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Year 2015, Volume: 10 Issue: 3, 120 - 134, 10.07.2015

AŞIR Özbek

Abstract

The effective and efficient operation of deposit money banks, which are the most basic institutions of the financial system, is of great importance in terms of the profitability of the banking sector and the economy of the country. Banks are financial institutions that mediate the conversion of savings into investment. There is a very serious competition among banks serving in the financial sector. Those that increase their efficiency by continuously improving the service quality will be always one step ahead in this race. Competitiveness Operational Rating (OCRA) is a relatively new method that can be quite useful in measuring the relative efficiencies in decision making units by using similar input to produce similar output. This study aims to develop a model for efficiency measurement based on OCRA to measure the effectiveness of the 32 commercial banks operating in Turkey using data for the years 2011-2014 taken from the “Book for Banks”. Input and output factors used in the model were determined according the results of a literature study. If you look at every four years, it has been established that YK showed the highest performance

Keywords

Competitiveness Operational Rating OCRA Efficiency

References

  • Akkoç, S. and Vatansever, K., (2013). Fuzzy performance evaluation with AHP and TOPSIS methods: evidence for Turkish banking sector after the global financial crisis. Eurasian J. Bus. Econ. Vol:6, Nr:11, pp:53–74 (Available at: http://www.ejbe.org/EJBE2013Vol06No11p053AKKOC-VATANSEVER.pdf).
  • Ayadi, I. and Ellouze, A., (2013). Market Structure and Performance of Tunisian Banks. International Journal of Economics and Financial Issues, Vol:3, Nr:2, pp:345-354.
  • Bankalarımız 2011, Yayın No:284, G.M. Matbacılık ve Ticaret A.Ş. İstanbul, Mayıs 2012.
  • Bankalarımız 2012, Yayın No:294, G.M. Matbacılık ve Ticaret A.Ş. İstanbul, Mayıs 2013.
  • Bankalarımız 2013, Yayın No:304, G.M. Matbacılık ve Ticaret A.Ş. İstanbul, Mayıs 2014.
  • Bankalarımız 2014, Yayın No:311, G.M. Matbacılık ve Ticaret A.Ş. İstanbul, Mayıs 2015.
  • Bauer, P.W., Berger, A.N., Ferrier, G.D., and Humphrey, D.B., (1998). Consistency conditions for regulatory analysis of financial institutions: A comparison of frontier efficiency methods. Journal of Economic and Business, Vol:50, Nr:2, pp:85–114.
  • Bayyurt, N., (2013). Ownership Effect on Bank's Performance: Multi Criteria Decision Making Approaches on Foreign and Domestic Turkish Banks. Procedia-Social and Behavioral Sciences, 2013, 99: 919-928.
  • Beccalli, E., Casu, B., and Girardone, C., (2006). Efficiency and stock performance in european banking. Journal of Business Finance and Accounting, Vol:33, Br:1-2, pp:245–262.
  • Berger, A.N. and Humphrey, D.B., (1997). Efficiency of financial institutions: International survey and directions for future research. European Journal of Operational Research, Nr:98, pp:175-212.
  • Camanho, A.S. and Dyson, R.G., (2006). Data envelopment analysis and Malmquist indices for measuring performance. Journal of Productivity Analysis Nr:26, pp:35–49.
  • Che, Z.H.,Wang, H.S., and Chuang, C.L., (2010). A fuzzy AHP and DEA approach for marketing bank loan decisions for small and medium enterprises in Taiwan. Expert Syst. Appl., Vol:37, Nr:10, pp:7189–7199.
  • Chen, Y.C., Chiu, Y.H., Huang, C.W., and Tu, C.H., (2013). The analysis of bank business performance and market risk — applying fuzzy DEA. Economic modeling, Nr:32, pp:225–232. http://dx. doi.org/10.1016/j.econmod.2013.02.008.
  • Chitan, G., (2012). Corporate governance and bank performance in the Romanian banking sector. Procedia Economics and Finance, Nr:3,pp:549-554.
  • Colwell, R.J. and Davis, E.P., (1992). Output and productivity in banking. The Scandinavian Journal of Economics, Nr:94, pp:111-129.
  • Daly, K. and Zhang, X., (2014). Comparative analysis of the performance of Chinese Owned Banks’in Hong Kong 2004–2010. Journal of Multinational Financial Management, NT:27, pp:1-10.
  • Demir, Y. and Astarcıoğlu, M., (2007). Determining bank performance via financial prediction: An application in ISE. Suleyman Demirel University. Journal of Business Administration and Economics Faculty, Vol:12, Nr:1, pp:273–292
  • Denizer, C., Dinc, M., and Tarımcılar, M., (2000). Measuring bank efficiency in the pre and post liberalization environment: Evidence from the Turkish banking system. World Bank Policy Research Working Paper, p:2476.
  • Doumpos, M. and Zopounidis, C., (2010), A multi criteria decision support system for bank rating. Decision Support Systems, Vol:50, Nr:1, pp:55-63.
  • Erdem, C. and Erdem, M.S., (2008). Turkish banking efficiency and its relation to stock performance. Applied Economics Letters Nr:15, pp:207–211.
