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.
References
[1]
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
[2]
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
[3]
Liu, C. (2012) Researches on the R&D Innovation Efficiency for China High-Tech Industry. Industrial Technology Economy, No. 12, 19-25
[4]
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.
[5]
Parkan, C. and Wang, Y.M. (2000) Worst Efficiency Analysis Based on Inefficient Production Frontier. Information Sciences, 175, 20-29.
[6]
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
[7]
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
[8]
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
[9]
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
[10]
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
[11]
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
[12]
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
[13]
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.
[14]
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.
