Operational Efficiency Evaluation of Chinese Internet Banks: Two-Stage Network DEA Approach
Abstract
:1. Introduction
2. Literature Review
3. Research Methods
4. Empirical Analysis
4.1. Index Selection of Internet Banking System
4.2. Sample Selection and Data Sources
4.3. Two-Stage Efficiency Analysis
4.3.1. Stage Efficiency and Comprehensive Efficiency Analysis of Internet Banking in 2018
4.3.2. Stage Efficiency and Comprehensive Efficiency Analysis of Internet Banking in 2019
4.4. The Kruskal–Wallis Test
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Year | Project | Net Assets (Thousand CNY) | Employees (Number) | R&D Investment (Thousand CNY) | Total Deposits (Thousand CNY) | Provision for Loan Impairment (Thousand CNY) | Net Interest Income (Thousand CNY) | Non-Interest Income (Thousand CNY) | Non-Performing Loan Ratio |
---|---|---|---|---|---|---|---|---|---|
2018 | Mean | 3,528,078 | 384 | 241,924 | 31,418,835 | 581,484 | 1,021,746 | 435,069 | 0.43% |
Std | 2,453,992 | 433 | 662,626 | 42,136,088 | 986,196 | 1,611,883 | 1,161,784 | 0.82% | |
Min | 1,793,165 | 87 | 528 | 1,976,129 | 51,000 | 21,313 | −156,546 | 0.00% | |
Max | 11,940,475 | 1906 | 2,698,378 | 175,462,888 | 3,554,500 | 5,520,011 | 4,509,728 | 3.16% | |
0.25 | 2,092,616 | 180 | 12,459 | 9,487,995 | 134,115 | 196,037 | 3698 | 0.00% | |
0.50 | 3,118,311 | 271 | 39,513 | 20,661,524 | 216,242 | 484,092 | 63,529 | 0.01% | |
0.70 | 3,962,590 | 396 | 155,255 | 26,652,582 | 433,465 | 862,893 | 179,550 | 0.56% | |
2019 | Mean | 4,289,105 | 475 | 306,542 | 46,232,394 | 878,733 | 1,670,672 | 442,299 | 0.67% |
Std | 3,767,311 | 568 | 744,399 | 60,823,572 | 1,244,040 | 2,400,071 | 1,404,875 | 0.48% | |
Min | 1,959,403 | 113 | 7382 | 8,230,615 | 79,520 | 176,085 | −800,378 | 0.00% | |
Max | 16,119,128 | 2509 | 3,044,986 | 253,423,229 | 4,323,000 | 9,463,779 | 5,406,552 | 1.30% | |
0.25 | 2,166,873 | 220 | 25,045 | 15,410,046 | 223,668 | 397,126 | 4509 | 0.17% | |
0.50 | 3,369,344 | 321 | 77,082 | 26,489,432 | 472,171 | 934,283 | 56,585 | 0.60% | |
0.70 | 4,153,743 | 445 | 208,338 | 41,718,618 | 748,500 | 1,649,300 | 355,514 | 1.14% |
Bank Name | Value Operation Stage Efficiency | Value Creation Stage Efficiency | Comprehensive Efficiency | Value Operation Stage Weight | Value Creation Stage Weight |
---|---|---|---|---|---|
WeBank | 1 | 1 | 1 | 0.4006 | 0.5994 |
Wenzhou Mingshang Bank | 0.437 | 0.963 | 0.962 | 0.0021 | 0.9979 |
Zhejiang E-Commerce Bank | 1 | 1 | 1 | 0.3864 | 0.6136 |
Kincheng Bank of Tianjin | 0.549 | 0.656 | 0.629 | 0.2498 | 0.7502 |
Shanghai HuaRui Bank | 0.518 | 0.834 | 0.762 | 0.2254 | 0.7746 |
Chongqing Fuming Bank | 0.625 | 0.852 | 0.851 | 0.0041 | 0.9959 |
