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Reprint of "Performance evaluation of China's high-tech innovation process :Analysis based on the innovation value chain"

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  • Chen, Xiafei
  • Liu, Zhiying
  • Zhu, Qingyuan

Abstract

The Chinese high-tech industry has developed greatly since the beginning of China's “National High-tech R&D (863) Program” and “China Torch Program”. This paper introduces a conceptual model extended from the innovation value chain model to simultaneously estimate the R&D and commercialization efficiencies for the high-tech industries of 29 provincial-level regions in China. To match reality, a network DEA incorporating both shared inputs and additional intermediate inputs is constructed to open the “black box” view of decision making units used in single-stage DEA. This study is the first attempt to link the R&D and commercialization with a solid theoretical foundation and feasible mathematical methods. The empirical findings show that most of the 29 regions have low efficiency in the commercialization sub-process compared to the R&D sub-process, although there are regional differences in China's high-tech industry. Pearson correlation shows that the R&D sub-process is not closely correlated to the commercialization sub-process in terms of efficiency. Our analysis can provide information for the formulation of policies to achieve high innovation efficiency.

Suggested Citation

  • Chen, Xiafei & Liu, Zhiying & Zhu, Qingyuan, 2020. "Reprint of "Performance evaluation of China's high-tech innovation process :Analysis based on the innovation value chain"," Technovation, Elsevier, vol. 94.
    • Handle: RePEc:eee:techno:v:94-95:y:2020:i::s0166497219300598
    DOI: 10.1016/j.technovation.2019.102094
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    as
    1. Kao, Chiang, 2009. "Efficiency decomposition in network data envelopment analysis: A relational model," European Journal of Operational Research, Elsevier, vol. 192(3), pages 949-962, February.
    2. Yung-ho Chiu & Chin-wei Huang & Yu-Chuan Chen, 2012. "The R&D value-chain efficiency measurement for high-tech industries in China," Asia Pacific Journal of Management, Springer, vol. 29(4), pages 989-1006, December.
    3. Dirk Czarnitzki & Hanna Hottenrott & Susanne Thorwarth, 2011. "Industrial research versus development investment: the implications of financial constraints," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 35(3), pages 527-544.
    4. Chen, Chialin & Zhu, Joe & Yu, Jiun-Yu & Noori, Hamid, 2012. "A new methodology for evaluating sustainable product design performance with two-stage network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 221(2), pages 348-359.
    5. Alireza Amirteimoori, 2013. "A DEA two-stage decision processes with shared resources," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 21(1), pages 141-151, January.
    6. repec:fth:harver:1473 is not listed on IDEAS
    7. Roper, Stephen & Arvanitis, Spyros, 2012. "From knowledge to added value: A comparative, panel-data analysis of the innovation value chain in Irish and Swiss manufacturing firms," Research Policy, Elsevier, vol. 41(6), pages 1093-1106.
    8. Buesa, Mikel & Heijs, Joost & Baumert, Thomas, 2010. "The determinants of regional innovation in Europe: A combined factorial and regression knowledge production function approach," Research Policy, Elsevier, vol. 39(6), pages 722-735, July.
    9. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    10. Roper, Stephen & Du, Jun & Love, James H., 2008. "Modelling the innovation value chain," Research Policy, Elsevier, vol. 37(6-7), pages 961-977, July.
    11. Kao, Chiang & Hwang, Shiuh-Nan, 2008. "Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan," European Journal of Operational Research, Elsevier, vol. 185(1), pages 418-429, February.
    12. Wang, Chun-Hsien & Lu, Yung-Hsiang & Huang, Chin-Wei & Lee, Jun-Yen, 2013. "R&D, productivity, and market value: An empirical study from high-technology firms," Omega, Elsevier, vol. 41(1), pages 143-155.
    13. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    14. Bronzini, Raffaello & Piselli, Paolo, 2016. "The impact of R&D subsidies on firm innovation," Research Policy, Elsevier, vol. 45(2), pages 442-457.
    15. Zvi Griliches, 1998. "Issues in Assessing the Contribution of Research and Development to Productivity Growth," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 17-45, National Bureau of Economic Research, Inc.
    16. Michael Fritsch, 2002. "Measuring the Quality of Regional Innovation Systems: A Knowledge Production Function Approach," International Regional Science Review, , vol. 25(1), pages 86-101, January.
