Advertisement

Journal of Productivity Analysis

, Volume 33, Issue 2, pp 109–123 | Cite as

Adjusting for cultural differences, a new DEA model applied to a merged bank

  • Joseph C. Paradi
  • Sandra A. Vela
  • Haiyan Zhu
Article

Abstract

The usefulness and application of Data Envelopment Analysis (DEA) efficiency measurements is usually limited by the requirement of consistent operating circumstances. However, in many real world situations this is not the case, so to overcome this problem, this paper reports on a new strategy by inventing a Culturally Adjusted DEA model to benchmark business units that operate under different cultural (business) environments. This is especially useful when these environmental factors are partial causes of inefficiency and can not be simply incorporated into a DEA model as inputs or outputs. A simulation analysis is conducted to examine the effectiveness of the CA-DEA model for controlling these environmental effects. This model is applied to a real life efficiency study of two major financial firms in Canada in 2000, when the two entities started to consolidate and merge their branch networks. Two cultural indices are identified to represent a firm’s unique operating environment, one to capture the nature of a firm’s corporate strategies (Corporate Index), and the other to estimate the effectiveness of a firm’s operational systems (Service Index). The results show that a firm’s corporate culture has a significant influence on its branches’ efficiency and this, we found, is often neglected in such studies. This paper also makes a contribution to the bank merger literature by providing an internal view of the potential benefits that may result from sharing cultural advantages while identifying the true managerial inefficiencies.

Keywords

Data envelopment analysis Cultural environment Branch efficiency Bank merger Cross firm comparison Model comparison 

