The characteristics of
Chinese economic area are distinct, and the development status of the inter-provincial
high-tech industry is significantly different. As Chinese high-tech industry is
acting an increasingly important role in pushing the economic development, it
is necessary to evaluate the inter-provincial R&D innovation efficiency for
high-tech industry. This text, using the DEA Approach with Double Frontiers,
made comparison for the evaluation unit with the “optimized” and “worst”
decision-making unit respectively. Therefore it carried out measurement for the
inter-provincial optimistic efficiency and pessimistic efficiency for Chinese
high-tech industry, and suggested the comprehensive results with the geometric
average efficiency. The research results showed that there were great
differences among the R&D innovation efficiencies for Chinese high-tech
industry among 26 provinces and municipalities. There is no need for the high-tech
industry to blossom everywhere. It, based on the economic development and
efficiency status, should be strengthened in Beijing, Tianjin, Guangdong,
Chongqing and Jiangsu, etc.
Zhu, Y.W. and Xu, K.N. (2006) Empirical Researches on the R&D Innovation Efficiency for the China High-Tech Industry. Chinese Industrial Economy, No. 11, 38-45
Guan, J.C. and Chen, K.H. (2009) The Measurement for the Technological Innovation for China High-Tech Industry. Quantity Economy Technology Economic Researches, No. 10, 19 -33
Gui, H.B. (2014) Economic Econometrics Analysis on the Innovation Efficiency and the Influencing Factors for China High-Tech Industry. Economic Geography, No. 6, 100-107.
Entani, T., Maeda, Y. and Tanaka, H. (2002) Dual Models of Interval DEA and Its Extension to Interval Data. European Journal of Operational Research, 136, 32-45. http://dx.doi.org/10.1016/S0377-2217(01)00055-8
Wang, Y.M. and Chin, K.S. (2009) A New Approach for the Selection of Advanced Manufacturing Technologies DEA with Double Frontiers. International Journal of Production Research, 47, 6663-6679.
http://dx.doi.org/10.1080/00207540802314845
Wang, Y.M. and Lan, Y.-X. (2011) Measuring Malmquist Productivity Index: A New Approach Based on Double Frontiers Data Envelopment Analysis. Mathematical and Computer Modelling, 54, 2760-2771.
http://dx.doi.org/10.1016/j.mcm.2011.06.064
Charnes, A., Cooper, W.W. and Rhodes, E. (1978) Measuring the Efficiency of Decision Making Units. European Journal of Operational Research, 2, 429-444. http://dx.doi.org/10.1016/0377-2217(78)90138-8
Wang, Y.M., Chin, K.S. and Yang, J.B. (2007) Measuring the Performances of Decision Making Units Using Geometric Average Efficiency. Journal of the Operational Research Society, 58, 929-937.
http://dx.doi.org/10.1057/palgrave.jors.2602205
Charnes, A., and Cooper, W.W. (1962) Programming with Linear Fractional Functionals. Naval Research Logistics Quarterly, 9, 181-186. http://dx.doi.org/10.1002/nav.3800090303
Sharma, S. and Thomas, V.J. (2008) Inter-Country R&D Efficiency Analysis: Application of Data Envelopment Analysis. Scientometrics, 76, 483-501. http://dx.doi.org/10.1007/s11192-007-1896-4
Bai, J.H., Jiang, K.S. and Li, J. (2009) Comprehensive Evaluation and Analysis on the Innovation Efficiency of the Regional Innovation System in China. Management Review, No. 9, 3-9.
Cooper, W.W., Seiford, L.M., Tone, K. (2007) Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software. 2nd Edition, Springer, New York.
