IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i4p1523-d322110.html
   My bibliography  Save this article

Non-Parametric Model for Evaluating the Performance of Chinese Commercial Banks’ Product Innovation

Author

Listed:
  • Luning Shao

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Jianxin You

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Tao Xu

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Yilei Shao

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

Abstract

A thorough analysis of commercial banks’ product innovation performance is essential to promoting bank product innovation capabilities and sustainable development. In this paper, the product innovation performance of commercial banks is defined as the conversion efficiency of input and output factors. The credit risk of product innovation of banks is considered as an undesirable output and incorporated in the performance evaluation system. Depending on whether there is a synchronous relationship between innovation income and risks, a Fixed Correlation model (FCM) and a Variable Correlation model (VCM) are then constructed based on Data Envelopment Analysis (DEA) method for the evaluation of commercial bank product innovation performance. In addition, an output optimization model of the objective function is also constructed to estimate the target income of commercial banks’ product innovation in the FCM and VCM. Finally, the proposed model is applied to Chinese listed commercial banks for estimating the performance and target income of product innovation.

Suggested Citation

  • Luning Shao & Jianxin You & Tao Xu & Yilei Shao, 2020. "Non-Parametric Model for Evaluating the Performance of Chinese Commercial Banks’ Product Innovation," Sustainability, MDPI, vol. 12(4), pages 1-15, February.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1523-:d:322110
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/4/1523/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/4/1523/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Calmès, Christian & Théoret, Raymond, 2010. "The impact of off-balance-sheet activities on banks returns: An application of the ARCH-M to Canadian data," Journal of Banking & Finance, Elsevier, vol. 34(7), pages 1719-1728, July.
    2. Vlontzos, George & Niavis, Spyros & Manos, Basil, 2014. "A DEA approach for estimating the agricultural energy and environmental efficiency of EU countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 91-96.
    3. Li Xie & Chunlin Chen & Yihua Yu, 2019. "Dynamic Assessment of Environmental Efficiency in Chinese Industry: A Multiple DEA Model with a Gini Criterion Approach," Sustainability, MDPI, vol. 11(8), pages 1-22, April.
    4. Khondoker Abdul Mottaleb & Tetsushi Sonobe, 2013. "What determines the performance of small enterprises in developing countries? Evidence from the handloom industry in Bangladesh," International Journal of Business and Globalisation, Inderscience Enterprises Ltd, vol. 10(1), pages 39-55.
    5. Feng Wang & Minxue Huang & Zhigang Shou, 2015. "Business expansion and firm efficiency in the commercial banking industry: Evidence from the US and China," Asia Pacific Journal of Management, Springer, vol. 32(2), pages 551-569, June.
    6. Weixin Yang & Lingguang Li, 2017. "Analysis of Total Factor Efficiency of Water Resource and Energy in China: A Study Based on DEA-SBM Model," Sustainability, MDPI, vol. 9(8), pages 1-21, July.
    7. Hao Cai & Ling Liang & Jing Tang & Qianxian Wang & Lihong Wei & Jiaping Xie, 2019. "An Empirical Study on the Efficiency and Influencing Factors of the Photovoltaic Industry in China and an Analysis of Its Influencing Factors," Sustainability, MDPI, vol. 11(23), pages 1-22, November.
    8. Bruno Rossignoli & Francesca Arnaboldi, 2009. "Financial innovation: theoretical issues and empirical evidence in Italy and in the UK," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 56(3), pages 275-301, September.
    9. Laetitia Lepetit & Emmanuelle Nys & Philippe Rous & Amine Tarazi, 2006. "The Provision of Services, Interest Margins and Loan Pricing in European Banking," Working Papers hal-00918533, HAL.
    10. Josh Lerner & Peter Tufano, 2011. "The Consequences of Financial Innovation: A Counterfactual Research Agenda," NBER Chapters, in: The Rate and Direction of Inventive Activity Revisited, pages 523-575, National Bureau of Economic Research, Inc.
    11. Peter W. Roberts & Raphael Amit, 2003. "The Dynamics of Innovative Activity and Competitive Advantage: The Case of Australian Retail Banking, 1981 to 1995," Organization Science, INFORMS, vol. 14(2), pages 107-122, April.
    12. Fare, R. & Grosskopf, S. & Pasurka, C., 1986. "Effects on relative efficiency in electric power generation due to environmental controls," Resources and Energy, Elsevier, vol. 8(2), pages 167-184, June.
    13. Bian, Yiwen & He, Ping & Xu, Hao, 2013. "Estimation of potential energy saving and carbon dioxide emission reduction in China based on an extended non-radial DEA approach," Energy Policy, Elsevier, vol. 63(C), pages 962-971.
    14. Chateau, John-Peter D., 2009. "Marking-to-model credit and operational risks of loan commitments: A Basel-2 advanced internal ratings-based approach," International Review of Financial Analysis, Elsevier, vol. 18(5), pages 260-270, December.
    15. Fukuyama, Hirofumi & Matousek, Roman, 2017. "Modelling bank performance: A network DEA approach," European Journal of Operational Research, Elsevier, vol. 259(2), pages 721-732.
    16. Norden, Lars & Silva Buston, Consuelo & Wagner, Wolf, 2014. "Financial innovation and bank behavior: Evidence from credit markets," Journal of Economic Dynamics and Control, Elsevier, vol. 43(C), pages 130-145.
