Optimal path selection of innovation resource allocation in China’s regions with shared inputs

References

• Bai, J., & Bian, Y. (2016). Factor market distortion and the efficiency losses of Chinese innovative production. China Industrial Economics, 1(11), 39–55. https://doi.org/10.19581/j.cnki.ciejournal.2016.11.005
   Google Scholar
• Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
   Google Scholar
• Barasa, L., Knoben, J., Vermeulen, P., Kimuyu, P., & Kinyanjui, B. (2017). Institutions, resources and innovation in East Africa: A firm level approach. Research Policy, 46(1), 280–291. https://doi.org/10.1016/j.respol.2016.11.008
   Web of Science ®Google Scholar
• Broekel, T. (2015). Do cooperative research and development subsidies stimulate regional innovation efficiency? Evidence from Germany. Regional Studies, 49(7), 1087–1110. https://doi.org/10.1080/00343404.2013.812781
   Web of Science ®Google Scholar
• Chen, K., & Guan, J. (2012). Measuring the efficiency of China’s regional innovation systems: Application of network data envelopment analysis (DEA). Regional Studies, 46(3), 355–377. https://doi.org/10.1080/00343404.2010.497479
   Web of Science ®Google Scholar
• Cruz-Cázares, C., Bayona-Sáez, C., & García-Marco, T. (2013). You can’t manage right what you can’t measure well: Technological innovation efficiency. Research Policy, 42(6–7), 1239–1250. https://doi.org/10.1016/j.respol.2013.03.012
   Web of Science ®Google Scholar
• Färe, R., Grosskopf, S., & Pasurka, C. A. (2007). Environmental production functions and environmental directional distance functions. Energy, 32(7), 1055–1066. https://doi.org/10.1016/j.energy.2006.09.005
   Web of Science ®Google Scholar
• Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54(2), 393–420. https://doi.org/10.5465/amj.2011.60263120
   Web of Science ®Google Scholar
• Fu, L. N., Peng, J. C., & Yi, M. (2020). A study on regional innovation resource allocation efficiency based on meta-frontier production function. Macroeconomics, 1(4), 85–102. https://doi.org/10.16304/j.cnki.11-3952/f.2020.04.009
   Google Scholar
• Furman, J. L., Porter, M. E., & Stern, S. (2002). The determinants of national innovative capacity. Research Policy, 31(6), 899–933. https://doi.org/10.1016/S0048-7333(01)00152-4
   Web of Science ®Google Scholar
• Goto, A., & Suzuki, K. (1989). R&D capital, rate of return on R&D investment and spillover of R&D in Japanese manufacturing industries. The Review of Economics and Statistics, 71(4), 555–564. https://doi.org/10.2307/1928096
   Web of Science ®Google Scholar
• Griliches, Z. (1980). R&D and the productivity slowdown. The American Economic Review, 70(2), 343–348. https://doi.org/10.2307/1815495
   Web of Science ®Google Scholar
• Guan, J., & Chen, K. (2010). Measuring the innovation production process: A cross-region empirical study of China’s high-tech innovations. Technovation, 30(5–6), 348–358. https://doi.org/10.1016/j.technovation.2010.02.001
   Web of Science ®Google Scholar
• Guan, J., & Chen, K. (2012). Modeling the relative efficiency of national innovation systems. Research Policy, 41(1), 102–115. https://doi.org/10.1016/j.respol.2011.07.001
   Web of Science ®Google Scholar
• Jayanthi, S., Witt, E. C., & Singh, V. (2009). Evaluation of potential of innovations: A DEA-based application to U.S. photovoltaic industry. IEEE Transactions on Engineering Management, 56(3), 478–493. https://doi.org/10.1109/TEM.2009.2013833
