A new approach in the DEA technique for measurement of productivity of decision-making units through efficiency and effectiveness

Document Type : Research Note


1 Department of Industrial Management, Faculty of Management and economics and Accounting, Islamic Azad University, Tabriz Branch, Tabriz, Iran

2 Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, 14348-63111 Tehran, Iran


So far, numerous studies have been developed to evaluate the performance of decision-making units through DEA technique in different places, but most of these studies have measured the performance of decision-making units by efficiency criteria. The productivity is considered as a key factor in the success and development of decision-making units and its evaluation is more comprehensive than efficiency evaluation. Recently, the productivity has been considered in DEA technique. The productivity in these studies is often evaluated through the productivity indexes. These indexes require at least two time periods and also the two important elements of efficiency and effectiveness in these studies are not significantly evident. There are few studies that measure productivity through efficiency and effectiveness. This few researches also measure the efficiency and effectiveness in two stages separately. So, the purpose of this study is to develop a new approach in the DEA technique in order to measure productivity of decision-making units through efficiency and effectiveness simultaneously, in one stage and interdependently. One case study demonstrates application of the proposed approach in the branches of a Bank. Using proposed approach revealed that efficient branches are not necessarily productive, but productive branches are also efficient.


. References
1. Kianfar, K. Ahadzadeh Namin, M. Alam Tabriz, A. et al. “Performance Evaluation of Banking Organizations Using the New Proposed Integrated DEA-BSC Model”, Journal of Modern Processes in Manufacturing and Production, 8(1), pp. 73-90 (2019).
2. Azar, A. Valipour khatir, M. Moghbel Baerz, A. et al. “Evaluation of Hospital Efficiency by Data Envelopment analysis: Tehran University of Medical Sciences: 2009-2011”, journal of health administration, 16(53), pp. 36-46 (2013).
3. Azadi, M. Jafarian, M. Farzipoor Saen, R. et al. “A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context”, Computers & Operations Research, 54, pp. 274-285 (2015).
4. Aduba, J.J. and Asgari, B. “Productivity and technological progress of the Japanese manufacturing industries 2000–2014: estimation with data envelopment analysis and log-linear learning model”, Asia-Pacific Journal of Regional Science, 4(2), pp. 343-387 (2020).
5. Roghanian, P. Rasli, A. and Gheysari H. “Productivity through Effectiveness and Efficiency in the Banking Industry”, Procedia - Social and Behavioral Sciences, 40, pp. 550-556 (2012).
6. Zelenyuk, V. “Aggregation of inputs and outputs prior to Data Envelopment Analysis under big data”, European Journal of Operational Research, 282(1), pp. 172-187 (2020).
7. Fernandes, FDS. Stasinakis, C. and Bardarova, V. “Two-stage DEA-Truncated Regression: Application in banking efficiency and financial development”, Expert Systems with Applications, 96, pp. 284-301 (2018).
8. Kao, C. and Liu, S-T. “Multi-period efficiency measurement in data envelopment analysis: The case of Taiwanese commercial banks”, Omega, 47, pp. 90-98 (2014).
9. Clermont, M. and Schaefer, J. “Identification of Outliers in Data Envelopment Analysis”, Schmalenbach Business Review, 71(4), pp. 475-496 (2019).
10. Falavigna, G. Ippoliti, R. and Ramello, G.B. “DEA-based Malmquist productivity indexes for understanding courts reform”, Socio-Economic Planning Sciences, 62, pp. 31-43 (2018).
