Advertisement

A novel multi-criteria analysis model for the performance evaluation of bank regions: an application to Turkish agricultural banking

  • Fatih TüysüzEmail author
  • Nurdan Yıldız
Methodologies and Application
  • 20 Downloads

Abstract

The banks serve in a highly dynamic and competitive environment and need to systematically evaluate their performance to improve their competitiveness. Performance evaluation is an important and complex process that requires flexible and analytic methods while handling the multidimensionality of the problem. This study presents a hybrid multi-criteria performance evaluation model for banking sector which combines two multi-criteria decision making methods that are simulation-integrated hesitant fuzzy linguistic term sets-based analytic hierarchy process method to determine the importance level of each criterion according to the decision makers’ subjective judgements and grey relational analysis method to rank bank regions according to their performance values. The presented model is based on both probability theory and fuzzy sets theory and thus better represents all the dimensions of the uncertainty inherent in decision making process. A real-life application of the proposed performance evaluation model for a private bank operating in agricultural banking sector in Turkey is also given to illustrate the effectiveness and the applicability of the model.

Keywords

Banking Performance evaluation Simulation Hesitant fuzzy sets AHP GRA 

Notes

Compliance with ethical standards

Conflict of interest

Fatih Tüysüz declares that he has no conflict of interest. Nurdan Yıldız declares that she has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Ahn H, Le MH (2014) An insight into the specification of the input–output set for DEA-based bank efficiency measurement. Manag Rev Q 64(1):3–37CrossRefGoogle Scholar
  2. Aiello F, Bonanno G (2016) Efficiency in banking: a meta-regression analysis. Int Rev Appl Econ 30(1):112–149CrossRefGoogle Scholar
  3. Aigner D, Lovell CK, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econ 6(1):21–37MathSciNetzbMATHCrossRefGoogle Scholar
  4. Albayrak YE, Erkut H (2005) Banka performans değerlendirmede analitik hiyerarşi süreç yaklaşımı. İTÜDERGİSİ/d 4(6):47–58 (in Turkish) Google Scholar
  5. An Q, Chen H, Wu J, Liang L (2015) Measuring slacks-based efficiency for commercial banks in China by using a two-stage DEA model with undesirable output. Ann Oper Res 235(1):13–35MathSciNetzbMATHCrossRefGoogle Scholar
  6. Atanassov K (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96MathSciNetzbMATHCrossRefGoogle Scholar
  7. Avkiran NK (2015) An illustration of dynamic network DEA in commercial banking including robustness tests. Omega 55:141–150CrossRefGoogle Scholar
  8. Aydogan EK (2011) Performance measurement model for Turkish aviation firms using the rough-AHP and TOPSIS methods under fuzzy environment. Expert Syst Appl 38(4):3992–3998CrossRefGoogle Scholar
  9. Bai C, Dhavale D, Sarkis J (2014) Integrating fuzzy C-means and TOPSIS for performance evaluation: an application and comparative analysis. Expert Syst Appl 41(9):4186–4196CrossRefGoogle Scholar
  10. Bentes AV, Carneiro J, da Silva JF, Kimura H (2012) Multidimensional assessment of organizational performance: Integrating BSC and AHP. J Bus Res 65(12):1790–1799CrossRefGoogle Scholar
  11. Berger AN (1993) “Distribution-free” estimates of efficiency in the US banking industry and tests of the standard distributional assumptions. J Prod Anal 4(3):261–292CrossRefGoogle Scholar
  12. Berger AN, Di Patti EB (2006) Capital structure and firm performance: a new approach to testing agency theory and an application to the banking industry. J Bank Finance 30(4):1065–1102CrossRefGoogle Scholar