XWBank | 0.825 | 0.771 | 0.780 | 0.162 | 0.838 |
Bank of Sanxiang | 0.760 | 1 | 0.999 | 0.0035 | 0.9965 |
Fujian OneBank | 0.087 | 0.688 | 0.686 | 0.0023 | 0.9977 |
Wuhanzbank | 1 | 1 | 1 | 0.2215 | 0.7785 |
Weihai Blue Ocean Bank | 0.850 | 1 | 0.978 | 0.1456 | 0.8544 |
Zhongguancun Bank | 0.490 | 1 | 0.998 | 0.0032 | 0.9968 |
Jilin Yillion Bank | 0.493 | 0.897 | 0.896 | 0.0023 | 0.9977 |
Jiangsu Suning Bank | 0.779 | 0.906 | 0.906 | 0.0033 | 0.9967 |
Meizhou Hakka Bank | 0.817 | 1 | 1 | 0.0012 | 0.9988 |
NewUp Bank of Liaoning | 1 | 1 | 1 | 0.0787 | 0.9213 |
Bank Name | Value Operation Stage Efficiency | Value Creation Stage Efficiency | Comprehensive Efficiency | Value Operation Stage Weight | Value Creation Stage Weight |
---|---|---|---|---|---|
WeBank | 1 | 1 | 1 | 0.0266 | 0.9734 |
Wenzhou Mingshang Bank | 0.908 | 1 | 1 | 0.0014 | 0.9986 |
Zhejiang E-Commerce Bank | 1 | 1 | 1 | 0.4199 | 0.5801 |
Kincheng Bank of Tianjin | 0.626 | 1 | 0.998 | 0.0042 | 0.9958 |
Shanghai HuaRui Bank | 0.472 | 0.689 | 0.688 | 0.0053 | 0.9947 |
Chongqing Fuming Bank | 0.630 | 0.648 | 0.642 | 0.3705 | 0.6295 |
XWBank | 0.652 | 1 | 0.885 | 0.3319 | 0.6681 |
Bank of Sanxiang | 0.805 | 0.967 | 0.966 | 0.0054 | 0.9946 |
Fujian OneBank | 0.262 | 0.820 | 0.819 | 0.0031 | 0.9969 |
Wuhanzbank | 1 | 1 | 1 | 0.0254 | 0.9746 |
Weihai Blue Ocean Bank | 1 | 1 | 1 | 0.0962 | 0.9038 |
Zhongguancun Bank | 0.439 | 1 | 0.998 | 0.0027 | 0.9973 |
Jilin Yillion Bank | 0.846 | 0.747 | 0.791 | 0.4488 | 0.5512 |
Jiangsu Suning Bank | 1 | 0.992 | 0.993 | 0.0413 | 0.9587 |
Meizhou Hakka Bank | 0.794 | 1 | 1 | 0.0017 | 0.9983 |
NewUp Bank of Liaoning | 0.986 | 0.461 | 0.725 | 0.5035 | 0.4965 |
Bank Name | Mean Comprehensive Efficiency | Province | Economic Belt | Group |
---|---|---|---|---|
WeBank | 1.000 | Guangdong | Eastern | 1 |
Wenzhou Mingshang Bank | 0.981 | Zhejiang | Eastern | 1 |
Zhejiang E-Commerce Bank | 1.000 | Zhejiang | Eastern | 1 |
Kincheng Bank of Tianjin | 0.814 | Tianjin | Eastern | 1 |
Shanghai HuaRui Bank | 0.725 | Shanghai | Eastern | 1 |
Chongqing Fuming Bank | 0.746 | Chongqing | Western | 3 |
XWBank | 0.832 | Sichuan | Western | 3 |
Bank of Sanxiang | 0.983 | Hunan | Central | 2 |
Fujian OneBank | 0.752 | Fujian | Eastern | 1 |
Wuhanzbank | 1.000 | Hubei | Central | 2 |
Weihai Blue Ocean Bank | 0.989 | Shandong | Eastern | 1 |
Zhongguancun Bank | 0.998 | Beijing | Eastern | 1 |
Jilin Yillion Bank | 0.844 | Jilin | Central | 2 |
Jangsu Suning Bank | 0.949 | Jiangsu | Eastern | 1 |
Meizhou Hakka Bank | 1.000 | Guangdong | Eastern | 1 |
NewUp Bank of Liaoning | 0.863 | Liaoning | Eastern | 1 |
The Null Hypothesis | Test | Sig. | Decision | |
