A second-order cone programming based robust data envelopment analysis model for the new-energy vehicle industry

  • Chao Lu
  • Jie TaoEmail author
  • Qiuxian An
  • Xiaodong Lai
Original - OR Modeling/Case Study


The validity of performance evaluation is determined by, and therefore greatly influenced by, the accuracy of data set. To address such imprecise and negative data problems widely spread in the real world, this paper proposes a second-order cone based robust data envelopment analysis (SOCPR-DEA) model, which is more robust to data variety. Further, this new computational tractable model is applied to analyze 13 new-energy vehicle (NEV) manufacturers from China. The findings support that the SOCPR-DEA model could well mitigate the deficiency caused by data variety, and the evidence from Chinese NEV industry shows that a focus strategy is more likely to enhance a firm’s efficiency especially at its emerging stage, and the efficiency is more sensitive with production cost than other factors such as research and development, sales income, earnings per share, and predicted income. In addition, this paper also gives some industrial implications and policy suggestions based on these interesting findings.


New-energy vehicle industry Efficiency Robust data envelopment analysis model Data variety Conic programming 



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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of ManagementShanghai UniversityShanghaiChina
  2. 2.Business SchoolUniversity of Shanghai for Science and TechnologyShanghaiChina
  3. 3.School of BusinessNorth China Electric Power UniversityBeijingChina
  4. 4.School of Economic and ManagementSouth China Normal UniversityGuangzhouChina

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