    17. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    18. Wade Cook & Moez Hababou & Hans Tuenter, 2000. "Multicomponent Efficiency Measurement and Shared Inputs in Data Envelopment Analysis: An Application to Sales and Service Performance in Bank Branches," Journal of Productivity Analysis, Springer, vol. 14(3), pages 209-224, November.
    19. Liang Liang & Feng Yang & Wade Cook & Joe Zhu, 2006. "DEA models for supply chain efficiency evaluation," Annals of Operations Research, Springer, vol. 145(1), pages 35-49, July.
    20. Robert E. Hall & Charles I. Jones, 1999. "Why do Some Countries Produce So Much More Output Per Worker than Others?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(1), pages 83-116.
    21. Guangzhou Hu, Albert, 2001. "Ownership, Government R&D, Private R&D, and Productivity in Chinese Industry," Journal of Comparative Economics, Elsevier, vol. 29(1), pages 136-157, March.
    22. Hu, Mei-Chih & Mathews, John A., 2008. "China's national innovative capacity," Research Policy, Elsevier, vol. 37(9), pages 1465-1479, October.
    23. Lin, Bou-Wen & Lee, Yikuan & Hung, Shih-Chang, 2006. "R&D intensity and commercialization orientation effects on financial performance," Journal of Business Research, Elsevier, vol. 59(6), pages 679-685, June.
    24. Tseng, Fang-Mei & Chiu, Yu-Jing & Chen, Ja-Shen, 2009. "Measuring business performance in the high-tech manufacturing industry: A case study of Taiwan's large-sized TFT-LCD panel companies," Omega, Elsevier, vol. 37(3), pages 686-697, June.
    25. Chen, Yao & Cook, Wade D. & Li, Ning & Zhu, Joe, 2009. "Additive efficiency decomposition in two-stage DEA," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1170-1176, August.
    26. Bruno Cassiman & Giovanni Valentini, 2016. "Open innovation: Are inbound and outbound knowledge flows really complementary?," Strategic Management Journal, Wiley Blackwell, vol. 37(6), pages 1034-1046, June.
    27. Chen, Yao & Du, Juan & David Sherman, H. & Zhu, Joe, 2010. "DEA model with shared resources and efficiency decomposition," European Journal of Operational Research, Elsevier, vol. 207(1), pages 339-349, November.
    28. Zha, Yong & Liang, Liang, 2010. "Two-stage cooperation model with input freely distributed among the stages," European Journal of Operational Research, Elsevier, vol. 205(2), pages 332-338, September.
    29. Zvi Griliches, 1998. "Patent Statistics as Economic Indicators: A Survey," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 287-343, National Bureau of Economic Research, Inc.
    30. Kaihua Chen & Jiancheng Guan, 2012. "Measuring the Efficiency of China's Regional Innovation Systems: Application of Network Data Envelopment Analysis (DEA)," Regional Studies, Taylor & Francis Journals, vol. 46(3), pages 355-377, April.
    31. Hong, Jin & Feng, Bing & Wu, Yanrui & Wang, Liangbing, 2016. "Do government grants promote innovation efficiency in China's high-tech industries?," Technovation, Elsevier, vol. 57, pages 4-13.
    32. Cappelen, Ådne & Raknerud, Arvid & Rybalka, Marina, 2012. "The effects of R&D tax credits on patenting and innovations," Research Policy, Elsevier, vol. 41(2), pages 334-345.
    33. Cook, Wade D. & Hababou, Moez, 2001. "Sales performance measurement in bank branches," Omega, Elsevier, vol. 29(4), pages 299-307, August.
    34. Maietta, Ornella Wanda, 2015. "Determinants of university–firm R&D collaboration and its impact on innovation: A perspective from a low-tech industry," Research Policy, Elsevier, vol. 44(7), pages 1341-1359.
    35. Hung, Shiu-Wan & Wang, An-Pang, 2012. "Entrepreneurs with glamour? DEA performance characterization of high-tech and older-established industries," Economic Modelling, Elsevier, vol. 29(4), pages 1146-1153.
    36. Li, Xibao, 2009. "China's regional innovation capacity in transition: An empirical approach," Research Policy, Elsevier, vol. 38(2), pages 338-357, March.
    37. Cook, Wade D. & Zhu, Joe & Bi, Gongbing & Yang, Feng, 2010. "Network DEA: Additive efficiency decomposition," European Journal of Operational Research, Elsevier, vol. 207(2), pages 1122-1129, December.
    38. Maietta, Ornella Wanda, 2015. "Determinants of R&D University-Frim Collaboration and Its Impact on Innovation: a Perspective from the Italian Food and Drink Industry," 2015 Conference, August 9-14, 2015, Milan, Italy 225668, International Association of Agricultural Economists.
    39. Zhang, Anming & Zhang, Yimin & Zhao, Ronald, 2003. "A study of the R&D efficiency and productivity of Chinese firms," Journal of Comparative Economics, Elsevier, vol. 31(3), pages 444-464, September.
    40. Liang Liang & Wade D. Cook & Joe Zhu, 2008. "DEA models for two‐stage processes: Game approach and efficiency decomposition," Naval Research Logistics (NRL), John Wiley & Sons, vol. 55(7), pages 643-653, October.
    41. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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