Input/output indicator selection for DEA efficiency evaluation: An empirical study of Chinese commercial banks

One of the interesting research subjects in DEA is to choose appropriate input and output indicators. In the process, one may encounter many problems, such as the selection tools, correlation analysis, and the classification of input versus output status. In this paper, we propose a new method for choosing DEA variables. Unlike previous research, it is based on the conception of cash value added (CVA), and can make a selection according to the statistic results. This new method has some advantages: first, it is more objective, avoiding the influence of subjective factors on the subsequent calculation; second and most important is that it provides managers and researchers with measurement variables and exact classifications of these factors; third, all variables under discussion come from financial statements which are easily available. This variable selection method has been applied to 14 Chinese commercial banks, and both regression and statistic test results are satisfactory.

[1]  Robert G. Dyson,et al.  A generalisation of the Farrell cost efficiency measure applicable to non-fully competitive settings , 2008 .

[2]  Adi Raveh,et al.  Presenting DEA graphically , 2008 .

[3]  Adi Raveh,et al.  Co-plot: A graphic display method for geometrical representations of MCDM , 2000, Eur. J. Oper. Res..

[4]  K. A. Bubshait,et al.  Evaluation of Bank Branches by Means of Data Envelopment Analysis , 1993 .

[5]  A. Azadeh,et al.  MULTI CRITERIA QUALITY ASSESSMENT OF PRODUCTS BY INTEGRATED DEA-PCA APPROACH , 2007 .

[6]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[7]  Ramalingam Shanmugam,et al.  At a crossroad of data envelopment and principal component analyses , 2007 .

[8]  Pasi Luukka PCA for fuzzy data and similarity classifier in building recognition system for post-operative patient data , 2009, Expert Syst. Appl..

[9]  Rolf Färe,et al.  Modeling undesirable factors in efficiency evaluation: Comment , 2004, Eur. J. Oper. Res..

[10]  Lawrence M. Seiford,et al.  Data envelopment analysis (DEA) - Thirty years on , 2009, Eur. J. Oper. Res..

[11]  J. Beasley Determining Teaching and Research Efficiencies , 1995 .

[12]  M. Oral,et al.  An empirical study on measuring operating efficiency and profitability of bank branches , 1990 .

[13]  Joseph C. Paradi,et al.  Assessing Bank and Bank Branch Performance: Modeling Considerations and Approaches , 2004 .

[14]  Joe Zhu,et al.  Dual-role factors in data envelopment analysis , 2006 .

[15]  S. Wold,et al.  The multivariate calibration problem in chemistry solved by the PLS method , 1983 .

[16]  Tyrone T. Lin,et al.  Application of DEA in analyzing a bank's operating performance , 2009, Expert Syst. Appl..

[17]  Lawrence M. Seiford,et al.  Models for performance benchmarking: Measuring the effect of e-business activities on banking performance , 2004 .

[18]  L. Bove,et al.  Effect of Reciprocity on Well-being in Interpersonal Marketing Relationships: An Interview Study , 2011 .

[19]  William W. Cooper,et al.  Handbook on data envelopment analysis , 2011 .

[20]  T. Du,et al.  Using principal component analysis in process performance for multivariate data , 2000 .

[21]  Allen N. Berger,et al.  Efficiency of Financial Institutions: International Survey and Directions for Future Research , 1997 .

[22]  R. T. Zarinkamar,et al.  Bank branch operating efficiency: evaluation with data envelopment analysis , 2014 .

[23]  W. Cook,et al.  Multicomponent Efficiency Measurement and Shared Inputs in Data Envelopment Analysis: An Application to Sales and Service Performance in Bank Branches , 2000 .

[24]  N. Avkiran Developing foreign bank efficiency models for DEA grounded in finance theory , 2006 .

[25]  R. G. Dyson,et al.  Cost efficiency measurement with price uncertainty: a DEA application to bank branch assessments , 2005, Eur. J. Oper. Res..

[26]  Zhongsheng Hua,et al.  Performance measurement for network DEA with undesirable factors , 2008 .

[27]  C. Molinero,et al.  Microfinance institutions and efficiency , 2007 .

[28]  Manbir S. Sodhi,et al.  Minimizing the expected processing time on a flexible machine with random tool lives , 2006 .

[29]  Joe Zhu,et al.  DUAL ROLE FACTORS IN DEA , 2006 .

[30]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[31]  Joe Zhu,et al.  Benchmarking with quality-adjusted DEA (Q-DEA) to seek lower-cost high-quality service: Evidence from a U.S.bank application , 2006, Ann. Oper. Res..