---|---|---|---|---|
The distribution of the mean value of comprehensive efficiency has no significant difference among the three economic belts | Independent samples Kruskal–Wallis test | 0.257 | Do not reject the null hypothesis |
Bank Name | 2018 | 2019 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
MATLAB | Max DEA | MATLAB | Max DEA | |||||||||
WeBank | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Wenzhou Mingshang Bank | 0.437 | 0.963 | 0.962 | 0.86 | 0.845 | 0.852 | 0.908 | 1 | 1 | 1 | 1 | 1 |
Zhejiang E-Commerce Bank | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Kincheng Bank of Tianjin | 0.549 | 0.656 | 0.629 | 0.588 | 0.651 | 0.619 | 0.626 | 1 | 0.998 | 0.719 | 1 | 0.859 |
Shanghai HuaRui Bank | 0.518 | 0.834 | 0.762 | 0.556 | 0.913 | 0.734 | 0.472 | 0.689 | 0.688 | 0.514 | 0.731 | 0.623 |
Chongqing Fuming Bank | 0.625 | 0.852 | 0.851 | 0.619 | 0.401 | 0.51 | 0.630 | 0.648 | 0.642 | 0.603 | 0.589 | 0.596 |
XWBank | 0.825 | 0.771 | 0.780 | 0.643 | 0.776 | 0.709 | 0.652 | 1 | 0.885 | 0.675 | 1 | 0.837 |
Bank of Sanxiang | 0.760 | 1 | 0.999 | 0.801 | 1 | 0.901 | 0.805 | 0.967 | 0.966 | 0.624 | 1 | 0.812 |
Fujian OneBank | 0.087 | 0.688 | 0.686 | 0.909 | 0.688 | 0.798 | 0.262 | 0.820 | 0.819 | 0.959 | 0.732 | 0.845 |
Wuhanzbank | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Weihai Blue Ocean Bank | 0.850 | 1 | 0.978 | 0.966 | 1 | 0.983 | 1 | 1 | 1 | 1 | 1 | 1 |
Zhongguancun Bank | 0.490 | 1 | 0.998 | 0.716 | 1 | 0.858 | 0.439 | 1 | 0.998 | 1 | 1 | 1 |
Jilin Yillion Bank | 0.493 | 0.897 | 0.896 | 1 | 0.513 | 0.757 | 0.846 | 0.747 | 0.791 | 1 | 0.757 | 0.878 |
Jiangsu Suning Bank | 0.779 | 0.906 | 0.906 | 0.548 | 0.808 | 0.678 | 1 | 0.992 | 0.993 | 1 | 1 | 1 |
Meizhou Hakka Bank | 0.817 | 1 | 1 | 1 | 1 | 1 | 0.794 | 1 | 1 | 1 | 1 | 1 |
NewUp Bank of Liaoning | 1 | 1 | 1 | 1 | 1 | 1 | 0.986 | 0.461 | 0.725 | 1 | 1 | 1 |
The number of banks with an efficiency value of 1 | 4 | 8 | 5 | 6 | 8 | 5 | 5 | 9 | 6 | 10 | 12 | 9 |
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Li, M.; Zhu, N.; He, K.; Li, M. Operational Efficiency Evaluation of Chinese Internet Banks: Two-Stage Network DEA Approach. Sustainability 2022, 14, 14165. https://doi.org/10.3390/su142114165
Li M, Zhu N, He K, Li M. Operational Efficiency Evaluation of Chinese Internet Banks: Two-Stage Network DEA Approach. Sustainability. 2022; 14(21):14165. https://doi.org/10.3390/su142114165
Chicago/Turabian StyleLi, Min, Nan Zhu, Kai He, and Minghui Li. 2022. "Operational Efficiency Evaluation of Chinese Internet Banks: Two-Stage Network DEA Approach" Sustainability 14, no. 21: 14165. https://doi.org/10.3390/su142114165