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Managerial efficiency in Taiwan bank branches: A network DEA

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  • Yang, Chyan
  • Liu, Hsian-Ming

Abstract

Taiwan's banking industry remains highly fragmented and competitive after a series of financial liberalization and restructuring. With the enforcement of these fiscal policies, domestic banking institutions face a more dynamic, increasingly intense and highly competitive environment even as the banking industry's overall efficiency has gradually been enhanced. This structural change has further forced individual banking institutions, especially state-owned banks, to inspect the performance of their branches and identify improvement directions so as to gain further competitive advantages. To conduct a valid, fair and reliable evaluation on Taiwan's bank branches, we integrate a two-stage series performance model and fuzzy multiobjective model. A new scheme that considers the complementation of production and intermediation activities within a branch and overcomes the shortage of the traditional network DEA methodology about DMUs cannot be assessed on a common base. The results indicate that the overall performances of mixed ownership bank branches are superior to those of state-owned bank branches, representing that the advantages of banking privatization have some remedial effects for improving the managerial inefficiency of state-owned banks. In addition, the sensitivity analysis and decision-making matrix herein help bank management to identify branches' efficiency, weakness, and directions for improvement.

Suggested Citation

  • Yang, Chyan & Liu, Hsian-Ming, 2012. "Managerial efficiency in Taiwan bank branches: A network DEA," Economic Modelling, Elsevier, vol. 29(2), pages 450-461.
  • Handle: RePEc:eee:ecmode:v:29:y:2012:i:2:p:450-461
    DOI: 10.1016/j.econmod.2011.12.004
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    References listed on IDEAS

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    Cited by:

    1. Wu, Yueh-Cheng & Wei Kiong Ting, Irene & Lu, Wen-Min & Nourani, Mohammad & Kweh, Qian Long, 2016. "The impact of earnings management on the performance of ASEAN banks," Economic Modelling, Elsevier, vol. 53(C), pages 156-165.
    2. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    3. Chen, Ping-Chuan & Hung, Shiu-Wan, 2016. "An actor-network perspective on evaluating the R&D linking efficiency of innovation ecosystems," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 303-312.
    4. Lozano, Sebastián, 2016. "Slacks-based inefficiency approach for general networks with bad outputs: An application to the banking sector," Omega, Elsevier, vol. 60(C), pages 73-84.
    5. Mirhedayatian, Seyed Mostafa & Azadi, Majid & Farzipoor Saen, Reza, 2014. "A novel network data envelopment analysis model for evaluating green supply chain management," International Journal of Production Economics, Elsevier, vol. 147(PB), pages 544-554.
    6. Aidan R. VINING & Anthony E. BOARDMAN & Mark A. MOORE, 2014. "The Theory And Evidence Pertaining To Local Government Mixed Enterprises," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 85(1), pages 53-86, March.
    7. Yang, Wei & Shi, Jinfeng & Qiao, Han & Shao, Yanmin & Wang, Shouyang, 2017. "Regional technical efficiency of Chinese Iron and steel industry based on bootstrap network data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 57(C), pages 14-24.
    8. Lu, Wen-Min & Liu, John S. & Kweh, Qian Long & Wang, Chung-Wei, 2016. "Exploring the benchmarks of the Taiwanese investment trust corporations: Management and investment efficiency perspectives," European Journal of Operational Research, Elsevier, vol. 248(2), pages 607-618.
    9. Huang, Tai-Hsin & Lin, Chung-I & Chen, Kuan-Chen, 2017. "Evaluating efficiencies of Chinese commercial banks in the context of stochastic multistage technologies," Pacific-Basin Finance Journal, Elsevier, vol. 41(C), pages 93-110.
    10. Lau, Kwok Hung, 2013. "Measuring distribution efficiency of a retail network through data envelopment analysis," International Journal of Production Economics, Elsevier, vol. 146(2), pages 598-611.
    11. An, Qingxian & Yan, Hong & Wu, Jie & Liang, Liang, 2016. "Internal resource waste and centralization degree in two-stage systems: An efficiency analysis," Omega, Elsevier, vol. 61(C), pages 89-99.
    12. Zha, Yong & Liang, Nannan & Wu, Maoguo & Bian, Yiwen, 2016. "Efficiency evaluation of banks in China: A dynamic two-stage slacks-based measure approach," Omega, Elsevier, vol. 60(C), pages 60-72.
    13. repec:eee:jomega:v:71:y:2017:i:c:p:114-128 is not listed on IDEAS
    14. Amir Moradi-Motlagh & Ali Salman Saleh, 2014. "Re-Examining the Technical Efficiency of Australian Banks: A Bootstrap DEA Approach," Australian Economic Papers, Wiley Blackwell, vol. 53(1-2), pages 112-128, June.
    15. Bolt, Wilko & Humphrey, David, 2015. "A frontier measure of U.S. banking competition," European Journal of Operational Research, Elsevier, vol. 246(2), pages 450-461.
    16. repec:eee:ecmode:v:67:y:2017:i:c:p:23-33 is not listed on IDEAS
    17. Shiyi Chen & Wolfgang K. Härdle & Li Wang, 2014. "Estimation and Determinants of Chinese Banks’ Total Factor Efficiency: A New Vision Based on Unbalanced Development of Chinese Banks and Their Overall Risk," SFB 649 Discussion Papers SFB649DP2014-068, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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