Tzu-Yu Lin
ABSTRACT
Innovative development has always been an important policy tool for accumulating effective intelligent property to the desirability of all policy makers in China, even more so now as the trade war has seriously threatened the Chinese economy. To understand the efficacy of regional innovative policies, we proposed the dynamic two-stage slacks-based measure (SBM) model with carry-over and intermediate variables, highlighting the importance of the status of invention patent, as granted patent and patent in force, to measure the overall innovative performance for the purposed of regional innovative development, which makes significant difference to previous studies on modelling setting. Using data of 30 provincial administration regions in China for the period of 2011-2017, the average regional innovative system performance is deemed as 0.5858 and we postulated that the difference of commercialization performance among three main areas should be pay attention, because the average performance of commercialization stage in the east area is obviously better than that the west and central areas. Based on this finding, we propose several policy suggestions as the provincial and central government who should be put more efforts on innovative competitiveness, as enhancing the quality and quantity of invention patents and the market-oriented to guild the direction for the R&D stage.
- Lin, T.Y., Chiu, S.H. 2018. Sustainable performance of low-carbon energy infrastructure investment on regional development: Evidence from China. Sustainability 10(12), 4657. https://doi.org/10.3390/su10124657Google Scholar
Cross Ref
- Tone, K. 2001. A slacks-based measure of efficiency in data envelopment analysis. Eur. J. Oper. Res. 130, 498--509. https://doi.org/10.1016/S0377-2217(99)00407-5Google ScholarCross Ref
- Cook, W.D., Liang, L., Zhu, J. 2010. Measuring performance of two-stage network structure by DEA: A review and future perspective. Omega 38, 423--430. https://doi.org/10.1016/j.omega.2009.12.001Google ScholarCross Ref
- Galagedera, D.U.A., Roshdi, I., Fukuyama, H., Zhu, J. 2018. A new network DEA model for mutual fund performance appraisal: An application to U.S. equity mutual funds. Omega 77, 168--179. Http://doi.org/10.1016/j.omega.2017.06.006Google ScholarCross Ref
- Iftikhar, Y., Wang, Z., Zhang, B., Wang, B. 2018. Energy and CO2 emissions efficiency of major economies: A network DEA approach. Energy 147, 197--207. Http://doi.org/10.1016/j.energy.2018.01.012Google ScholarCross Ref
- Tone, K., Tsutsui, M. 2009. Network DEA: A slacks-based measure approach. Eur. J. Oper. Res. 197(1), 243--252. http://dx.doi.org/10.1016/j.ejor.2008.05.027Google ScholarCross Ref
- Tone, K., Tsutsui, M. 2014. Dynamic DEA with network structure: A slacks-based measure. Omega 42. 124--131. https://doi.org/10.1016/j.omega.2013.04.002Google Scholar
- Seiford, L.M., Thrall, R.M. 1990. Recent developments in DEA: the mathematic programming approach to frontier analysis. J. Econom. 46, 7-38. https://doi.org/10.1016/0304-4076(90)90045-UGoogle ScholarCross Ref
- Zhang, B., Luo, Y., Chiu, Y.H. 2019. Efficiency evaluation of China's high-tech industry with multi-activity network data envelopment analysis approach. Socio-Econ Plan Sci 66, 2--9. https://doi.org/10.1016/j.seps.2018.07.013Google ScholarCross Ref
- Zhong, W., Yuan, W., Li, S.X., Huang, Z. 2011. The performance evaluation of regional R&D investment in China: An application of DEA based on the first official China economic census data. Omega 39, 447--455. https://doi.org/10.1016/j.omega.2010.09.004Google ScholarCross Ref
- Li, L.B., Liu, B.L., Liu, W.L., Chiu, Y.H. 2017. Efficiency evaluation of the regional high-tech industry in China: A new framework based on meta-frontier dynamic DEA analysis. Socio-Econ Plan Sci 60, 24--33. https://doi.org/10.1016/j.seps.2017.02.001Google ScholarCross Ref
- Xiong, X., Yang, G.L., Guan, Z.C. 2018. Assessing R&D efficiency using a two-stage dynamic DEA model: A case study of research institutes in the Chinese Academy of Sciences. J. Informetr. 12, 784--805. https://doi.org/10.1016/j.joi.2018.07.003Google ScholarCross Ref
- Lu, W.M., Kweh, Q.L., Huang, C.L. 2014. Intellectual capital and national innovation systems performance. Knowledge-Based Syst. 71, 201--210. https://doi.org/10.1016/j.knosys.2014.08.001Google ScholarDigital Library
- Lu, W.M., Kweh, Q.L., Nourani, M., Huang, F.W. 2016. Evaluating the efficiency of dual-use technology development programs from the R&D and socio-economic perspectives. Omega 62, 82--92. https://doi.org/10.1016/j.omega.2015.08.011Google ScholarCross Ref
- Kao, C. 2013. Dynamic data envelopment analysis: a relational analysis. Eur. J. Oper. Res. 227, 325--330.Google ScholarCross Ref
- Li, Y., Shi, X., Yang, M., Liang, I. 2017. Variables selection in data envelopment analysis via Akaike's information. Ann. Oper. Res. 253, 453--476. https://doi.org/10.1007/s10479-016-2382-2Google ScholarCross Ref
- Tone, K., Kweh, Q.L., Lu, W.M., Ting, I.W.K. 2019. Modeling investments in the dynamic network performance of insurance companies. Omega 88, 237--247. https://doi.org/10.1016/j.omega.2018.09.005Google ScholarCross Ref
- Golany, B., Roll, Y. 1989. An application procedure for DEA. Omega 17, 237--250. https://doi.org/10.1016/0305-0483(89)90029-7Google ScholarCross Ref
- Cooper, W.W., Li, S., Seiford, L., Tone, K., Thrall, R.M., Zhue, J. 2001. Sensitivity and stability analysis in DEA: Some recent development. J. Prod. Anal. 15, 217--246. https://doi.org/10.1023/A:1011128409257Google ScholarCross Ref
- Tone, K., Tsutsui, M. 2010. Dynamic DEA: A slacks-based measure. Omega 38, 145--156. https://doi.org/10.1016/j.omega.2009.07.003Google ScholarCross Ref
Index Terms
- Modeling Regional Innovative System Performance in China Using A Dynamic Two-Stage SBM Model
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