   Web of Science ®Google Scholar
• Kao, C., & Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418–429. https://doi.org/10.1016/j.ejor.2006.11.041
   Web of Science ®Google Scholar
• Klingebiel, R., & Rammer, C. (2014). Resource allocation strategy for innovation portfolio management. Strategic Management Journal, 35(2), 246–268. https://doi.org/10.1002/smj.2107
   Web of Science ®Google Scholar
• Kogan, L., Papanikolaou, D., Seru, A., & Stoffman, N. (2017). Technological innovation, resource allocation and growth. The Quarterly Journal of Economics, 132(2), 665–712. https://doi.org/10.1093/qje/qjw040
   Web of Science ®Google Scholar
• Lewis, H. F., & Sexton, T. R. (2004). Network DEA: Efficiency analysis of organizations with complex internal structure. Computers & Operations Research, 31(9), 1365–1410. https://doi.org/10.1016/S0305-0548(03)00095-9
   Web of Science ®Google Scholar
• Li, X. (2014). Research on the regional scientific innovation based on the innovation network. Scientific Management Research, 1(6), 52–55. https://doi.org/10.19445/j.cnki.15-1103/g3.2014.06.014
   Google Scholar
• Li, M., & Chu, Y. (2017). Explore the research front of a specific research theme based on a novel technique of enhanced co-word analysis. Journal of Information Science, 43(6), 725–741. https://doi.org/10.1177/0165551516661914
   Web of Science ®Google Scholar
• Liu, C. C. (2011). A study for allocating resources to research and development programs by integrated fuzzy DEA and fuzzy AHP. Scientific Research and Essays, 6(19), 3973–3978. https://doi.org/10.5897/SRE10.838
   Google Scholar
• McElwee, G., & Atherton, A. (2005). Publication trends and patterns in entrepreneurship: The case of the international journal of entrepreneurship and innovation. Journal of Small Business and Enterprise Development, 12(1), 92–103. https://doi.org/10.1108/14626000510579662
   Google Scholar
• Mei, S., & Chen, W. J. (2015). Research on allocation efficiency of S&T resource and the influential factors of sub-provincial cities. Science and Technology Management Research, 1(6), 64–68.
   Google Scholar
• Ngamassi, L., Maitland, C., & Tapia, A. H. (2014). Humanitarian interorganizational information exchange network: how do clique structures impact network effectiveness. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 25(6), 1483–1508. https://doi.org/10.1007/s11266-013-9403-4
   Web of Science ®Google Scholar
• Qi, Y., & Guo, Y. (2015). Evaluation on market allocation efficiency of science and technology resources based on SFA model. Science Research Management, 36(3), 84–91. https://doi.org/10.19571/j.cnki.1000-2995.2015.03.011
   Google Scholar
• Ragin, C. C. (2006). Set relations in social research: evaluating their consistency and coverage. Political Analysis, 14(3), 291–310. https://doi.org/10.1093/pan/mpj019
   Web of Science ®Google Scholar
• Ragin, C. C. (2008). Redesigning social inquiry: Fuzzy Sets and Beyond. University of Chicago Press. https://doi.org/10.7208/chicago/9780226702797.001.0001
   Google Scholar
• Ren, S. G., Hu, C. Y., & Wang, L. W. (2011). Empirical study of the influence of China's regional innovation network structural characteristics on regional innovation capability. Systems Engineering, 2(1), 50–55.
