Study on the heterogeneity of the influence path of high-tech industrial park policies on innovation efficiency

References

• Balk. (1994). Technology innovation policy in the market economy. Scientific Research, 12(4), 47–54. https://doi.org/10.1086/592423
   Google Scholar
• Cai, Z. D. (2012). The international comparison of high-tech industrial parks.
   Google Scholar
• Cheng, Z. (2016). On the industrial transfer strategy of developed regions in eastern China to central and western China. The New Economy, (21), 39. CNKI:SUN:XJJB.0.2016-21-031
   Google Scholar
• Gao, F., & Guo, H. X. (2014). Research on the conceptual model of science and technology innovation policy lag. Science & Technology Progress and Policy, 31(10), 101–105. https://doi.org/10.6049/kjjbydc.2013080735
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Hu, L. N. (2020). Effect simulation and countermeasure research on the implementation of China’s policies supporting scientific and technological innovation. China Science and Technology Forum, (1), 1–8. https://doi.org/10.13580/j.cnki.fstc.2020.01.002
   Google Scholar
• Hu, L. Y., & Nie, J. (2002). Research on the formation mechanism and countermeasures of industrial group in high-tech parks in China. China Science and Technology Forum, (4), 49–52. https://doi.org/10.3969/j.issn.1002-6711.2002.04.011
   Google Scholar
• Jia, F. T., & Wang, Y. (2005). Mathematical model of technological diffusion in technical economic systems. Journal of Liaoning University of Engineering and Technology, (3), 250–251. https://doi.org/10.3969/j.issn.1008-391X.2005.03.010
   Google Scholar
• Jia, X. F., & Hu, Z. M. (2022). Research on the framework of science and technology innovation policy system. Science and Technology Management Research, 42(15), 43–48. https://doi.org/10.37155/2717-5189-0408-81
   Google Scholar
• Liu, H. B. (2018). Evaluation of the implementation effect of regional science and technology innovation policies. Central South University of Forestry and Technology.
   Google Scholar
• Liu, X. L. (1993). The economics of technological innovation. China Economic Press.
   Google Scholar
• OECD. (1982). Eutrophication of waters: Monitoring, assessment and control paperback.
   Google Scholar
• Pustovrh, A., & Marko, J. (2014). National innovation policies in the EU: A fuzzy-set analysis. Economic and Business Review, 16(1), 39–62. https://doi.org/10.15458/2335-4216.1179
   Google Scholar
• Radicic, D., & Pugh, G. (2016). R&D programmes, policy mix, and the “European paradox”: Evidence from European SMEs. Science & Public Policy, 44(4), 497–512. https://doi.org/10.1093/scipol/scw077
   Web of Science ®Google Scholar
• Rogge, K. S., & Reichardt, K. (2016). Policy mixes for sustainability transitions: An extended concept and framework for analysis. Research Policy, 45(8), 1620–1635. https://doi.org/10.1016/j.respol.2016.04.004
   Web of Science ®Google Scholar
• Rothwell, R. (1986). Public innovation policy:To have or to have not. R&DManagement, 16(1), 34–63. https://doi.org/10.1111/j.1467-9310.1986.tb01154
   Google Scholar
• Su, Z. F., & Zhang, X. X. (2020). The impact of government innovation subsidies on firms’ R&D investment from the perspective of policy “target-instrument”. Science & Technology Progress and Policy, 37(19), 133–140. https://doi.org/10.6049/kjjbydc.2019120028
   Google Scholar
• Wang, B., & Zhang, N. M. (2018). Exploration on the path of fiscal policy promoting scientific and technological innovation under the innovation-driven orientation. Social Sciences in Yunnan, (1), 57–63. https://doi.org/10.26549/jxffcxysj.v4i8.7151
   Google Scholar
• Wang, C. H., Lu, Y. H., Huang, C. W. (2013). R&D, productivity, and market value: An empirical study from high-technology firms. Omega, 41(1), 143–155. https://doi.org/10.1016/j.omega.2011.12.011