    17. Wang, Ke & Huang, Wei & Wu, Jie & Liu, Ying-Nan, 2014. "Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA," Omega, Elsevier, vol. 44(C), pages 5-20.
    18. Liu, Wenbin & Zhou, Zhongbao & Ma, Chaoqun & Liu, Debin & Shen, Wanfang, 2015. "Two-stage DEA models with undesirable input-intermediate-outputs," Omega, Elsevier, vol. 56(C), pages 74-87.
    19. Yang, Hongliang & Pollitt, Michael, 2010. "The necessity of distinguishing weak and strong disposability among undesirable outputs in DEA: Environmental performance of Chinese coal-fired power plants," Energy Policy, Elsevier, vol. 38(8), pages 4440-4444, August.
    20. Wei, Quanling & Zhang, Jianzhong & Zhang, Xiangsun, 2000. "An inverse DEA model for inputs/outputs estimate," European Journal of Operational Research, Elsevier, vol. 121(1), pages 151-163, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    2. Tamer Khraisha & Keren Arthur, 2018. "Can we have a general theory of financial innovation processes? A conceptual review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 4(1), pages 1-27, December.
    3. Fukuyama, Hirofumi & Matousek, Roman & Tzeremes, Nickolaos G., 2020. "A Nerlovian cost inefficiency two-stage DEA model for modeling banks’ production process: Evidence from the Turkish banking system," Omega, Elsevier, vol. 95(C).
    4. Chen, Lei & Wang, Ying-Ming & Lai, Fujun, 2017. "Semi-disposability of undesirable outputs in data envelopment analysis for environmental assessments," European Journal of Operational Research, Elsevier, vol. 260(2), pages 655-664.
    5. Qingxian An & Xuyang Liu & Yongli Li & Beibei Xiong, 2019. "Resource planning of Chinese commercial banking systems using two-stage inverse data envelopment analysis with undesirable outputs," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-20, June.
    6. Jiawei Yang, 2023. "Disentangling the sources of bank inefficiency: a two-stage network multi-directional efficiency analysis approach," Annals of Operations Research, Springer, vol. 326(1), pages 369-410, July.
    7. Li, Xingchen & Xu, Guangcheng & Wu, Jie & Xu, Chengzhen & Zhu, Qingyuan, 2024. "Evaluation of bank efficiency by considering the uncertainty of nonperforming loans," Omega, Elsevier, vol. 126(C).
    8. Feng Li & Qingyuan Zhu & Jun Zhuang, 2018. "Analysis of fire protection efficiency in the United States: a two-stage DEA-based approach," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(1), pages 23-68, January.
    9. Jeanneaux, Philippe & Latruffe, Laure, 2016. "Modelling pollution-generating technologies in performance benchmarking: Recent developments, limits and future prospects in the nonparametric frameworkAuthor-Name: Dakpo, K. Hervé," European Journal of Operational Research, Elsevier, vol. 250(2), pages 347-359.
    10. Alex Bara & Pierre LeRoux, 2018. "Technology, Financial Innovations and Bank Behavior in a Low Income Country," Journal of Economics and Behavioral Studies, AMH International, vol. 10(4), pages 221-234.
    11. Du, Xiaoyun & Meng, Conghui & Guo, Zhenhua & Yan, Hang, 2023. "An improved approach for measuring the efficiency of low carbon city practice in China," Energy, Elsevier, vol. 268(C).
    12. Li, Feng & Zhu, Qingyuan & Chen, Zhi, 2019. "Allocating a fixed cost across the decision making units with two-stage network structures," Omega, Elsevier, vol. 83(C), pages 139-154.
    13. Ke Wang & Chia-Yen Lee & Jieming Zhang & Yi-Ming Wei, 2018. "Operational performance management of the power industry: a distinguishing analysis between effectiveness and efficiency," Annals of Operations Research, Springer, vol. 268(1), pages 513-537, September.
    14. Jianping Li & Lu Wei & Cheng-Few Lee & Xiaoqian Zhu & Dengsheng Wu, 2018. "Financial statements based bank risk aggregation," Review of Quantitative Finance and Accounting, Springer, vol. 50(3), pages 673-694, April.
    15. Chu, Junfei & Shao, Caifeng & Emrouznejad, Ali & Wu, Jie & Yuan, Zhe, 2021. "Performance evaluation of organizations considering economic incentives for emission reduction: A carbon emission permit trading approach," Energy Economics, Elsevier, vol. 101(C).
    16. Jamal Ouenniche & Skarleth Carrales, 2018. "Assessing efficiency profiles of UK commercial banks: a DEA analysis with regression-based feedback," Annals of Operations Research, Springer, vol. 266(1), pages 551-587, July.
    17. Sueyoshi, Toshiyuki & Goto, Mika, 2012. "Weak and strong disposability vs. natural and managerial disposability in DEA environmental assessment: Comparison between Japanese electric power industry and manufacturing industries," Energy Economics, Elsevier, vol. 34(3), pages 686-699.
    18. Eucabeth Majiwa & Boon L. Lee & Clevo Wilson & Hidemichi Fujii & Shunsuke Managi, 2018. "A network data envelopment analysis (NDEA) model of post-harvest handling: the case of Kenya’s rice processing industry," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 10(3), pages 631-648, June.
    19. Xiaohong Liu & Feng Yang & Jie Wu, 2020. "DEA considering technological heterogeneity and intermediate output target setting: the performance analysis of Chinese commercial banks," Annals of Operations Research, Springer, vol. 291(1), pages 605-626, August.
    20. Wei, Chu & Löschel, Andreas & Liu, Bing, 2015. "Energy-saving and emission-abatement potential of Chinese coal-fired power enterprise: A non-parametric analysis," Energy Economics, Elsevier, vol. 49(C), pages 33-43.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1523-:d:322110. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.