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Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach

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  • Chen, Yufeng
  • Ni, Liangfu
  • Liu, Kelong

Abstract

Innovation ability is vital to the new energy vehicle(NEV) industry in China. To bridge the previous studies’ gaps, a dynamic network slacks-based measure(DNSBM) model, coupled with the three-stage innovation framework, is employed to evaluate the industrial innovation efficiency. Overall, dynamic efficiencies and the divisional efficiencies of the R&D, manufacturing, and marketing process are present, drawing on 17 listed companies’ data from 2012 to 2019. Additionally, the Malmquist index measures industrial productivity and its decomposition, including the Frontier-shift and Catch-up effect. The firm-level efficiencies’ analysis ends up following the types of enterprises and divisional performance. The findings reveal that the overall innovation efficiency was low, and marketing and manufacturing efficiency were higher than those of the R&D division. Then, the annual overall and divisional productivities were low, whereas the cumulative productivities were high. In the end, all enterprises’ performance differentiated, and commercial-vehicle firms’ efficiencies were higher than those of the passenger-vehicle firms except for the marketing division. Meanwhile, the state-owned companies’ efficiencies were higher than those of private companies’ except for the marketing process. The theoretical and practical implications are available for the policymakers, managers, and academic researchers from this study.

Suggested Citation

  • Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2021. "Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
  • Handle: RePEc:eee:tefoso:v:173:y:2021:i:c:s0040162521005941
    DOI: 10.1016/j.techfore.2021.121161
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    as
    1. Rolf Färe & Shawna Grosskopf & Gerald Whittaker, 2014. "Network DEA II," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 307-327, Springer.
    2. Machura, Philip & Li, Quan, 2019. "A critical review on wireless charging for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 209-234.
    3. Wu, Yang Andrew & Ng, Artie W. & Yu, Zichao & Huang, Jie & Meng, Ke & Dong, Z.Y., 2021. "A review of evolutionary policy incentives for sustainable development of electric vehicles in China: Strategic implications," Energy Policy, Elsevier, vol. 148(PB).
    4. Mukherjee, Sanghamitra Chattopadhyay & Ryan, Lisa, 2020. "Factors influencing early battery electric vehicle adoption in Ireland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    5. Rong, Ke & Shi, Yongjiang & Shang, Tianjiao & Chen, Yantai & Hao, Han, 2017. "Organizing business ecosystems in emerging electric vehicle industry: Structure, mechanism, and integrated configuration," Energy Policy, Elsevier, vol. 107(C), pages 234-247.
    6. Wanke, Peter & Barros, Carlos Pestana, 2016. "Efficiency drivers in Brazilian insurance: A two-stage DEA meta frontier-data mining approach," Economic Modelling, Elsevier, vol. 53(C), pages 8-22.
    7. Philippe Aghion & Antoine Dechezleprêtre & David Hémous & Ralf Martin & John Van Reenen, 2016. "Carbon Taxes, Path Dependency, and Directed Technical Change: Evidence from the Auto Industry," Journal of Political Economy, University of Chicago Press, vol. 124(1), pages 1-51.
    8. Wang, Ya & Pan, Jiao-feng & Pei, Rui-min & Yi, Bo-Wen & Yang, Guo-liang, 2020. "Assessing the technological innovation efficiency of China's high-tech industries with a two-stage network DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    9. Guan, Jiancheng & Chen, Kaihua, 2012. "Modeling the relative efficiency of national innovation systems," Research Policy, Elsevier, vol. 41(1), pages 102-115.
    10. Zhang, Lei & Qin, Quande, 2018. "China’s new energy vehicle policies: Evolution, comparison and recommendation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 57-72.
    11. Zhang, Xiang & Bai, Xue, 2017. "Incentive policies from 2006 to 2016 and new energy vehicle adoption in 2010–2020 in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 24-43.
    12. Siran Fang & Xiaoshan Xue & Ge Yin & Hong Fang & Jialin Li & Yongnian Zhang, 2020. "Evaluation and Improvement of Technological Innovation Efficiency of New Energy Vehicle Enterprises in China Based on DEA-Tobit Model," Sustainability, MDPI, vol. 12(18), pages 1-22, September.