   Google Scholar
• Rihoux, B., & Ragin, C. C. (2009). Configurational comparative methods: Qualitative comparative analysis (QCA) and related techniques. Sage Publications. https://doi.org/10.4135/9781452226569
   Google Scholar
• Samimi, P., Teimouri, Y., & Mukhtar, M. (2016). A combinatorial double auction resource allocation model in cloud computing. Information Sciences, 357(357), 201–216. https://doi.org/10.1016/j.ins.2014.02.008
   Web of Science ®Google Scholar
• Schilling, M. A., & Phelps, C. C. (2007). Interfirm collaboration networks: the impact of large-scale network structure on firm innovation. Management Science, 53(7), 1113–1126. https://doi.org/10.1287/mnsc.1060.0624
   Web of Science ®Google Scholar
• Schneider, C. Q., & Wagemann, C. (2012). Set-theoretic methods for the social sciences: A guide to qualitative comparative analysis. Cambridge University Press. https://doi.org/10.1080/13645579.2013.762611
   Google Scholar
• Shao, H., Zhou, L., & Liu, Y. (2018). Study on the spatial network structure and driving factors of regional innovation development in China. Studies in Science of Science, 36(11), 2055–2069. https://doi.org/10.16192/j.cnki.1003-2053.2018.11.016
   Google Scholar
• Tan, Q. (2004). On the efficient allocation of regional innovation resources. Studies in Science of Science, 1(5), 543–545. https://doi.org/10.16192/j.cnki.1003-2053.2004.05.018
   Google Scholar
• Tao, Q., Li, R., & Wang, Y. (2016). Effects on innovation performance by combinations of innovation network features and environmental turbulence. Science & Technology Progress and Policy, 33(18), 19–27. https://doi.org/10.6049/kjjbydc.2016020263
   Google Scholar
• Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243–252. https://doi.org/10.1016/j.ejor.2008.05.027
   Web of Science ®Google Scholar
• Wang, Q., Hang, Y., Sun, L., & Zhao, Z. (2016). Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach. Technological Forecasting and Social Change, 112, 254–261. https://doi.org/10.1016/j.techfore.2016.04.019
   Web of Science ®Google Scholar
• Wang, X., & Wang, H. (2008). Pattern selection and structural equation modeling of regional science and technology innovation resource allocation system. Technology Economics, 27(12), 36–42. https://doi.org/10.3969/j.issn.1002-980X.2008.12.007
   Google Scholar
• Wang, Z., Wang, Q., Zhao, X., Lyles, M. A., & Zhu, G. (2016). Interactive effects of external knowledge sources and internal resources on the innovation capability of Chinese manufacturers. Industrial Management & Data Systems, 116(8), 1617–1635. https://doi.org/10.1108/IMDS-10-2015-0412
   Web of Science ®Google Scholar
• Xiang, X. D., & Chen, L. F. (2016). Research on technology innovation efficiency evaluation model of manufacturing industries and its application. Operations Research and Management Science, 25(1), 215–223.
   Google Scholar
• Xiao, R. Q., Qian, L., & Chen, Z. W. (2012). Research on the innovation efficiency and its affecting factors in China’s high-tech industries. Journal of Management Science, 25(5), 85–98.
   Google Scholar
• Yin, X., Bao, X., & Meng, J. (2020). Evaluation and optimization path of scientific and technological resources allocation efficiency of advanced precise-top industries. Science & Technology for Development, 15(10), 1075–1085. https://doi.org/10.11842/chips.20190603001
   Google Scholar
• Yu, M., Guo, P., & Zhang, G. (2013). Impact of the regional innovation network structure on regional innovation efficiency. Science of Science and Management of S. & T, 3(8), 56–63.
   Google Scholar
• Zhang, X. M., Wang, Y. M., & Shi, H. L. (2018). A study of the optimal allocation of key resources in innovative enterprises from the perspective of efficiency. Science Research Management, 1(5), 103–111. https://doi.org/10.19571/j.cnki.1000-2995.2018.05.012
   Google Scholar
• Zhang, L. (2019). Research on efficiency of science and technology resources allocation in regional business incubation networks [Ph.D. Dissertation]. Shanxi University of Finance & Economics. https://tra.oversea.cnki.net/KCMS/detail/detail.aspx?dbname=CDFDLAST2019&filename=1019120985.nh
   Google Scholar
• Zhao, W., & Wang, N. (2017). Research on performance improvement path of Chinese returnees enterprises considering ambidextrous network: The fuzzy-set qualitative comparative analysis. Science of Science and Management of S. & T., 38(5), 128–139.
   Google Scholar
• Zhou, J. Z., Hu, Z. J., & Zhou, Y. (2002). Allocating S&T resources in national systems of innovation. Journal of Management Sciences in China, 5(3), 40–49.
   Google Scholar
• Zhu, P., & Xu, W. (2003). On the impact of government's S&T incentive policy on the R&D input and its patent output of large and medium-sized industrial enterprises in shanghai. Economic Research Journal, 1(6), 45–53.
   Google Scholar