  13. Berger AN, Humphrey DB (1992) Measurement and efficiency issues in commercial banking. In: Griliches Z (ed) Output measurement in the service sectors. University of Chicago Press, Chicago, pp 245–300Google Scholar
  14. Berger AN, Humphrey DB (1997) Efficiency of financial institutions: international survey and directions for future research. Eur J Oper Res 98(2):175–212zbMATHCrossRefGoogle Scholar
  15. Berger AN, Hancock D, Humphrey DB (1993) Bank efficiency derived from the profit function. J Bank Finance 17(2):317–347CrossRefGoogle Scholar
  16. Bolt W, Humphrey D (2010) Bank competition efficiency in Europe: a frontier approach. J Bank Finance 34(8):1808–1817CrossRefGoogle Scholar
  17. Büyüközkan G, Karabulut Y (2017) Energy project performance evaluation with sustainability perspective. Energy 119:549–560CrossRefGoogle Scholar
  18. Çelen A, Yalçın N (2012) Performance assessment of Turkish electricity distribution utilities: an application of combined FAHP/TOPSIS/DEA methodology to incorporate quality of service. Util Policy 23:59–71CrossRefGoogle Scholar
  19. Chantapong S (2005) Comparative study of domestic and foreign bank performance in Thailand: the regression analysis. Econ Change Restruct 38(1):63–83CrossRefGoogle Scholar
  20. Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444MathSciNetzbMATHCrossRefGoogle Scholar
  21. Chen FH, Hsu TS, Tzeng GH (2011) A balanced scorecard approach to establish a performance evaluation and relationship model for hot spring hotels based on a hybrid MCDM model combining DEMATEL and ANP. Int J Hosp Manag 30(4):908–932CrossRefGoogle Scholar
  22. Chithambaranathan P, Subramanian N, Gunasekaran A, Palaniappan PK (2015) Service supply chain environmental performance evaluation using grey based hybrid MCDM approach. Int J Prod Econ 166:163–176CrossRefGoogle Scholar
  23. Chiu YH, Luo Z, Chen YC, Wang Z, Tsai MP (2013) A comparison of operating performance management between Taiwan banks and foreign banks based on the meta-hybrid DEA model. Econ Model 33:433–439CrossRefGoogle Scholar
  24. Cook WD, Ramón N, Ruiz JL, Sirvent I, Zhu J (2019) DEA-based benchmarking for performance evaluation in pay-for-performance incentive plans. Omega 84:45–54CrossRefGoogle Scholar
  25. Daly S, Frikha M (2017) Determinants of bank performance: comparative study between conventional and Islamic banking in Bahrain. J Knowl Econ 8(2):471–488CrossRefGoogle Scholar
  26. Das MC, Sarkar B, Ray S (2012) A framework to measure relative performance of Indian technical institutions using integrated fuzzy AHP and COPRAS methodology. Socio-Econ Plan Sci 46(3):230–241CrossRefGoogle Scholar
  27. Delen D, Kuzey C, Uyar A (2013) Measuring firm performance using financial ratios: a decision tree approach. Expert Syst Appl 40(10):3970–3983CrossRefGoogle Scholar
  28. Demir Y, Astarcıoğlu M (2007) Determining bank performance via financial prediction: an application in ISE. Suleyman Demirel University. J Bus Adm Econ Fac 12(1):273–292Google Scholar
  29. Deng JL (1982) Control problems of grey systems. Syst Control Lett 1(5):288–294MathSciNetzbMATHCrossRefGoogle Scholar
  30. Dey PK, Cheffi W (2013) Green supply chain performance measurement using the analytic hierarchy process: a comparative analysis of manufacturing organisations. Prod Plan Control 24(8–9):702–720CrossRefGoogle Scholar
  31. Dinçer H, Görener A (2011) Analitik Hiyerarşi Süreci ve VIKOR Tekniği ile Dinamik Performans Analizi: Bankacılık Sektöründe Bir Uygulama. Istanb Ticaret Üniv Sos Bilimler Derg 10(19):109–127 (in Turkish) Google Scholar
  32. dos Santos BM, Godoy LP, Campos LM (2019) Performance evaluation of green suppliers using entropy-TOPSIS-F. J Clean Prod 207:498–509CrossRefGoogle Scholar
  33. Duman GM, Tozanli O, Kongar E, Gupta SM (2017) A holistic approach for performance evaluation using quantitative and qualitative data: a food industry case study. Expert Syst Appl 81:410–422CrossRefGoogle Scholar