11. Gandhi Aradhana, V. and Sharma, D. “Technical efficiency of private sector hospitals in India using data envelopment analysis”, Benchmarking: An International Journal, 25(9), pp. 3570-3591 (2018).
12. Wang, Z. and Feng, c. “Sources of production inefficiency and productivity growth in China: A global data envelopment analysis”, Energy Economics, 49, pp. 380-389 (2015).
13. song, Y. Schubert, T. Liu, H. et al. “Measuring Scientific Productivity in China Using Malmquist Productivity Index”, Journal of Data and Information Science, 4(1), pp. 32–59 (2019).
14. Li, T. Huang, Z. and Drakeford, B.M. “Statistical measurement of total factor productivity under resource and environmental constraints”, National Accounting Review, 1(1), PP. 16–27 (2019).
15. Wang, K and Wei, Y-M. “Sources of energy productivity change in China during 1997–2012: A decomposition analysis based on the Luenberger productivity indicator”, Energy Economics, 54, pp. 50-59 (2016).
16. Lee, C-Y. and Johnson A-L. “Effective production: measuring of the sales effect using data envelopment analysis”, Annals of Operations Research, 235(1), pp. 453-486 (2015).
17. Kao, H-Y. Chan, C-Y. and Wu, D-J. “A multi-objective programming method for solving network DEA”, Applied Soft Computing, 24, pp. 406-413 (2014).
18. Asmild, M. Paradi, J.C. Reese, D.N. et al, “Measuring overall efficiency and effectiveness using DEA”, European Journal of Operational Research, 178(1), pp. 305-321 (2007).
19. Budimir, D. Sostaric, M-I. and Vidovic, K. “Data Envelopment Analysis for Determining the Efficiency of Variant Solutions for Traffic Flow Organization”, Promet-Traffic & Transportation, 31 (3), pp. 341-353 (2019).
20. Azar, A. and Zaree, M. “Improving performance evaluation and resolution Measurement in DEA models by presenting a new model of CSW”, Journal of Management Improvement, 7(2), pp. 99-114 (2013).
21. Nisar Khan, M. Ahmad, A. and Jehan, N. “Pakistani Firms' Efficiency: An Empirical Study of Pakistani Listed Firms through Data Envelopment Analysis”, Global Social Sciences Review (GSSR), 3(3), PP. 158 – 174 (2018).
22. Azar, A. Zaree, M. Moghbel Baerz, A. et al. “Evaluating the Productivity of a Bank's Branches Using Network Data Envelopment Analysis Approach (Case Study: A Bank in Gilan Province)”, Journal of Monetary and Banking Research, 7(20), pp. 285-305 (2014).
23. Farrell, MJ. “The measurement of productive efficiency”, Journal of the Royal Statistical Society, 120(3), pp. 253-281 (1957).
24. Chames, A. Cooper, WW. and Rhodes, E. “Measuring the efficiency of decision making units”, European journal of operational research, 2(6), pp. 429-444 (1978).
25. Banker, RD. Charnes, A. and Cooper WW. “Some models for estimating technical and scale inefficiencies in data envelopment analysis”, Management science, 30(9), pp. 1078-1092 (1984).
26. Alamtabriz, A. and Imanipour, M. “Measuring the Relative Efficiency of Health Care Offered in Hospitals of Shahid Beheshti University of Medical Sciences Using Data Envelopment Analysis (DEA) Technique”, health information management journal, 8(3), pp. 315-325 (2011).
27. Stolzer, AJ. Friend, MA. Truong D. et al. “Measuring and evaluating safety management system effectiveness using Data Envelopment Analysis”, Safety science, 104, pp. 55-69 (2018).
28. Kocisova, K. and Paleckova, I. “The super‑efficiency model and its use for ranking and identification of outliers”, Acta universitatis agriculturae et Silviculturae mendelianae brunensis, 65(4) , pp. 1371-1382 (2017).
29. Visbal-Cadavid, D. Martinez-Gomez, M. and Guijarro, F. “Assessing the Efficiency of Public Universities through DEA: A Case Study”, Sustainability, 9, pp. 14-16 (2017).
