| Issue |
E3S Web Conf.
Volume 214, 2020
2020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
|
|
|---|---|---|
| Article Number | 01036 | |
| Number of page(s) | 3 | |
| Section | Big Data Analysis Application and Energy Consumption Research | |
| DOI | https://doi.org/10.1051/e3sconf/202021401036 | |
| Published online | 07 December 2020 | |
Two-stage DEA for Bank Efficiency Evaluation Considering Shared Input and Unexpected Output Factors
1 School of Management, Huazhong University of Science and Technology, Wuhan, 450074
2 School of Business, ZhengZhou Vocational College of Finance and Taxation, Zhengzhou, 450048
3 School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, 450046
* Corresponding author: Yang Juan
With the increasingly fierce market competition, only by relying on high-quality products and high customer satisfaction can enterprises survive in the fierce competition. Among many evaluation methods, Data Envelopment Analysis (DEA), as a non-parametric statistical method to effectively deal with multi-input and multi-output problems, has received more and more attention in evaluating the relative efficiency of decision-making units. In the process of bank efficiency evaluation based on DEA method, there will be a situation that banks have both dual role factors and unexpected output factors. The Two-stage DEA model provides an effective analysis method to solve the problem of bank efficiency evaluation of complex organizational structure. In order to evaluate the efficiency of unexpected output with uncertain information, a stochastic DEA model of unexpected output is established.
© The Authors, published by EDP Sciences, 2020
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