  34. Emrouznejad A, Tavana M, Hatami-Marbini A (2014) The state of the art in fuzzy data envelopment analysis. In: Emrouznejad A, Tavana M (eds) Performance measurement with fuzzy data envelopment analysis. Springer, Berlin, Heidelberg, pp 1–45CrossRefzbMATHGoogle Scholar
  35. Ertuğrul İ, Karakaşoğlu N (2009) Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Syst Appl 36(1):702–715CrossRefGoogle Scholar
  36. Färe R, Grosskopf S (2000) Network dea. Socio-Econ Plan Sci 34(1):35–49zbMATHCrossRefGoogle Scholar
  37. Fu HP, Chang TH, Shieh L, Lin A, Lin SW (2015) Applying DEA–BPN to enhance the explanatory power of performance measurement. Syst Res Behav Sci 32(6):707–720CrossRefGoogle Scholar
  38. Fukuyama H, Matousek R (2017) Modelling bank performance: a network DEA approach. Eur J Oper Res 259(2):721–732MathSciNetzbMATHCrossRefGoogle Scholar
  39. Fukuyama H, Weber WL (2015) Measuring Japanese bank performance: a dynamic network DEA approach. J Prod Anal 44(3):249–264CrossRefGoogle Scholar
  40. Fukuyama H, Weber WL (2017) Japanese bank productivity, 2007–2012: a dynamic network approach. Pac Econ Rev 22:649–676CrossRefGoogle Scholar
  41. Garibaldi JM, Ozen T (2007) Uncertain fuzzy reasoning: a case study in modelling expert decision making. IEEE Trans Fuzzy Syst 15(1):16–30CrossRefGoogle Scholar
  42. Gil-Alana LA, Barros C, Mandlaze D (2017) A performance assessment of Mozambique banks: a Bayesian stochastic frontier. Appl Econ 49(45):4579–4587CrossRefGoogle Scholar
  43. Görener A, Ayvaz B, Kuşakcı AO, Altınok E (2017) A hybrid type-2 fuzzy based supplier performance evaluation methodology: the Turkish Airlines technic case. Appl Soft Comput 56:436–445CrossRefGoogle Scholar
  44. Goyal S, Grover S (2013) Manufacturing system’s effectiveness measurement by using combined approach of ANP and GTMA. Int J Syst Assur Eng Manag 4(4):404–423CrossRefGoogle Scholar
  45. Grifell-Tatjé E, Marques-Gou P (2008) Internal performance evaluation: the case of bank branches. Int J Serv Ind Manag 19(3):302–324CrossRefGoogle Scholar
  46. Gürbüz T, Albayrak YE (2014) An engineering approach to human resources performance evaluation: hybrid MCDM application with interactions. Appl Soft Comput 21:365–375CrossRefGoogle Scholar
  47. Haghighi SM, Torabi SA, Ghasemi R (2016) An integrated approach for performance evaluation in sustainable supply chain networks (with a case study). J Clean Prod 137:579–597CrossRefGoogle Scholar
  48. Han H, Trimi S (2018) A fuzzy TOPSIS method for performance evaluation of reverse logistics in social commerce platforms. Expert Syst Appl 103:133–145CrossRefGoogle Scholar
  49. Ho CTB, Wu DD (2009) Online banking performance evaluation using data envelopment analysis and principal component analysis. Comput Oper Res 36(6):1835–1842zbMATHCrossRefGoogle Scholar
  50. Hsieh LF, Lin LH (2010) A performance evaluation model for international tourist hotels in Taiwan—an application of the relational network DEA. Int J Hosp Manag 29(1):14–24CrossRefGoogle Scholar
  51. Jain S, Triantis KP, Liu S (2011) Manufacturing performance measurement and target setting: a data envelopment analysis approach. Eur J Oper Res 214(3):616–626CrossRefGoogle Scholar
  52. Jyoti, Banwet DK, Deshmukh SG (2008) Evaluating performance of national R&D organizations using integrated DEA-AHP technique. Int J Product Perform Manag 57(5):370–388CrossRefGoogle Scholar
  53. Kahraman C (2018) A special issue on extensions of fuzzy sets in decision-making. Soft Comput 22(15):4851–4853zbMATHCrossRefGoogle Scholar
  54. Kahraman C, Onar SC, Oztaysi B (2015) Fuzzy multicriteria decision-making: a literature review. Int J Comput Intell Syst 8(4):637–666zbMATHCrossRefGoogle Scholar