    13. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    14. Gu, Huaying & Liu, Zhixue & Qing, Qiankai, 2017. "Optimal electric vehicle production strategy under subsidy and battery recycling," Energy Policy, Elsevier, vol. 109(C), pages 579-589.
    15. 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.
    16. Herrera-Restrepo, Oscar & Triantis, Konstantinos & Trainor, Joseph & Murray-Tuite, Pamela & Edara, Praveen, 2016. "A multi-perspective dynamic network performance efficiency measurement of an evacuation: A dynamic network-DEA approach," Omega, Elsevier, vol. 60(C), pages 45-59.
    17. Kaoru Tone & Miki Tsutsui, 2014. "Slacks-Based Network DEA," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 231-259, Springer.
    18. Langbroek, Joram H.M. & Franklin, Joel P. & Susilo, Yusak O., 2016. "The effect of policy incentives on electric vehicle adoption," Energy Policy, Elsevier, vol. 94(C), pages 94-103.
    19. Li, Wenbo & Long, Ruyin & Chen, Hong, 2016. "Consumers’ evaluation of national new energy vehicle policy in China: An analysis based on a four paradigm model," Energy Policy, Elsevier, vol. 99(C), pages 33-41.
    20. Tone, Kaoru & Kweh, Qian Long & Lu, Wen-Min & Ting, Irene Wei Kiong, 2019. "Modeling investments in the dynamic network performance of insurance companies," Omega, Elsevier, vol. 88(C), pages 237-247.
    21. Avkiran, Necmi Kemal, 2015. "An illustration of dynamic network DEA in commercial banking including robustness tests," Omega, Elsevier, vol. 55(C), pages 141-150.
    22. Yuan, Xueliang & Liu, Xin & Zuo, Jian, 2015. "The development of new energy vehicles for a sustainable future: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 298-305.
    23. Chao, Shih-Liang & Yu, Ming-Miin & Hsieh, Wei-Fan, 2018. "Evaluating the efficiency of major container shipping companies: A framework of dynamic network DEA with shared inputs," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 44-57.
    24. Wang, Sinan & Chen, Kangda & Zhao, Fuquan & Hao, Han, 2019. "Technology pathways for complying with Corporate Average Fuel Consumption regulations up to 2030: A case study of China," Applied Energy, Elsevier, vol. 241(C), pages 257-277.
    25. Qiao, Qinyu & Zhao, Fuquan & Liu, Zongwei & He, Xin & Hao, Han, 2019. "Life cycle greenhouse gas emissions of Electric Vehicles in China: Combining the vehicle cycle and fuel cycle," Energy, Elsevier, vol. 177(C), pages 222-233.
    26. Wang, Qunwei & Hang, Ye & Sun, Licheng & Zhao, Zengyao, 2016. "Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 254-261.
    27. Dong, Feng & Liu, Yajie, 2020. "Policy evolution and effect evaluation of new-energy vehicle industry in China," Resources Policy, Elsevier, vol. 67(C).
    28. Tone, Kaoru & Tsutsui, Miki, 2010. "Dynamic DEA: A slacks-based measure approach," Omega, Elsevier, vol. 38(3-4), pages 145-156, June.
    29. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    30. Khushalani, Jaya & Ozcan, Yasar A., 2017. "Are hospitals producing quality care efficiently? An analysis using Dynamic Network Data Envelopment Analysis (DEA)," Socio-Economic Planning Sciences, Elsevier, vol. 60(C), pages 15-23.
    31. Yan, Jianghui & Tseng, Fang-Mei & Lu, Louis Y.Y., 2018. "Developmental trajectories of new energy vehicle research in economic management: Main path analysis," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 168-181.
    32. Shafiei, Ehsan & Davidsdottir, Brynhildur & Fazeli, Reza & Leaver, Jonathan & Stefansson, Hlynur & Asgeirsson, Eyjolfur Ingi, 2018. "Macroeconomic effects of fiscal incentives to promote electric vehicles in Iceland: Implications for government and consumer costs," Energy Policy, Elsevier, vol. 114(C), pages 431-443.
    33. Hao, Han & Ou, Xunmin & Du, Jiuyu & Wang, Hewu & Ouyang, Minggao, 2014. "China’s electric vehicle subsidy scheme: Rationale and impacts," Energy Policy, Elsevier, vol. 73(C), pages 722-732.
    34. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    35. 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.
    36. Chao Lu & Jie Tao & Qiuxian An & Xiaodong Lai, 2020. "A second-order cone programming based robust data envelopment analysis model for the new-energy vehicle industry," Annals of Operations Research, Springer, vol. 292(1), pages 321-339, September.
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