A Study of High-Tech Firms’ Innovation Performance and its Determinants

Abstract

Abstract: Using new product sales as indicator of enterprise innovation performance, and taking technical efficiency, enterprise size, government policy, R&D investment etc. as key determinants, this paper established a research model by means of the quantile regression method. Based on the 2008-2010 panel data from the enterprises in Shanghai Zhangjiang Hi-Tech Park, this paper also added the ownerships and other dummy variables as control variables, and investigates empirically how these factors impact the hi-tech enterprises at different quantile performance points and compares them with the OLS regression results. Based on such results, the paper draws conclusions and proposes policies to enhance the hi-tech enterprises' innovation performance in China.

Key words: Innovation performance, quantile regression, high-tech firms

CLC Number:

F062.4

Zhang Yuchen, Lv Xianpeng. A Study of High-Tech Firms’ Innovation Performance and its Determinants[J]. Science Research Management, 2013, 34(12): 58-65.

References

[1] Romer P. Endogenous technological change[J]. Journal of Political Economy, 1990, 98(5):71-102.
[2] Nathan Rosenberg. Science, invention and economic growth[J]. The Economic Journal, 1974, 84(333):90-108.
[3] Richard R Nelson. National innovation systems: a comparative analysis[M]. Oxford University Press, New York, 1993.
[4] Freeman, C., Soete, L. The Economics of Industrial Innovation[M]. The MIT Press, London, 1997.
[5] Hagedoorn, J., Cloodt, M. Measuring innovative performance: is there an advantage in using multiple indicators?[J]. Research Policy, 2003, 32(8):1365-1379.
[6] Ernst, H. Patent applications and subsequent changes of performance: evidence from time-series cross-section analyses on the firm level[J]. Research Policy, 2001, 30(1): 143-157.
[7] Xiaohui, Liu & Trevor, Buck. Innovation performance and channels for international technology spillovers: evidence from Chinese high-tech industries[J]. Research Policy, 2007, 36(3):355-366.
[8] Duysters, G., Hagedoorn, J. Do company strategies and structures converge in global markets? Evidence from the computer industry[J]. Journal of International Business Studies, 2001, 32 (2): 347-356.
[9] Archibugi, D. Patenting as an indicator of technological innovation: a review[J]. Science and Public Policy, 1992, 19 (6):357-358.
[10] Dosi, G. Sources, procedures, and microeconomic effects of innovation[J]. Journal of Economic Literature, 1988, 26(3): 1120–1171.
[11] 张维迎, 周黎安, 顾全林.高新技术企业的成长及其影响因素:分位回归模型的一个应用[J].管理世界, 2005(10): 94-112.
[12] Battese, George, E., Coelli, Tim. A model of technical inefficiency effects in stochastic frontier production for panel data[J]. Empirical Economics, 1995, 20(2):325-332.
[13] Tsai, K., H., Wang. J.C. Does R&D performance decline with firm size?-a re-examination in terms of elasticity[J]. Research Policy, 2005, 34(6):966-976.
[14] Hu Albert. G. Ownership, government R&D, private R&D, and productivity in Chinese industry[J]. Journal of Comparative Economics, 2001, 29(1):136-157.
[15] 王俊.政府R&D资助与企业R&D投入的产出效率比较[J]. 数量经济技术经济研究, 2011(6):93-105.
[16] 朱平芳, 徐伟民.政府的科技激励政策对大中型工业企业R&D投入及其专利产出的影响——上海市的实证研究[J]. 经济研究, 2003(6):45-53.
[17] Mosteller, F., Tukey, J. Data Analysis and Regression.[M]. Addison-Wesley, Reading, MA., 1977.
[18] Yasar, M., Nelson, C.H., Rejesus, R.M. Productivity and exporting status of manufacturing firms: evidence from quantile regression.[J]. Review of World Economics, 2006, 142(4), 675-694.
[19] Koenker, R., Hallock, K. Quantile regression[J]. Journal of Economic Perspectives, 2001, 15(4): 143-156.
[20] Koenker, R., Bassett, G. Regression quantiles[J]. Econometrica, 1978, 46(1):33-50.
[21] Coad, A. & Rao, R. Innovation and firm growth in high-tech sectors: a quantile regression approach[J]. Research Policy, 2008, 37(4):633-634.
[22] Coad, A. & Rao, R. Innovation and market value: a quantile regression analysis[J]. Economics Bulletin, 2006, 15(13):1-1.