  55. Kahraman C, Öztayşi B, Çevik Onar S (2016) A comprehensive literature review of 50 years of fuzzy set theory. Int J Comput Intell Syst 9(sup1):3–24CrossRefGoogle Scholar
  56. Kalogeras N, Baourakis G, Zopounidis C, van Dijk G (2005) Evaluating the financial performance of agri-food firms: a multicriteria decision-aid approach. J Food Eng 70(3):365–371CrossRefGoogle Scholar
  57. Kumar V (2016) Evaluating the financial performance and financial stability of national commercial banks in the UAE. Int J Bus Glob 16(2):109–128CrossRefGoogle Scholar
  58. Kuo MS, Liang GS (2012) A soft computing method of performance evaluation with MCDM based on interval-valued fuzzy numbers. Appl Soft Comput 12(1):476–485CrossRefGoogle Scholar
  59. Lang G, Welzel P (1998) Technology and cost efficiency in universal banking a “Thick Frontier”-analysis of the German banking ındustry. J Prod Anal 10(1):63–84CrossRefGoogle Scholar
  60. Lee ZY, Pai CC (2011) Operation analysis and performance assessment for TFT-LCD manufacturers using improved DEA. Expert Syst Appl 38(4):4014–4024CrossRefGoogle Scholar
  61. Lee AH, Chen WC, Chang CJ (2008) A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan. Expert Syst Appl 34(1):96–107CrossRefGoogle Scholar
  62. Lensink R, Meesters A (2014) Institutions and bank performance: a stochastic frontier analysis. Oxf Bull Econ Stat 76(1):67–92CrossRefGoogle Scholar
  63. Li N, Zhao H (2016) Performance evaluation of eco-industrial thermal power plants by using fuzzy GRA-VIKOR and combination weighting techniques. J Clean Prod 135:169–183CrossRefGoogle Scholar
  64. Li H, Chen C, Cook WD, Zhang J, Zhu J (2018) Two-stage network DEA: who is the leader? Omega 74:15–19CrossRefGoogle Scholar
  65. Lin TY, Chiu SH (2013) Using independent component analysis and network DEA to improve bank performance evaluation. Econ Model 32:608–616CrossRefGoogle Scholar
  66. Matthews K (2013) Risk management and managerial efficiency in Chinese banks: a network DEA framework. Omega 41(2):207–215CrossRefGoogle Scholar
  67. Mercan M, Reisman A, Yolalan R, Emel AB (2003) The effect of scale and mode of ownership on the financial performance of the Turkish banking sector: results of a DEA-based analysis. Socio-Econ Plan Sci 37(3):185–202CrossRefGoogle Scholar
  68. Modak M, Pathak K, Ghosh KK (2017) Performance evaluation of outsourcing decision using a BSC and fuzzy AHP approach: a case of the Indian coal mining organization. Resour Policy 52:181–191CrossRefGoogle Scholar
  69. Moghimi R, Anvari A (2014) An integrated fuzzy MCDM approach and analysis to evaluate the financial performance of Iranian cement companies. Int J Adv Manuf Technol 71(1–4):685–698CrossRefGoogle Scholar
  70. Nguyen TPT, Nghiem SH, Roca E, Sharma P (2016) Bank reforms and efficiency in Vietnamese banks: evidence based on SFA and DEA. Appl Econ 48(30):2822–2835CrossRefGoogle Scholar
  71. Omrani H, Beiragh RG, Kaleibari SS (2015) Performance assessment of Iranian electricity distribution companies by an integrated cooperative game data envelopment analysis principal component analysis approach. Int J Electr Power Energy Syst 64:617–625CrossRefGoogle Scholar
  72. Önder E, Taş N, Hepsen A (2013) Performance evaluation of Turkish banks using analytical hierarchy process and TOPSIS methods. J Int Sci Publ Econ Bus 7(Part 1):470–503Google Scholar
  73. Oral M, Yolalan R (1990) An empirical study on measuring operating efficiency and profitability of bank branches. Eur J Oper Res 46(3):282–294CrossRefGoogle Scholar
  74. Ozcan T, Tuysuz F (2016) Modified grey relational analysis integrated with grey dematel approach for the performance evaluation of retail stores. Int J Inf Technol Decis Mak 15(02):353–386CrossRefGoogle Scholar
  75. Özceylan E, Çetinkaya C, Erbaş M, Kabak M (2016) Logistic performance evaluation of provinces in Turkey: a GIS-based multi-criteria decision analysis. Transp Res Part A Policy Pract 94:323–337CrossRefGoogle Scholar
