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Evaluating Performance of Telecommunication Branch : Application of DEA with Non-Discretionary Factor

통신지사의 성과평가 : 비재량 요인을 포함한 DEA 적용

  • Kwon, Sun-Man (School of Management Consulting, Hanyang University) ;
  • Han, Chang Hee (School of Business Administration, Hanyang University)
  • 권순만 (한양대학교 일반대학원 경영컨설팅학과) ;
  • 한창희 (한양대학교 경상대학 경영학부)
  • Received : 2017.11.20
  • Accepted : 2017.12.11
  • Published : 2017.12.31

Abstract

Improving efficiency of the telecommunication is crucial to the development and growth of Korean economy. Recently, it has become important with the huge development of information technology and its greater potential for extensive impact on the rest of the economy. Hence, it is useful to determine the factors that help enhance efficiency in telecommunication and consider them in improving the evaluation model. This study applies DEA (data envelopment analysis) to evaluate the relative efficiency of 51 branches of a Korean telecommunication company. Using the super-efficiency approach, we tested outliers which may affect the results and ranked the efficient branches. A method of deriving key variables applied to business operation is proposed to identify the key performance indicators for evaluation that takes environmental (non-discretionary) factors into account. We used the extended CCR model proposed by Banker and Morey to investigate the influence of non-discretionary factor. The information provided by the model (slacks, weights) and the sensitivity analysis shows that the most important indicator that affects the branch performance is operating cost. The results of sensitivity analysis show that average efficient score decreases from 0.972 (base case) to 0.863 for CASE2-COST. The average score of the data proves the priority of operating cost over other indicators. The effect of environmental (non-discretionary) variable was found to be significant. The population effect was positive and improved overall efficiency by 0.91% on average. Non-discretionary factor plays a meaningful role explaining the performance of branches. The performance optimization report can help a manager of an inefficient branch to develop branch strategies. Managers can identify the top-performing units, study best practices and adopt the strategy to the organization.

