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Evaluation of Bank Innovation Efficiency with Data Envelopment Analysis: From the Perspective of Uncovering the Black Box between Input and Output

Author

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  • Kaiyang Zhong

    (School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China)

  • Chenglin Li

    (School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China)

  • Qing Wang

    (Chongqing College of Electronic Engineering, Chongqing 401331, China)

Abstract

The evaluation of corporation operation efficiency (especially innovation efficiency) has been always a hot topic. The currently popular evaluation methods are data envelopment analysis (DEA) and its improved methods. However, these methods have the following problems: the production process is regarded as a black box, and the actual production relationship between input and output is not analyzed. To solve these problems: (1) the black box theory and production function theory are introduced to uncover the black box of input and output; (2) regression models are used to alleviate the multicollinearity problem of inputs, and the most appropriate model of production relationship is selected; and (3) the results of the production function are compared with the results of the efficiency evaluation from multiple perspectives. Taking rural commercial banks in China as examples to evaluate their innovation efficiency, this article shows the following: (1) with the black box theory and production function theory, the staff, equipment, and intermediate business cost are suitable as innovation input variables, and intermediate business income is suitable as an innovation output variable; (2) the main challenges faced by rural commercial banks are reducing the reliance on human capital investment, strengthening technological innovation, and improving the efficiency of intermediate business cost management, which is hard to reveal with traditional DEA. The method proposed in this article provides an applicable reference for improving DEA method analysis.

Suggested Citation

  • Kaiyang Zhong & Chenglin Li & Qing Wang, 2021. "Evaluation of Bank Innovation Efficiency with Data Envelopment Analysis: From the Perspective of Uncovering the Black Box between Input and Output," Mathematics, MDPI, vol. 9(24), pages 1-18, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:24:p:3318-:d:706521
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    Cited by:

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    5. Yongrong Xin & Kengcheng Zheng & Yujiao Zhou & Yangyang Han & P. R. Tadikamalla & Qin Fan, 2022. "Logistics Efficiency under Carbon Constraints Based on a Super SBM Model with Undesirable Output: Empirical Evidence from China’s Logistics Industry," Sustainability, MDPI, vol. 14(9), pages 1-24, April.
    6. Yujiao Zhou & Ding Li & Weifeng Li & Dong Mei & Jianyi Zhong, 2022. "Drag Effect of Economic Growth and Its Spatial Differences under the Constraints of Resources and Environment: Empirical Findings from China’s Yellow River Basin," IJERPH, MDPI, vol. 19(5), pages 1-21, March.
    7. Siwei Xiao & Marios Dominikos Kremantzis & Leonidas Sotirios Kyrgiakos & George Vlontzos & Panos M. Pardalos, 2024. "Embracing fairness within a cross-efficiency hierarchical network DEA system," Operational Research, Springer, vol. 24(1), pages 1-31, March.
    8. Yang Liu & Yanlin Yang & Huihui Li & Kaiyang Zhong, 2022. "Digital Economy Development, Industrial Structure Upgrading and Green Total Factor Productivity: Empirical Evidence from China’s Cities," IJERPH, MDPI, vol. 19(4), pages 1-23, February.
    9. Zhishuo Zhang & Yao Xiao & Zitian Fu & Kaiyang Zhong & Huayong Niu, 2022. "A Study on Early Warnings of Financial Crisis of Chinese Listed Companies Based on DEA–SVM Model," Mathematics, MDPI, vol. 10(12), pages 1-23, June.
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