Innovation Capability and Innovation Talents: Evidence from China Based on a Quantile Regression Approach
Abstract
:1. Introduction
2. Theory Foundation and Research Hypotheses
3. Materials and Methods
3.1. Index Selection
3.2. Samples and Data
3.3. Method and Model
3.3.1. Global Spatial Autocorrelation
3.3.2. Quantile Regression
4. Quantile Analysis
4.1. Sample Description
4.1.1. Global Spatial Autocorrelation Analysis
4.1.2. Description of Distribution Characteristics
4.2. Data Analysis and Test
4.2.1. Correlation Analysis
4.2.2. Unit Root Test
4.3. Parameter Estimation and Model Test
4.3.1. Parameter Estimation
4.3.2. Model Test
4.4. Conditional Density Forecast
5. Concluding Remarks
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Wei, S.H.; Gui-Sheng, W.U.; Xin-Lei, L.V. The determinants of regional innovation capability—Comment on the regional dap of innovation capability in China. China Soft Sci. 2010, 46, 76–85. [Google Scholar]
- Peilei, F.; Guanghua, W. China’s regional inequality in innovation capability, 1995–2006. China World Econ. 2012, 23, 16–36. [Google Scholar]
- Cao, Y. Measurable analysis on the difference changes of regional innovation capability based on the dynamic performance in China. China Popul. Res. Environ. 2012, 22, 164–169. [Google Scholar]
- China Technology Development Strategies Research Group. China Regional Innovation Evaluation Report; Scientific and Technical Documentation Press: Beijing, China, 2015. [Google Scholar]
- Xu, Q. Total Innovation Management: Theory and Practice, 1st ed.; Science Press: Beijing, China, 2007. [Google Scholar]
- Doloreux, D.; Dionne, S. Is regional innovation system development possible in peripheral regions? Some evidence from the case of la Pocatière, Canada. Entrep. Reg. Dev. 2008, 20, 259–283. [Google Scholar] [CrossRef]
- Balykhin, G.A.; Balykhin, M.G. Innovative person as a way and method of achieving global competitive educational advantage of Russia. Int. J. Exp. 2014, 11, 173–176. (In Russian) [Google Scholar]
- Zollo, G.; Autorino, G.; De Crescenzo, E.; Iandoli, L.; Imperiale, E.; Liguori, V.; Ponsiglione, C. A gap analysis of regional innovation systems (RIS) with medium-low innovative capabilities: The case of campania region (Italy). In 8th ESU Conference on Entrepreneurship (2011); Universidad de Sevilla: Sevilla, Spain, 2011; pp. 1–21. [Google Scholar]
- Woo, Y.; Kim, E.; Lim, J. The impact of education and R&D investment on regional economic growth. Sustainability 2017, 9, 1–18. [Google Scholar]
- Guilford, J.P. Traits of creativity. Creat. Cultiv. 1959, 10, 141–161. [Google Scholar]
- Glover, C.; Smethurst, S. Creative license. People Manag. 2003, 9, 30–37. [Google Scholar]
- Van Dam, N. Innovative talent management strategies. Chief Learn. Off. 2005, 4, 13–19. [Google Scholar]
- Franses, P.H. Merging models and experts. Int. J. Forecast. 2008, 24, 31–33. [Google Scholar] [CrossRef]
- Pruis, E. The five key principles for talent development. Ind. Commer. Train. 2011, 43, 206–217. [Google Scholar] [CrossRef]
- Jiang, D.K.; Liu, X.M.; Zhu, H.C. Factors influencing talent flow of open innovation. Technol. Innov. Manag. 2016, 37, 406–416. (In Chinese) [Google Scholar]
- Carr, S.C.; Inkson, K.; Thorn, K. From global careers to talent flow: Reinterpreting ‘brain drain’. J. World Bus. 2005, 40, 386–398. [Google Scholar] [CrossRef]
- Zhao, S.L.; Zhu, D.Y.; Peng, X.B.; Song, W. An empirical analysis of the regional competitiveness based on S&T talents flow. Hum. Syst. Manag. 2016, 35, 1–10. [Google Scholar]
- Baird, K.; Wang, H. Employee empowerment: Extent of adoption and influential factors. Pers. Rev. 2010, 39, 574–599. [Google Scholar] [CrossRef]
- Kong, D.Y.; Zhang, X.Q. Research on the organizational commitment and motivation of talented personality. Commer. Res. 2013, 102–107. [Google Scholar] [CrossRef]
- Lin, W.; Zhang, X. Factors affecting the flow of knowledge-based talents: An empirical analysis on Chinese enterprises. Inf. Technol. J. 2013, 12, 4262–4266. [Google Scholar] [CrossRef]
- Niu, C. The talent accumulation effect and environment in Silicon valley and Zhongguancun: A comparative study. Chin. J. Manag. 2008, 5, 396–400. [Google Scholar]
