583–1590. https://doi.org/10.1007/s00521-015-2132-4
China’s National Bureau of Statistics. (2019). 2009–2016 National Data [OL]. http://www.stats.gov.cn/
Cho, Y. (2020). The effects of knowledge assets and path dependence in innovations on firm value in the Korean semiconductor industry. Sustainability, 12(6), 2319. https://doi.org/10.3390/su12062319
Coad, A., Segarra, A., & Teruel, M. (2016). Innovation and firm growth: Does firm age play a role? Research Policy, 45(2), 387–400. https://doi.org/10.1016/j.respol.2015.10.015
Dazheng, W. (2009). Encourage the introduction of technology or encourage self-innovation. Science & Technology Progress and Policy, 24.
Demirel, P., & Mazzucato, M. (2012). Innovation and firm growth: Is R&D worth it? Industry and Innovation, 19(1), 45–62. https://doi.org/10.1080/13662716.2012.649057
Du, J., Liu, Y., & Diao, W. (2019). Assessing regional differences in green innovation efficiency of industrial enterprises in China. International Journal of Environmental Research and Public Health, 16(6), 940. https://doi.org/10.3390/ijerph16060940
Guo, D., Guo, Y., & Jiang, K. (2016). Government-subsidized R&D and firm innovation: Evidence from China. Research Policy, 45(6), 1129–1144. https://doi.org/10.1016/j.respol.2016.03.002
Holland, J. H. (1992). Adaptation in natural and artificial systems. MIT Press. https://doi.org/10.7551/mitpress/1090.001.0001
Hong, J., Feng, B., Wu, Y., & Wang, L. (2016). Do government grants promote innovation efficiency in China’s high-tech industries? Technovation, 57, 4–13. https://doi.org/10.1016/j.technovation.2016.06.001
Hu, A. G. (2001). Ownership, government R&D, private R&D, and productivity in Chinese industry. Journal of Comparative Economics, 29(1), 136–157. https://doi.org/10.1006/jcec.2000.1704
Hu, R., Wen, S., Zeng, Z., & Huang, T. (2017). A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm. Neurocomputing, 221, 24–31. https://doi.org/10.1016/j.neucom.2016.09.027
Hua, Z., Wang, Y., Xu, X., Zhang, B., & Liang, L. (2007). Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems with Applications, 33(2), 434–440. https://doi.org/10.1016/j.eswa.2006.05.006
Huang, Q., Jiang, M. S., & Miao, J. (2016). Effect of government subsidization on Chinese industrial firms’ technological innovation efficiency: A stochastic frontier analysis. Journal of Business Economics and Management, 17(2), 187–200. https://doi.org/10.3846/16111699.2015.1061590
Huang, Y., Salike, N., Yin, Z., & Zeng, D. Z. (2017). Enterprise innovation in China: Does ownership or size matter? (RIEI Working Papers 2017-06). Xi’an Jiaotong-Liverpool University, Research Institute for Economic Integration. https://ideas.repec.org/p/xjt/rieiwp/2017-06.html
Jefferson, G. H., Huamao, B., Xiaojing, G., & Xiaoyun, Y. (2006). R&D performance in Chinese industry. Economics of Innovation and New Technology, 15(4–5), 345–366. https://doi.org/10.1080/10438590500512851
Jiang, J. L., Su, X., Zhang, H., Zhang, X. H., & Yuan, Y. J. (2013). A novel approach to active compounds identification based on support vector regression model and mean impact value. Chemical Biology & Drug Design, 81(5), 650–657. https://doi.org/10.1111/cbdd.12111
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95 – International Conference on Neural Networks (Vol. 4, pp. 1942–1948). https://doi.org/10.1109/ICNN.1995.488968
Koc, T., & Ceylan, C. (2007). Factors impacting the innovative capacity in large-scale companies. Technovation, 27(3), 105–114. https://doi.org/10.1016/j.technovation.2005.10.002
Ladlani, I., Houichi, L., Djemili, L., Heddam, S., & Belouz, K. (2012). Modeling daily reference evapotranspiration (ET 0) in the north of Algeria using generalized regression neural networks (GRNN) and radial basis function neural networks (RBFNN): A comparative study. Meteorology and Atmospheric Physics, 118(3–4), 163–178. https://doi.org/10.1007/s00703-012-0205-9
Lewandowska, A., Pater, R., & Cywiński, L. (2019). Determinants of business innovation in the Regional Innovation System context. Policy implications for a less developed region. Studia Regionalne i Lokalne, 1(75), 5–27. https://doi.org/10.7366/1509499517501
Li, D., Chau, P. Y., & Lai, F. (2010). Market orientation, ownership type, and e‐business assimilation: Evidence from Chinese firms. Decision Sciences, 41(1), 115–145. https://doi.org/10.1111/j.1540-5915.2009.00261.x
Li, G. S., & Shen, K. R. (2011). Study on technological introduction, independent R&D and innovation performance in China. Journal of Finance and Economics, 11.