  76. Öztayşi B, Kaya T, Kahraman C (2011) Performance comparison based on customer relationship management using analytic network process. Expert Syst Appl 38(8):9788–9798CrossRefGoogle Scholar
  77. Parkan C, Wu ML (1999) Measurement of the performance of an investment bank using the operational competitiveness rating procedure. Omega 27(2):201–217CrossRefGoogle Scholar
  78. Piltan M, Sowlati T (2016) A multi-criteria decision support model for evaluating the performance of partnerships. Expert Syst Appl 45:373–384CrossRefGoogle Scholar
  79. Rabbani A, Zamani M, Yazdani-Chamzini A, Zavadskas EK (2014) Proposing a new integrated model based on sustainability balanced scorecard (SBSC) and MCDM approaches by using linguistic variables for the performance evaluation of oil producing companies. Expert Syst Appl 41(16):7316–7327CrossRefGoogle Scholar
  80. Rangan N, Grabowski R, Aly HY, Pasurka C (1988) The technical efficiency of US banks. Econ Lett 28(2):169–175CrossRefGoogle Scholar
  81. Rezaie K, Ramiyani SS, Nazari-Shirkouhi S, Badizadeh A (2014) Evaluating performance of Iranian cement firms using an integrated fuzzy AHP–VIKOR method. Appl Math Model 38(21):5033–5046MathSciNetzbMATHCrossRefGoogle Scholar
  82. Rodríguez RM, Martinez L, Herrera F (2012) Hesitant fuzzy linguistic term sets for decision making. IEEE Trans Fuzzy Syst 20(1):109–119CrossRefGoogle Scholar
  83. Rushton A, Croucher P, Baker P (2014) The handbook of logistics and distribution management: understanding the supply chain, 5th edn. Kogan Page Publishers, The Chartered Institute of Logistics and TransportGoogle Scholar
  84. Saleh H, Malkhalifeh MR (2013) Performance evaluation in bank branch with two-stage DEA model. Shiraz J Syst Manag 1(1):17–33Google Scholar
  85. Salimi N, Rezaei J (2018) Evaluating firms’ R&D performance using best worst method. Eval Progr Plan 66:147–155CrossRefGoogle Scholar
  86. Saranga H, Moser R (2010) Performance evaluation of purchasing and supply management using value chain DEA approach. Eur J Oper Res 207(1):197–205CrossRefGoogle Scholar
  87. Seçme NY, Bayrakdaroğlu A, Kahraman C (2009) Fuzzy performance evaluation in Turkish banking sector using analytic hierarchy process and TOPSIS. Expert Syst Appl 36(9):11699–11709CrossRefGoogle Scholar
  88. Seiford LM, Zhu J (1999) Profitability and marketability of the top 55 US commercial banks. Manage Sci 45(9):1270–1288CrossRefGoogle Scholar
  89. Sengupta JK (1992) A fuzzy systems approach in data envelopment analysis. Comput Math Appl 24(8–9):259–266MathSciNetzbMATHCrossRefGoogle Scholar
  90. Shafiee M, Sangi M, Ghaderi M (2013) Bank performance evaluation using dynamic DEA: a slacks-based measure approach. J Data Envel Anal Decis Sci 2013(26):1–12Google Scholar
  91. Shafiee M, Lotfi FH, Saleh H, Ghaderi M (2016) A mixed integer bi-level DEA model for bank branch performance evaluation by Stackelberg approach. J Ind Eng Int 12(1):81–91CrossRefGoogle Scholar
  92. Shahroudi K, Assimi S (2012) Performance evaluation of banks using DEA (Case study: Guilan Saderat Bank Branches). Iran J Optim 4(2):375–387Google Scholar
  93. Shaik MN, Abdul-Kader W (2014) Comprehensive performance measurement and causal-effect decision making model for reverse logistics enterprise. Comput Ind Eng 68:87–103CrossRefGoogle Scholar
  94. Shaverdi M, Heshmati MR, Ramezani I (2014) Application of fuzzy AHP approach for financial performance evaluation of Iranian petrochemical sector. Proc Comput Sci 31:995–1004CrossRefGoogle Scholar
  95. Silva TC, Tabak BM, Cajueiro DO, Dias MVB (2017) A comparison of DEA and SFA using micro-and macro-level perspectives: efficiency of Chinese local banks. Phys A 469:216–223CrossRefGoogle Scholar