- Cai, Y.Z. Measurement of national innovative capacities in the perspective of national innovation system: Theories, methods, data and Chinese practice. Seeking Truth 2012, 39, 42–50. [Google Scholar]
- European Commission. European Innovation Scoreboard (EIS) 2007: Comparative Analysis of Innovation Performance; EU: Brussels, Belgium, 2008. [Google Scholar]
- Klaus, S.; Xavier, S. The Global Competitiveness Report 2011–2012; World Economic Forum: Geneva, Switzerland, 2010. [Google Scholar]
- OECD. Oecd Science, Technology and Industry Scoreboard 2015; OECD Publishing: Paris, Frence, 2015. [Google Scholar]
- Group, R. Research of China innovation index. Stat. Res. 2014, 31, 24–28. [Google Scholar]
- Furman, J.L.; Hayes, R. Catching up or standing still?: National innovative productivity among ‘follower’ countries, 1978–1999. Res. Policy 2004, 33, 1329–1354. [Google Scholar] [CrossRef]
- Wen, T.; Cai, J.; Xie, L. Research on the Relationship among Urban Amenities, Talents and Urban Income in China: Structural Equation Model in Practice; Springer: Berlin, Germany, 2015; pp. 5048–5060. [Google Scholar]
- Gu, F.; Bi, W. Influence factors analysis of the sustainable development of scientific and technological personnel in Tibet. In The International Conference on Mechatronics, Electronic, Industrial and Control Engineering; Atlantis Press: Amsterdam, The Netherland, 2014; pp. 1307–1310. [Google Scholar]
- Batabyal, A.A.; Nijkamp, P. A multi-region model of economic growth with human capital and negative externalities in innovation. J. Evol. Econ. 2013, 23, 909–924. [Google Scholar] [CrossRef]
- Horowitz, I. Some aspects of the effects of the regional distribution of scientific talent on regional economic activity. Manag. Sci. 1966, 13, 217–232. [Google Scholar] [CrossRef]
- Fornahl, D.; Zellner, C.; Audretsch, D.B. The Role of Labour Mobility and Informal Networks for Knowledge Transfer; Springer: New York, NY, USA, 2005. [Google Scholar]
- Fleisher, B.M.; Li, H.; Zhao, M.Q. Human capital, economic growth, and regional inequality in China. J. Dev. Econ. 2010, 92, 215–231. [Google Scholar] [CrossRef]
- Simon, D.F.; Cao, C. Creating an innovative talent pool. China Bus. Rev. 2009, 36, 34–42. [Google Scholar]
- Niu, C.H.; Wang, Y.N.; Yang, C.Y. Research of environment and effect of technological talent aggregation—Study on the impact’s of cultural environment upon the effect of technological talent aggregation. J. Taiyuan Univ. Technol. 2008, 26, 1–4. [Google Scholar]
- Zhou, H.; Dekker, R.; Kleinknecht, A. The impact of labour relations on innovative output: An exploration of firm-level data in The Netherlands. In The 5th International Symposium on Management of Technology, 2007; SSRN Electronic Journal: Rochester, NY, USA, 2007; pp. 433–436. [Google Scholar]
- Kirankabes, M.C.; Er Akar, M.E. Importance of relationship between R&D personnel and patent applications on economics growth: A panel data analysis. Int. Res. J. Financ. Econ. 2012, 92, 72–81. [Google Scholar]
- J.Hong, B.; Yue, W. Does the interregional flow of r&d elements promote the improvement of innovation efficiency. Forum Technol. Sci. China Forum Technol. Sci. China 2015, 27–32. [Google Scholar] [CrossRef]
- Ernst, H.; Leptien, C.; Vitt, J. Inventors are not alike: The distribution of patenting output among industrial R&D personnel. IEEE Trans. Eng. Manag. 2000, 47, 184–199. [Google Scholar]
- Louw, W.J.; Benade, S. The integration of talent management practices and computer aided engineering in new product development. Incose Int. Symp. 2015, 24, 177–190. [Google Scholar] [CrossRef]
- Chen, C.P.; Hu, J.L.; Yang, C.H. An international comparison of R&D efficiency of multiple innovative outputs: The role of the national innovation system. Innovation 2011, 13, 341–360. [Google Scholar]
- Asmild, M. Regional r&d efficiency in korea from static and dynamic perspectives. Reg. Stud. 2016, 50, 1–15. [Google Scholar]
- Hung, W.C. The determinants of national innovative capability-a cross-country innovation efficiency analysis. In 2009 Atlanta Conference on Science and Innovation Policy; IEEE: Atlanta, GA, USA, 2009; pp. 1–4. [Google Scholar]
- Li, X.; Wang, J.; Liu, X. Can locally-recruited R&D personnel significantly contribute to multinational subsidiary innovation in an emerging economy? Int. Bus. Rev. 2013, 22, 639–651. [Google Scholar]