Li, K., Qu, J., Pan, W., Ai, H., & Jia, P. (2020). Modelling technological bias and productivity growth: A case study of China’s three urban agglomerations. Technological and Economic Development of Economy, 26(1), 135–164. https://doi.org/10.3846/tede.2020.11329
Lin, J., & Zhang, P. (2006). Appropriate technology, technological selection, and economic growth in developing countries. China Economic Quarterly, 5(4), 985.
Luo, W., & Fu, Z. (201)3. Application of generalized regression neural network to the agricultural machinery demand forecasting. Applied Mechanics and Materials, 278–280, 2177–2182. https://doi.org/10.4028/www.scientific.net/AMM.278-280.2177
Marchioni, A., & Magni, C. A. (2018). Investment decisions and sensitivity analysis: NPV-consistency of rates of return. European Journal of Operational Research, 268(1), 361–372. https://doi.org/10.1016/j.ejor.2018.01.007
Mei, L., & Shao, W. (2016). The effect of firm size on regional innovation efficiency in China. Modern Economy, 7(10), 1035–1049. https://doi.org/10.4236/me.2016.710106
Noesselt, N. (2017). Governance change and patterns of continuity: Assessing China’s “New Normal”. Journal of Chinese Political Science, 22(3), 341–355. https://doi.org/10.1007/s11366-017-9487-6
Pavitt, K., Robson, M., & Townsend, J. (1987). The size distribution of innovating firms in the UK: 1945–1983. The Journal of Industrial Economics, 35(3), 297–316. https://doi.org/10.2307/2098636
Pfeffer, J., & Salancik, G. R. (2003). The external control of organizations: A resource dependence perspective. Stanford University Press.
Schumpeter J., & Backhaus U. (2003). The theory of economic development. In J. Backhaus (Ed.), Joseph Alois Schumpeter. The European heritage in economics and the social sciences (Vol. 1, pp. 61–116). Springer, Boston, MA. https://doi.org/10.1007/0-306-48082-4_3
Shefer, D., & Frenkel, A. (2005). R&D, firm size and innovation: an empirical analysis. Technovation, 25(1), 25–32. https://doi.org/10.1016/S0166-4972(03)00152-4
Smith, N., & Thomas, E. (2017). Regional conditions and innovation in Russia: The impact of foreign direct investment and absorptive capacity. Regional Studies, 51(9), 1412–1428. https://doi.org/10.1080/00343404.2016.1164307
Stock, G. N., Greis, N. P., & Fischer, W. A. (2002). Firm size and dynamic technological innovation. Technovation, 22(9), 537–549. https://doi.org/10.1016/S0166-4972(01)00061-X
Szczygielski, K., Grabowski, W., Pamukcu, M. T., & Tandogan, V. S. (2017). Does government support for private innovation matter? Firm-level evidence from two catching-up countries. Research Policy, 46(1), 219–237. https://doi.org/10.1016/j.respol.2016.10.009
Tseng, S. C., & Hung, S. W. (2013). A framework identifying the gaps between customers’ expectations and their perceptions in green products. Journal of Cleaner Production, 59, 174–184. https://doi.org/10.1016/j.jclepro.2013.06.050
Wan, L., Luo, B., Li, T., Wang, S., & Liang, L. (2015). Effects of technological innovation on ecoefficiency of industrial enterprises in China. Nankai Business Review International, 6(3), 226–239. https://doi.org/10.1108/NBRI-01-2015-0003
Wang, Y., & Guo, Y. (2008). An empirical study on R&D input and output efficiency of listed companies. Industrial Economics Research, 6(6), 44–52.
Wang, Y., Ning, L., Li, J., & Prevezer, M. (2016). Foreign direct investment spillovers and the geography of innovation in Chinese regions: The role of regional industrial specialization and diversity. Regional Studies, 50(5), 805–822. https://doi.org/10.1080/00343404.2014.933800
Wu, J., Ma, Z., & Zhuo, S. (2017). Enhancing national innovative capacity: The impact of high-tech international trade and inward foreign direct investment. International Business Review, 26(3), 502–514. https://doi.org/10.1016/j.ibusrev.2016.11.001
Wu, Y. B. (2008). The determinants of innovation – Empirical study based on Chinese manufacturing industry. The Journal of World Economy, 2, 46–58.
Xiao, W., Pan, J. D., & Liu, L. Y. (2018). China’s industrial structure upgrade in the “New Normal”: Empirical test and determinants. The Singapore Economic Review, 63(04), 1037–1058. https://doi.org/10.1142/S021759081742005X
Zhang, H., & Shi, J. (2011). On the technical efficiency of new product in Chinese provincial industry. Economic Research Journal, 1, 83–96.
Zhong, W., Yuan, W., Li, S. X., & Huang, Z. (2011). The performance evaluation of regional R&D investments in China: An application of DEA based on the first official China economic census data. Omega, 39(4), 447–455. https://doi.org/10.1016/j.omega.2010.09.004
Zhu, Y., Wang, Z., Qiu, S., & Zhu, L. (2019). Effects of environmental regulations on technological innovation efficiency in China’s industrial enterprises: A spatial analysis. Sustainability, 11(7), 2186. https://doi.org/10.3390/su11072186