JEL Classification

C51 

References

  1. Arnold V, Bardhan I, Cooper WW (1996) New uses of DEA and statistical regressions for efficiency evaluation and estimation—with an illustrative application to public secondary schools in Texas. Ann Oper Res 66:255–278CrossRefGoogle Scholar
  2. Athanassopoulos AD (1997) Service quality and operating efficiency synergies for management control in the provision of financial services: evidence from Greek bank branches. Eur J Oper Res 98:300–313CrossRefGoogle Scholar
  3. Athanassopoulos AD (1998) Nonparametric frontier models for assessing the market and cost efficiency of large-scale bank branch networks. J Money Credit Banking 30:172–192CrossRefGoogle Scholar
  4. Avkiran NK, Rowlands T (2008) How to better identify the true managerial performance: state of the art using DEA. Omega 36:317–324CrossRefGoogle Scholar
  5. Bala K, Cook WD (2003) Performance measurement with classification information: an enhanced additive DEA model. Omega 31(6):439–450CrossRefGoogle Scholar
  6. Banker RD, Morey R (1986a) Efficiency analysis for exogenously fixed inputs and outputs. Oper Res 34(4):513–521CrossRefGoogle Scholar
  7. Banker RD, Morey R (1986b) The use of categorical variables in data envelopment analysis. Manage Sci 32(12):1613–1627CrossRefGoogle Scholar
  8. Barnum DT, Gleason JM (2008) Bias and precision in the DEA two-stage method. Appl Econ 40(18):2305–2311CrossRefGoogle Scholar
  9. Berger AN, Humphrey DB (1997) Efficiency of financial firms: international survey and directions for future research. Eur J Oper Res 98:175–212CrossRefGoogle Scholar
  10. Bhattacharyya A, Lovell CAK, Sahay P (1997) The impact of liberalization on the productive efficiency of Indian commercial banks. Eur J Oper Res 98:332–345CrossRefGoogle Scholar
  11. Camanho AS, Dyson RG (1999) Efficiency, size, benchmarks and targets for bank branches: an application of data envelopment analysis. J Oper Res Soc 50(9):903–915CrossRefGoogle Scholar
  12. Camanho AS, Dyson RG (2005) Cost efficiency measurement with price uncertainty: a DEA application to bank branch assessments. Eur J Oper Res 161(2):432–446CrossRefGoogle Scholar
  13. Caves DW, Christensen LR, Diewert WE (1982) Multilateral comparisons of output, input, and productivity using superlative index numbers. Econ J 92:73–86CrossRefGoogle Scholar
  14. Coelli TJ, Rao DSP, O’Donnell CJ, Battese GE (2005) An introduction to efficiency and productivity analysis. Springer, New YorkGoogle Scholar
  15. Cook WD, Hababou M, Tuenter HJ (2000) Multicomponent efficiency measurement and shared inputs in data envelopment analysis: an application to sales and service performance in bank branches. J Prod Anal 14:209–224CrossRefGoogle Scholar
  16. Cook WD, Seiford LM, Zhu J (2004) Models for performance benchmarking: measuring the effect of e-business activities on banking performance. Omega 32:313–322CrossRefGoogle Scholar
  17. Dekker D, Post T (2001) A quasi-concave DEA model with an application for bank branch performance evaluation. Eur J Oper Res 132(2):296–311CrossRefGoogle Scholar
  18. Denison DR (1990) Corporate culture and organizational effectiveness. Wiley, New YorkGoogle Scholar
  19. Drake L, Howcroft B (2002) An insight into the size efficiency of a UK bank branch network. Manage Financ 28:24–36Google Scholar
  20. Drake L, Hall MJB, Simper R (2006) The impact of macroeconomic and regulatory factors on bank efficiency: a non-parametric analysis of Hong Kong’s banking system. J Bank Finance 30:1443–1466CrossRefGoogle Scholar
  21. Fixler DJ, Zieschang KD (1993) An index number approach to measuring bank efficiency: an application to mergers. J Bank Finance 17:437–450CrossRefGoogle Scholar
  22. Fizel JL, Hunnikhovern TS (1992) Technical efficiency of for-profit and non-profit nursing homes. Manage Decis Econ 13(5):429–440CrossRefGoogle Scholar
  23. Flamholtz EG, Aksehirli Z (2000) Organizational success and failure: an empirical test of a holistic model. Eur Manage J 18(5):488–498CrossRefGoogle Scholar
  24. Fried HO, Lovell CAK, Eeckaut PV (1993) Evaluating the performance of US credit unions. J Bank Finance 17:251–265CrossRefGoogle Scholar
  25. Fried HO, Lovell CAK, Schmidt SS, Yaisawarng S (2002) Accounting for environmental effects and statistical noise in data envelopment analysis. J Prod Anal 17:157–174CrossRefGoogle Scholar
  26. Giokas DI (2008) Cost efficiency impact of bank branch characteristics and location: an illustrative application to Greek bank branches. Manage Financ 34:172–185Google Scholar
  27. Golany B, Storbeck JE (1999) A data envelopment analysis of the operational efficiency of bank branches. Interfaces 29:14–26CrossRefGoogle Scholar
  28. Harker PT, Zenios SA (2000) Performance of financial firms: efficiency, innovation, regulation. Cambridge University Press, CambridgeGoogle Scholar
  29. Howland M, Rowse J (2006) Measuring bank branch efficiency using data envelopment analysis: managerial and implementation issues. INFOR 44(1):49–63Google Scholar
  30. Kotter JP, Heskett JL (1992) Corporate culture and performance. The Free Press, New YorkGoogle Scholar