- Lyu, Y.; Wu, Y.; Lin, Y. Granger causality test of science & technology innovation expense and new urbanization: a case ttudy of Hangzhou city. In Proceedings of the 20th International Symposium on Advancement of Construction Management and Real Estate; Springer: Singapore, 2017; pp. 1–10. [Google Scholar]
- Huang, S.; Bai, Y.; Tan, Q. How does the concentration of determinants affect industrial innovation performance?—An empirical analysis of 23 Chinese industrial sectors. PLoS ONE 2017, 12, 1–13. [Google Scholar] [CrossRef] [PubMed]
- Zhao, L.; Lei, Z.; Zhong, X.U. The measurement and evaluation of regional economic development disparities in Anshun experimental region from 2001 to 2008. World Reg. Stud. 2010, 19, 65–70. (In Chinese) [Google Scholar]
- Chen, Y.M.; Du, D.B.; Zhang, J.W. Study on causal association mechanism between regional innovation environment and Shanghai R&D industry. Soft Sci. 2011, 25, 124–130. [Google Scholar]
- Gunay, E.N.; Kazazoglu, G.N. National innovation efficiency during the global financial crisis period and afterwards. In National Innovation Efficiency During the Global Crisis: A Cross-Country Analysis; Palgrave Macmillan: New York, NY, USA, 2016; p. 198. [Google Scholar]
- Pandit, S.; Wasley, C.E.; Zach, T. The effect of R&D inputs and outputs on the relation between the uncertainty of future operating performance and R&D expenditures. Soc. Sci. Electron. Publ. 2009, 26, 121–144. [Google Scholar]
- Sougiannis, T. Discussion of “the effect of research and development (R&D) inputs and outputs on the relation between the uncertainty of future operating performance and R&D expenditures”. J. Account. Audit. Financ. 2011, 26, 145–149. [Google Scholar]
- Gao, W.; Chou, J. Innovation efficiency, global diversification, and firm value. J. Corp. Financ. 2015, 30, 278–298. [Google Scholar] [CrossRef]
- Koenker, R.; Bassett, G. Regression quantiles. Econometrica 1978, 46, 33–50. [Google Scholar] [CrossRef]
- Koenker, R. Quantile regression for longitudinal data. J. Multivar. Anal. 2004, 91, 74–89. [Google Scholar] [CrossRef]
- Chernozhukov, V.; Hansen, C. An iv model of quantile treatment effects. Econometrica 2005, 73, 245–261. [Google Scholar] [CrossRef]
- Chernozhukov, V.; Hansen, C. Instrumental quantile regression inference for structural and treatment effect models. J. Econ. 2006, 132, 491–525. [Google Scholar] [CrossRef]
- Chernozhukov, V.; Hansen, C. Instrumental variable quantile regression: A robust inference approach. J. Econ. 2008, 142, 379–398. [Google Scholar] [CrossRef]
- Harding, M.; Lamarche, C. A quantile regression approach for estimating panel data models using instrumental variables. Econ. Lett. 2009, 104, 133–135. [Google Scholar]
- Liu, K.; Yang, X.; Ling, Z. Evaluation on and optimization of science&technology innovation environment in liquor-making industry in Sichuan. Liquor-Mak. Sci. Technol. 2015, 127–131. [Google Scholar] [CrossRef]
- Xiao, Y.; Wang, W. Developing information economy to seize the commanding heights of global strategic competition. Bull. Chin. Acad. Sci. 2015, 16, 47–50. [Google Scholar]
- Zhang, T.Q.; Jiang, D.K. Research on regional high-level innovative talents competitiveness appraisal system. East China Econ. Manag. 2012, 26, 150–154. [Google Scholar]
- Zhu, L. Addressing extreme poverty and marginality: Experiences in rural China. Marginality 2014, 239–255. [Google Scholar] [CrossRef]
- Scott, A.J. New Industrial Spaces: Flexible Production Organization and Regional Development in North America and Western Europe; Pion: London, UK, 1988. [Google Scholar]
- Palivos, T.; Wang, P. Spatial agglomeration and endogenous growth. Reg. Sci. Urban Econ. 1996, 26, 645–669. [Google Scholar] [CrossRef]
- Rui, X.; Li, Y.; Niu, C. Influence of regional evolutionary trend of chinese technological talent accumulation to regional innovation efficiency. Forum Sci. Technol. China 2015, 126–131. (In Chinese) [Google Scholar] [CrossRef]
- Wu, R.Y. Hard to love you, dear west: Lost of scientific backtone, difficult in talent introduction. People’s Daily. 25 April 2014. Available online: http://news.163.com/14/0425/04/9QLBSVSQ00014Q4P.html (accessed on 21 April 2017). (In Chinese).