   Web of Science ®Google Scholar
• Wang, M., Yi, T. S. Y., & Li, Z. R. (2017). Levels, types and enterprise innovation of science and technology innovation policy: An empirical analysis based on survey data. Science and Science and Technology Management, 38(11), 20–30. CNKI:SUN:KXXG.0.2017-11-003
   Google Scholar
• Wang, Y. W. (2020). Authoritative response, resource integration and external pressure neutralization: The logic of choosing policy tools in neighborhood resistance governance – A qualitative comparative analysis based on (fsQCA) fuzzy sets. Journal of Public Management, 17(2), 27–39. https://doi.org/10.16149/j.cnki.23-1523.20200326.002
   Google Scholar
• Woodside, A. G. (2013). Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory. Journal of Business Research, 66(4), 463–472. https://doi.org/10.1016/j.jbusres.2012.12.021
   Web of Science ®Google Scholar
• Wu, J. M. (2022). Development trend of major science and technology parks in the world. Science and Technology Think Tank, (9), 1–3. https://doi.org/10.19881/j.cnki.1006-3676.2022.09.01
   Google Scholar
• Wu, M., Liu, C., & Huang, J. L. (2021). The technological innovation effect of development zone policies: Evidence from patent data. Economics (Quarterly), 21(5), 1817–1838. https://doi.org/10.13821/j.cnki.ceq.2021.05.15
   Google Scholar
• Xia, D. (2003). Ownership structure and enterprise innovation efficiency. Nankai Management Review, (3), 32–36. https://doi.org/10.37155/2717-5642-0102-2
   Google Scholar
• Yan, H. Y. (2022). Comparative study on innovation capability of ordinary universities in central and eastern China. Higher Science Education, 03(3), 40–50. https://doi.org/10.17265/1537-1506/2006.11.010
   Google Scholar
• Yan, L. G., & Zeng, X. M. (2020). The explanation of why it is difficult to transfer – To the Midwest is based on the spatial differences in human capital. Economic Geography, 40(1), 125–131. CNKI:SUN:JJDL.0.2020-01-014
   Google Scholar
• Yang, Q. K., & Duan, X. J. (2014). A study on the spatial and temporal measurement of high-tech industry development efficiency and inter-provincial differences based on DEA-Malmquist. Economic Geography, 34(7), 103–110. CNKI:SUN:JJDL.0.2014-07-016
   Google Scholar
• Yong, L., Mezei, J., Kostakos, V. (2017). Applying configurational analysis to IS behavioural research: A methodological alternative for modelling combinatorial complexities. Information Systems Journal, 27(1), 59–89. https://doi.org/10.1111/isj.12094
   Web of Science ®Google Scholar
• Yu, P., & Cheng, Y. (2016). Measurement of innovation efficiency in China’s national high-tech parks and regional comparison study – Based on the perspective of innovation value chain. Contemporary Finance & Economics, (12), 3–15. CNKI:SUN:DDCJ.0.2016-12-001
   Google Scholar
• Zeng, W. J., Li, Q. H., & Cai, C. G. (2020). Research on innovation efficiency of high-tech industrial development zones in China and its influencing factors. Soft Science, 34(5), 6–11. CNKI:SUN:XUXI.0.2020-05-004
   Google Scholar
• Zhang, J. L., & Li, L. L. (2014). Research on the relationship between R & D funding differences and enterprise technological innovation stage. Studies in Science of Science, 32(11), 1740–1746. https://doi.org/10.3969/j.issn.1003-2053.2014.11.018
   Google Scholar
• Zhang, J. X., & Li, X. L. (2019). Research on the impact of enterprise type differences on technical efficiency in national high-tech zones. Reform of Economic System, (3), 46–53. CNKI:SUN:JJTG.0.2019-03-008
   Google Scholar
• Zhang, W. G. (2019). Research on the path of improving research productivity in China's universities–a qualitative comparative analysis of fuzzy sets based on 31 provinces. China Higher Education Research, (7), 78–84. https://doi.org/10.16298/j.cnki.1004-3667.2019.07.12
   Google Scholar