30. Tavassoli, M. Faramarzi, G.R. and Farzipoor Saen, R. “Efficiency and effectiveness in airline performance using a SBM-NDEA model in the presence of shared input”, Journal of Air Transport Management, 34, pp. 146-153 (2014).
31. Kumar, N. and Singh, A. “Efficiency Analysis of Banks using DEA: A Review”, International Journal of Advance Research and Innovation, 1, PP. 120-126 (2014).
32. Yang, G. Fukuyama, H. and Song, Y. “Measuring the inefficiency of Chinese research universities based on a two-stage network DEA model”, Journal of Informetrics, 12(1), pp. 10-30 (2018).
33. Noulas Athanasios, G. Glaveli, N. and Kiriakopoulos, I. “Investigating cost efficiency in the branch network of a Greek bank: an empirical study”, Managerial Finance, 34(3), pp. 160-171 (2008).
34. Fujii, H. Managi, S. and Matousek, R. “Indian bank efficiency and productivity changes with undesirable outputs: A disaggregated approach”, Journal of Banking & Finance, 38, pp. 41-50 (2014).
35. Salarieh, M. Mohamadi Nejad, A. and Moghaddasi, R. “Impact of Technological Progress and Efficiency Changes on the Productivity Growth of Iran Agriculture Sector: Data Envelopment Analysis”, Quartery journal of economical modeling, 10(34), pp. 133-148 (2016).
36.  Sadeghi, S.R. Maleki, M.H. and Motaghi, P. “A Two-Stage Dynamic Model for Evaluating the Performance of Private Banks: Using DEA Approach”, Journal of monetary and banking research, 4(35), PP. 83-98 (2018).
37. Fujii, H. Managi, S. Matousek, R. et al. “Bank efficiency, productivity, and convergence in EU countries: a weighted Russell directional distance model”, The European Journal of Finance, 24(2), pp. 135-156 (2018).
38. Kao, C. and Liu, S-T. “Stochastic data envelopment analysis in measuring the efficiency of Taiwan commercial banks”, European Journal of Operational Research, 196(1), pp. 312-322 (2009).
39. Charnes, A. and Cooper, WW. “Programming with linear fractional functionals”, Naval Research logistics quarterly, 9(3‐4), pp. 181-186 (1962).
40. Wang, K. Huang, W. Wu, J. et al. “Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA”, Omega, 44, pp. 5-20 (2014).
41. Wanke, P. Barros, C.P. and Emrouznejad, A. “Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks”, European Journal of Operational Research, 249(1), pp. 378-389 (2016).
42. Yang, C. and Liu, H-M. “Managerial efficiency in Taiwan bank branches: A network DEA”, Economic Modelling, 29(2), pp. 450-461 (2012).
43. Giokas, D.I. “Assessing the efficiency in operations of a large Greek bank branch network adopting different economic behaviors”, Economic Modelling, 25(3), pp. 559-574 (2008).
44. LaPlante, A.E. and Paradi, J.C. “Evaluation of bank branch growth potential using data envelopment analysis”, Omega, 52, pp. 33-41 (2015).
45. Ray, S. “Cost efficiency in an Indian bank branch network: A centralized resource allocation model”, Omega, 65, pp. 69-81 (2016).
46. Jahangir Nia, H. and Esfandiar, M. “Assessment of the efficiency of banks accepted in Tehran Stock Exchange using the data envelopment analysis technique”, Journal of Industrial Strategic Management, 2(3), PP. 1-7 (2018).
47. Wanke, P. Maredza, A. and Gupta, R. “Merger and acquisitions in South African banking: A network DEA model”, Research in International Business and Finance, 41, pp. 362-376 (2017).
48. Zarei Mahmoudabadi, M. “Multilevel Measuring of Efficiency in Banking Industry (Network Slacks-Based Measure (NSBM) Approach)”, journal of industrial Management, 8(3), pp. 359-380 (2016).
49. Cook, WD. and Seiford, LM. “Data envelopment analysis (DEA)–Thirty years on”, European journal of operational research, 192(1), 1-17 (2009).