  31. Liu J, Tone K (2008) A multistage method to measure efficiency and its application to Japanese banking industry. Socioecon Plann Sci 42:75–91CrossRefGoogle Scholar
  32. Lovell CA, Pastor JT (1997) Target setting: an application to a bank branch network. Eur J Oper Res 98:290–299CrossRefGoogle Scholar
  33. Lozano-Vivas A, Pastor JT, Pastor JM (2002) An efficiency comparison of European banking systems operating under different environmental conditions. J Prod Anal 18:59–77CrossRefGoogle Scholar
  34. McCarty T, Yaisawarng S (1993) Technical efficiency in New Jersey school districts. In: Fried HO, Lovell CAK, Schmidt SS (eds) The measurement of productive efficiency: techniques and applications. Oxford University Press, New YorkGoogle Scholar
  35. Muňiz MA (2002) Separating managerial inefficiency and external conditions in data envelopment analysis. Eur J Oper Res 143:625–643CrossRefGoogle Scholar
  36. Office of the Superintendent of Financial Institutions, Banks’ (2002) Financial data. Website: www.osfi-bsif.gc.ca
  37. Oral M, Yolalan R (1990) An empirical study on measuring operating efficiency and profitability of bank branches. Eur J Oper Res 46:282–294CrossRefGoogle Scholar
  38. Paradi JC, Schaffnit C (2004) Commercial branch performance evaluation and results communication in a Canadian bank—a DEA application. Eur J Oper Res 156(3):719–735CrossRefGoogle Scholar
  39. Parkan C (1987) Measuring the efficiency of service operations: an application to bank branches. Eng Costs Prod Econ 12:237–242CrossRefGoogle Scholar
  40. Pastor JM (2002) Credit risk and efficiency in the European banking system: a three-stage analysis. Appl Financ Econ 1212:895–911CrossRefGoogle Scholar
  41. Podinovski VV, Thanassoulis E (2007) Improving discrimination in data envelopment analysis: some practical suggestions. J Prod Anal 28:117–126CrossRefGoogle Scholar
  42. Portela MCAS, Thanassoulis E (2005) Profitability of a sample of Portuguese bank branches and its decomposition into technical and allocative components. Eur J Oper Res 162:850–866CrossRefGoogle Scholar
  43. Ray SC (1988) Data envelopment analysis nondiscretionary inputs and efficiency: an alternative interpretation. Socioecon Plann Sci 22:167–176CrossRefGoogle Scholar
  44. Rosenthal J, Masarech MA (2003) High-performance cultures: how values can drive business results. J Organ Excell 22(2):3–18CrossRefGoogle Scholar
  45. Roth AV, Jackson WE (1995) Strategic determinants of service quality and performance: evidence from the banking industry. Manage Sci 41(11):1720–1733CrossRefGoogle Scholar
  46. Ruggiero J (1998) Non-discretionary inputs in data envelopment analysis. Eur J Oper Res 111:461–469CrossRefGoogle Scholar
  47. Ruggiero J (2004) Performance evaluation when non-discretionary factors correlate with technical efficiency. Eur J Oper Res 159:250–257CrossRefGoogle Scholar
  48. Schaffnit C, Rosen D, Paradi JC (1997) Best practice analysis of bank branches: an application of DEA in a large Canadian bank. Eur J Oper Res 98(2):269–289CrossRefGoogle Scholar
  49. Sherman HD, Gold F (1985) Bank branch operating efficiency: evaluation with data envelopment analysis. J Bank Finance 9(2):297–316CrossRefGoogle Scholar
  50. Sherman HD, Rupert TJ (2006) Do bank mergers have hidden or foregone value? Realized and unrealized operating synergies in one bank merger. Eur J Oper Res 168(1):253–268CrossRefGoogle Scholar
  51. Sherman HD, Zhu J (2006) Benchmarking with quality-adjusted DEA (Q-DEA) to seek lower-cost high-quality service: evidence from a US bank application. Ann Oper Res 145(1):301–319CrossRefGoogle Scholar
  52. Simar L, Wilson PW (2007) Estimation and inference in two-stage, semi-parametric models of production processes. J Econom 136:31–64CrossRefGoogle Scholar
  53. Sowlati T, Paradi JC (2004) Establishing the “practical frontier” in data envelopment analysis. Omega 32(4):261–272CrossRefGoogle Scholar
  54. Timmer CP (1971) Using a probabilistic frontier production function to measure technical efficiency. J Polit Econ 79(4):776–794CrossRefGoogle Scholar
  55. Wade D (2005) Building performance standards into data envelopment analysis structures. IIE Trans 37:267–275CrossRefGoogle Scholar
  56. Wu D, Yang Z, Liang L (2006) Efficiency analysis of cross-region bank branches using fuzzy data envelopment analysis. Appl Math Comput 181:271–281CrossRefGoogle Scholar
  57. Yang Z, Paradi JC (2006) Cross firm bank branch benchmarking using “Handicapped” data envelopment analysis to adjust for corporate strategic effects. In: Proceedings of the 39th Hawaii international conference on systems sciences, pp 1–10Google Scholar
  58. Zenios CV, Zenios SA, Agathocleous K (1999) Benchmarks of the efficiency of bank branches. Interfaces 29(3):37–51CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Joseph C. Paradi
    • 1
  • Sandra A. Vela
    • 1
  • Haiyan Zhu
    • 1
  1. 1.Centre for Management of Technology and Entrepreneurship, Faculty of Applied Science and EngineeringUniversity of TorontoTorontoCanada

Personalised recommendations