- Liu, X.; Buck, T. Innovation performance and channels for international technology spillovers: Evidence from chinese high-tech industries. Res. Policy 2007, 36, 355–366. [Google Scholar] [CrossRef]
- Chen, Q.; Yin, Z.; Ye, J. State-owned enterprises and regional innovation efficiency: From the externality perspective. Ind. Econ. Res. 2015, 10–20. (In Chinese) [Google Scholar] [CrossRef]
Variables | INET | INIT | INEY |
---|---|---|---|
INTL | 0.6260 | 0.5560 | 0.7850 |
(0.0000) | (0.0000) | (0.0000) |
Method | Levin, Lin & Chu | Im, Pesaran and Shin W-Stat | ADF-Fisher Chi-Square | PP-Fisher Chi-Square |
---|---|---|---|---|
INTL | 10.5355 | 14.0329 | 4.1648 | 4.7945 |
INET | 18.9895 | 21.7331 | 2.1655 | 1.3812 |
INIT | 2.0487 | 4,7220 | 43.6846 | 55.3185 |
INEY | 15.5310 | 16.8763 | 16.1484 | 4.2844 |
∆INTL | −11.9212 * | −9.1692 * | 196.4370 * | 237.9140 * |
∆INET | −7.1945 * | −2.7080 * | 88.7372 * | 85.5951 * |
∆INIT | −17.3404 * | −14.1380 * | 288.9580 * | 392.0390 * |
∆INEY | −8.8415 * | −6.7246 * | 161.6330 * | 208.7330 * |
tau | c | Ln(INET) | Ln(INIT) | Ln(INEY) | Prob(F-Statistic) | |
---|---|---|---|---|---|---|
0.1 | 8.3325 * | −0.7533 * | 0.8927 * | 0.9111 * | 0.5368 | 0.0000 |
0.2 | 10.8400 * | −0.9973 * | 0.9331 * | 0.9263 * | 0.4947 | 0.0000 |
0.3 | 11.9607 * | −1.0562 * | 0.8231 * | 0.9649 * | 0.4523 | 0.0000 |
0.4 | 9.3025 * | −0.6217 * | 0.7911 * | 0.7322 * | 0.4368 | 0.0000 |
0.5 | 6.8714 * | −0.2621 ** | 0.8208 * | 0.5225 * | 0.4422 | 0.0000 |
0.6 | 5.9127 * | −0.1347 | 0.9240 * | 0.3966 * | 0.4491 | 0.0000 |
0.7 | 6.0943 * | −0.1098 | 0.8691 * | 0.3856 * | 0.4554 | 0.0000 |
0.8 | 7.5333 * | −0.2554 * | 0.7718 * | 0.5144 * | 0.4658 | 0.0000 |
0.9 | 7.8521 * | −0.2925 * | 0.7171 * | 0.6155 * | 0.4982 | 0.0000 |
Hypotheses | H1 | H2 | H3 |
---|---|---|---|
Result | Reject | Accept | Accept |
Types of Test | Degree of Freedom | Prob | H0 | Conclusion | |
---|---|---|---|---|---|
Equality | 24 | 186.7004 | 0.0000 | Reject | Inequality |
Symmetry | 16 | 40.7394 | 0.0006 | Reject | Asymmetry |
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Wei, F.; Feng, N.; Zhang, K.H. Innovation Capability and Innovation Talents: Evidence from China Based on a Quantile Regression Approach. Sustainability 2017, 9, 1218. https://doi.org/10.3390/su9071218
Wei F, Feng N, Zhang KH. Innovation Capability and Innovation Talents: Evidence from China Based on a Quantile Regression Approach. Sustainability. 2017; 9(7):1218. https://doi.org/10.3390/su9071218
Chicago/Turabian StyleWei, Fenfen, Nanping Feng, and Kevin H. Zhang. 2017. "Innovation Capability and Innovation Talents: Evidence from China Based on a Quantile Regression Approach" Sustainability 9, no. 7: 1218. https://doi.org/10.3390/su9071218