Keywords

References

  1. Avkiran, N., Productivity analysis in the service sector with data envelopment analysis, 2006.
  2. Banker, R.D. and Gifford, J.L., A relative efficiency model for the evaluation of public health nurse productivity, Mellon University Mimeo, Carnegie, 1988.
  3. Banker, R.D. and Morey, R.C., Efficiency analysis for exogenously fixed inputs and outputs, Operations research, 1986, Vol. 34, No. 4, pp. 513-521. https://doi.org/10.1287/opre.34.4.513
  4. Berg, S., Water Utility Benchmarking : Measurement, Methodology, and Performance Incentives, International Water Association, 2010.
  5. Camanho, A.S., Portela, M.C., and Vaz, C.B., Efficiency analysis accounting for internal and external non-discretionary factors, Computers & Operations Research, 2009, Vol. 36, No. 5, pp. 1591-1601. https://doi.org/10.1016/j.cor.2008.03.002
  6. Charnes, A., Cooper, W.W., and Rhodes, E., Measuring the efficiency of decision making units, European Journal of Operational Research, 1978, Vol. 2, No. 6, pp. 429-444. https://doi.org/10.1016/0377-2217(78)90138-8
  7. Cooper, W.W., Park, K.S., and Yu, G., An illustrative application of IDEA (imprecise data envelopment analysis) to a Korean mobile telecommunication company, Operations Research, 2001, Vol. 49, No. 6, pp. 807-820. https://doi.org/10.1287/opre.49.6.807.10022
  8. Cooper, W.W., Seiford, L.M., and Tone, K., Data Envelopment Analysis-A comprehensive Text with Models, Applications, reference and DEA-solver software, New York, Springer, 2007.
  9. Cordero-Ferrera, J.M., Pedraja-Chaparro, F., and Santin- Gonzalez, D., Enhancing the inclusion of non-discretionary inputs in DEA, Journal of the Operational Research Society, 2010, Vol. 61, No. 4, pp. 574-584. https://doi.org/10.1057/jors.2008.189
  10. Giokas, D.I. and Pentzaropoulos, G.C., Efficiency ranking of the OECD member states in the area of telecommunications : A composite AHP/DEA study, Telecommunications Policy, 2008, Vol. 32, No. 9, pp. 672- 685. https://doi.org/10.1016/j.telpol.2008.07.007
  11. Han, Y.J. and Han, C.H., The Performance Evaluation of Universities using DEA and AHP Model, Journal of Society of Korea Industrial and Systems Engineering, 2014, Vol. 37, No. 3, pp. 51-63. https://doi.org/10.11627/jkise.2014.37.3.51
  12. Lam, P.L. and Shiu, A., Productivity analysis of the telecommunications sector in China, Telecommunications Policy, 2008, Vol. 32, No. 8, pp. 559-571. https://doi.org/10.1016/j.telpol.2008.06.004
  13. Lotfi, F.H., Jahanshahloo, G.R., and Esmaeili, M. Nondiscretionary factors and imprecise data in DEA, International Journal of Math Analysis, 2007, Vol. 1, No. 5, pp. 237-246.
  14. Masson, S., Jain, R., Ganesh, N.M., and George, S.A., Operational efficiency and service delivery performance : A comparative analysis of Indian telecom service providers, Benchmarking : An International Journal, 2016, Vol. 23, No. 4, pp. 893-915. https://doi.org/10.1108/BIJ-02-2014-0014
  15. Muniz, M.A., Separating managerial inefficiency and external conditions in data envelopment analysis, European Journal of Operational Research, 2002, Vol. 143, No. 3, pp. 625-643. https://doi.org/10.1016/S0377-2217(01)00344-7
  16. Nigam, V., Thakur, T., Seth, V.K., and Singh, R.P., Benchmarking of Indian mobile telecom operators using DEA with sensitivity analysis, Benchmarking An International Journal, 2012, Vol. 19, No. 2, pp. 219-238. https://doi.org/10.1108/14635771211224545
  17. Pahwa, A., Feng, X., and Lubkeman, D., Performance evaluation of electric distribution utilities based on data envelopment analysis, IEEE Transactions on Power Systems, 2003, Vol. 18, No. 1, pp. 400-405. https://doi.org/10.1109/TPWRS.2002.800986
  18. Scheel, H., EMS : efficiency measurement system user's manual, Operations Research and Witshaftinsformetik, University of Dortmund, Germany, 2000.
  19. Sherman, H.D. and Zhu, J., Analyzing performance in service organizations, MIT Sloan Management Review, 2013, Vol. 54, No. 4, p. 37.
  20. Sherman, H.D. and Zhu, J., Service Productivity Management, Improving Service Performance using Data Envelopment Analysis(DEA), Springer, 2006.
  21. Shin, R. and Ying, J., Costly Gains to Breaking Up : LECs and the Baby Bells, Review of Economics and Statistics, 1993, Vol. 98, pp. 357-361.
  22. Sueyoshi, T., Measuring efficiencies and returns to scale of Nippon Telegraph & Telephone in production and cost analyses, Management Science, 1997, Vol. 43, No. 6, pp. 779-796. https://doi.org/10.1287/mnsc.43.6.779
  23. Tsai, H.C., Chen, C.M., and Tzeng, G.H., The comparative productivity efficiency for global telecoms, International Journal of Production Economics, 2006, Vol. 103, No. 2, pp. 509-526. https://doi.org/10.1016/j.ijpe.2005.11.001
  24. Uri, N.D., Technical efficiency, allocative efficiency, and the implementation of a price cap plan in telecommunications in the United States, Journal of Applied Economics, 2001, Vol. 4, No. 1, pp. 163-186.
  25. Yang, H.H. and Chang, C.Y., Using DEA window analysis to measure efficiencies of Taiwan's integrated telecommunication firms, Telecommunications Policy, 2009, Vol. 33, No. 1, pp. 98-108. https://doi.org/10.1016/j.telpol.2008.11.001

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