Abstract
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.
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This research is supported by National Natural Science Foundation of China “A Key Stakeholder-based Innovation Policy Mechanism and Simulating Optimization for Emerging Industry: Evidence from New-energy Vehicle” (Grant No. 71704101), National Natural Science Foundation of China “Methods for Computing Large-scale Data Envelopment Analysis models via techniques of enclosing of convex bodies” (Grant No. 71601117), MOE (Ministry of Education in China) Project of Humanities and Social Sciences “Innovation Policy Evaluation and Optimization of New Energy Vehicle Industry: A Key Stakeholder-based Empirical Study” (Grant No. 17YJC630094), The Key Soft Science Research Projects of Shanghai Science and Technology Innovation Action Plan “A Study on the Development Model and Policy of High-speed and Low-speed Electric Vehicle Industries” (Grant No. 18692108300).
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Lu, C., Tao, J., An, Q. et al. A second-order cone programming based robust data envelopment analysis model for the new-energy vehicle industry. Ann Oper Res 292, 321–339 (2020). https://doi.org/10.1007/s10479-019-03155-9
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DOI: https://doi.org/10.1007/s10479-019-03155-9