  96. Sokic A (2015) Cost efficiency of the banking industry and unilateral euroisation: a stochastic frontier approach in Serbia and Montenegro. Econ Syst 39(3):541–551CrossRefGoogle Scholar
  97. Srinivasan R, Jain V, Dharmaraja S (2019) Perception based performance analysis of higher education institutions: a soft computing approach. Soft Comput.  https://doi.org/10.1007/s00500-019-03931-6 CrossRefGoogle Scholar
  98. Sun CC (2010) A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Syst Appl 37(12):7745–7754CrossRefGoogle Scholar
  99. Tavana M, Khalili-Damghani K, Rahmatian R (2015) A hybrid fuzzy MCDM method for measuring the performance of publicly held pharmaceutical companies. Ann Oper Res 226(1):589–621MathSciNetzbMATHCrossRefGoogle Scholar
  100. Thanassoulis E, Boussofiane A, Dyson RG (1996) A comparison of data envelopment analysis and ratio analysis as tools for performance assessment. Omega 24(3):229–244CrossRefGoogle Scholar
  101. Titko J, Stankevičienė J, Lāce N (2014) Measuring bank efficiency: DEA application. Technol Econ Dev Econ 20(4):739–757CrossRefGoogle Scholar
  102. Torra V (2010) Hesitant fuzzy sets. Int J Intell Syst 25(6):529–539zbMATHGoogle Scholar
  103. Torra V, Narukawa Y (2009) On hesitant fuzzy sets and decision. In: IEEE international conference on fuzzy systems, 2009. FUZZ-IEEE 2009. IEEE, pp 1378–1382Google Scholar
  104. Tözüm H (2002) Performance evaluation of banks. Act J Bank Finance 27:1–9Google Scholar
  105. Tsai HY, Chang CW, Lin HL (2010) Fuzzy hierarchy sensitive with Delphi method to evaluate hospital organization performance. Expert Syst Appl 37(8):5533–5541CrossRefGoogle Scholar
  106. Tseng ML (2010) Implementation and performance evaluation using the fuzzy network balanced scorecard. Comput Educ 55(1):188–201CrossRefGoogle Scholar
  107. Tuysuz F (2018) Simulated hesitant fuzzy linguistic term sets based approach for modeling uncertainty in AHP method. Int J Inf Technol Decis Mak.  https://doi.org/10.1142/S0219622018500074 CrossRefGoogle Scholar
  108. Tüysüz F, Şimşek B (2017) A hesitant fuzzy linguistic term sets-based AHP approach for analyzing the performance evaluation factors: an application to cargo sector. Complex Intell Syst 3(3):167–175CrossRefGoogle Scholar
  109. Uygun Ö, Dede A (2016) Performance evaluation of green supply chain management using integrated fuzzy multi-criteria decision making techniques. Comput Ind Eng 102:502–511CrossRefGoogle Scholar
  110. Varmazyar M, Dehghanbaghi M, Afkhami M (2016) A novel hybrid MCDM model for performance evaluation of research and technology organizations based on BSC approach. Eval Progr Plan 58:125–140CrossRefGoogle Scholar
  111. Vincent FY, Hu KJ (2010) An integrated fuzzy multi-criteria approach for the performance evaluation of multiple manufacturing plants. Comput Ind Eng 58(2):269–277CrossRefGoogle Scholar
  112. Waemustafa W, Sukri S (2015) Bank specific and macroeconomics dynamic determinants of credit risk in Islamic banks and conventional banks. Int J Econ Financ Issues 5(2):476–481Google Scholar
  113. Wang RT, Ho CTB, Oh K (2010) Measuring production and marketing efficiency using grey relation analysis and data envelopment analysis. Int J Prod Res 48(1):183–199zbMATHCrossRefGoogle Scholar
  114. Wang K, Huang W, Wu J, Liu YN (2014) Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega 44:5–20CrossRefGoogle Scholar
  115. Wanke P, Barros C (2014) Two-stage DEA: an application to major Brazilian banks. Expert Syst Appl 41(5):2337–2344CrossRefGoogle Scholar
  116. Wanke P, Barros CP, Emrouznejad A (2016) Assessing productive efficiency of banks using integrated fuzzy-DEA and bootstrapping: a case of Mozambican banks. Eur J Oper Res 249(1):378–389zbMATHCrossRefGoogle Scholar
  117. Wei GW (2011) Grey relational analysis method for 2-tuple linguistic multiple attribute group decision making with incomplete weight information. Expert Syst Appl 38(5):4824–4828CrossRefGoogle Scholar
  118. Wu D, Dash Wu D (2010) Performance evaluation and risk analysis of online banking service. Kybernetes 39(5):723–734CrossRefGoogle Scholar
  119. Wu HY, Tzeng GH, Chen YH (2009) A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard. Expert Syst Appl 36(6):10135–10147CrossRefGoogle Scholar
  120. Wu CR, Lin CT, Tsai PH (2010) Evaluating business performance of wealth management banks. Eur J Oper Res 207(2):971–979CrossRefGoogle Scholar
  121. Wu HY, Lin YK, Chang CH (2011) Performance evaluation of extension education centers in universities based on the balanced scorecard. Eval Progr Plan 34(1):37–50CrossRefGoogle Scholar
  122. Wu HY, Chen JK, Chen IS, Zhuo HH (2012a) Ranking universities based on performance evaluation by a hybrid MCDM model. Measurement 45(5):856–880CrossRefGoogle Scholar
  123. Wu HY, Chen J, Chen I (2012b) Performance evaluation of aircraft maintenance staff using a fuzzy MCDM approach. Int J Innov Comput Inf Control 8:3919–3937Google Scholar
  124. Xu Z, Xia M (2011) Distance and similarity measures for hesitant fuzzy sets. Inf Sci 181(11):2128–2138MathSciNetzbMATHCrossRefGoogle Scholar
  125. Yager RR (1986) On the theory of bags. Int J Gen Syst 13(1):23–37MathSciNetCrossRefGoogle Scholar
  126. Yaghoobi T, Haddadi F (2016) Organizational performance measurement by a framework integrating BSC and AHP. Int J Prod Perform Manag 65(7):959–976CrossRefGoogle Scholar
  127. Yalcin N, Bayrakdaroglu A, Kahraman C (2012) Application of fuzzy multi-criteria decision making methods for financial performance evaluation of Turkish manufacturing industries. Expert Syst Appl 39(1):350–364CrossRefGoogle Scholar
  128. Yang C, Liu HM (2012) Managerial efficiency in Taiwan bank branches: a network DEA. Econ Model 29(2):450–461CrossRefGoogle Scholar
  129. Yang CL, Chuang SP, Huang RH (2009) Manufacturing evaluation system based on AHP/ANP approach for wafer fabricating industry. Expert Syst Appl 36(8):11369–11377CrossRefGoogle Scholar
  130. Yang JB, Wong BYH, Xu DL, Liu XB, Steuer RE (2010) Integrated bank performance assessment and management planning using hybrid minimax reference point–DEA approach. Eur J Oper Res 207(3):1506–1518MathSciNetzbMATHCrossRefGoogle Scholar
  131. Yıldız N, Tüysüz F (2018) A hybrid multi-criteria decision making approach for strategic retail location investment: application to Turkish food retailing. Socio-Econ Plan Sci.  https://doi.org/10.1016/j.seps.2018.02.006 CrossRefGoogle Scholar
  132. Yılmaz AA (2013) Bank efficiency analysis in Turkish banking system. In: WEU International Academic Conference Proceedings, pp 112–121Google Scholar
  133. Yurdakul M, Ic YT (2005) Development of a performance measurement model for manufacturing companies using the AHP and TOPSIS approaches. Int J Prod Res 43(21):4609–4641CrossRefGoogle Scholar
  134. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353zbMATHCrossRefGoogle Scholar
  135. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning—I. Inf Sci 8(3):199–249MathSciNetzbMATHCrossRefGoogle Scholar
  136. Zaim O (1995) The effect of financial liberalization on the efficiency of Turkish commercial banks. Appl Financ Econ 5(4):257–264CrossRefGoogle Scholar
  137. Zeydan M, Çolpan C, Çobanoğlu C (2011) A combined methodology for supplier selection and performance evaluation. Expert Syst Appl 38(3):2741–2751CrossRefGoogle Scholar
  138. Zhou L, Li H, Sun K (2017) Teaching performance evaluation by means of a hierarchical multifactorial evaluation model based on type-2 fuzzy sets. Appl Intell 46(1):34–44CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Industrial EngineeringIstanbul University - CerrahpaşaAvcılar, IstanbulTurkey
  2. 2.Department of Industrial EngineeringIstanbul Gelisim UniversityAvcılar, IstanbulTurkey

Personalised recommendations