  • García, F., Guijarro, F., and Moya, I., (2010). Ranking Spanish savings banks: A multi criteria approach. Mathematical and Computer Modelling Nr:52, pp:1058–1065.
  • Güven, S. and Persentili, E., (1997). A linear programming model for bank balance sheet management. Omega, Nr:25, pp:449–459.
  • Ho, C.T., (2006). Measuring bank operations performance: an approach based on grey relation analysis. Journal of the Operational Research Society Nr:57, pp:337–349.
  • Huang, T-H. and Wang, M-H., (2002). Comparison of economic efficiency estimation methods: Parametric and non-parametric techniques. The Manchester School, Vol:70, Nr:5, pp:682–709.
  • Ifeacho, C. and Ngalawa, H., (2014). Performance Of The South African Banking Sector Since 1994. Journal of Applied Business Research (JABR), Vol:30, Nr:4, pp:1183-1196.
  • Ishizaka, A. and Nguyen, N.H., (2013). Calibrated fuzzy AHP for current bank selection. Expert Syst. Appl. Vol:40, Nr:9, pp:3775–3783. http://dx.doi.org/10.1016/j.eswa.2012.12.089.
  • Işık, I., Uysal, D., and Meleke, U., (2003). Post-entry performance of de novo banks in Turkey. In 10th Annual conference of the ERF.
  • Jayanthi, S., Kocha, B., and Sinha, K.K., (1999).Competitive analysis of manufacturing plants: An application to the US processed food industry. European Journal of Operational Research, Vol:118, No:2, pp:217-234.
  • Kaya, Y.T., (2001). CAMELS analysis in Turkish banking sector. BRSA MSPD working report 6.
  • Lin, T.-Y. and Chiu, S.-H., (2013). Using independent component analysis and network DEA to improve bank performance evaluation. Economic Modelling, Nr:32, pp:608-616.
  • Mahrooz, A., Maedeh, S., and Morteza, P., (2013). Performance evaluation of banks using fuzzy AHP and TOPSIS, case study: state-owned banks, particularly private and private banks in Iran. Caspain J. Appl. Sci. Res., Vol:2, Nr:3, pp:128.
  • Mandic, K., Delibasic, B., Knezevic, S., and Benkovic, S., (2014). Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Economic Modelling, Nr:43, pp:30-37.
  • Mareschal, B. and Brans, J., (1991). BANKADVISER: an industrial evaluation system, European Journal of Operational Research Vol:54, Nr:3, pp:318–324.
  • Mareschal. B., and Mertens, D., (1992). BANKS: a multi criteria decision support system for financial evaluation in the international banking sector, Journal of Decision Systems, Vol:50, Nr:1, pp:175–189.
  • Özbek, A., (2015). Performance Analysis of Public Banks in Turkey, International Journal of Business Management and Economic Research(IJBMER), Vol:6, Nr:3, pp:178-186.
  • Parkan, C., (1999). Performance measurement in government services. Managing Service Quality, Vol:9, No:2, pp:121-135.
  • Parkan, C., (2002). Measuring the operational performance of a public transit company. International Journal of Operations & Production Management, Vol:22, No:6, pp:693-720.
  • Parkan, C., (2005). Benchmarking operational performance: the case of two hotels. International Journal of Productivity and Performance Management, Vol:54. No:8, pp:679-696.
  • Parkan, C., Lam, K., and Hang, G., (1997). Operational competitiveness analysis on software development. Journal of the Operational Research Society, Vol:48, No:9, pp:892-905.
  • Parkan, C. and Wu, M., (1999). Measurement of the performance of an investment bank using the operational competitiveness rating procedure. Omega, Vol:27, Nr:2, pp:210–217.
  • Parkan, C. and Wu, M.-L., (1999a). Measuring the performance of operations of Hong Kong’s manufacturing industries. European Journal of Operational Research, Vol:118, No:2, pp:235-258.
  • Parkan, C., Wu, M.-L., (1999b).Measurement of the performance of an investment bank using the operational competitiveness rating procedure. Omega, Vol:27, No:2, pp:201-217.
  • Parkan, C., (1994). Operational Competitiveness Ratings of Production Units. Managerial and Decision Economics, Vol:15, No:3, pp:201-221.
  • Parkan, C., (2003). Measuring the effect of a new point of sale system on the performance of drugstore operations. Computers & Operations Research, Vol:30, No:5, pp:729-744.
  • Parkan, C. and WU, M.-L., (2000). Comparison of three modern multi criteria decision-making tools. International Journal of Systems Science, 2000, Vol:31, Nr:4, pp:497-517.
  • Pasiouras, F., Liadaki, A., and Zopounidis, C., (2008). Bank efficiency and share performance: Evidence from Greece. Applied Financial Economics Vol:18, Nr:14, pp:1121–1130.
  • Peters, M.L. and Zelewski, S., (2010). Performance Measurement mithilfe des Operational Competitiveness Ratings (OCRA). WiSt, Wirtschaftswissenschaftliches Studium, Vol:39, Nr:5, p.224.
  • Portela, M.C.A.S. and Thanassoulis, E., (2007). Comparative efficiency analysis of Portuguese bank branches. European Journal of Operational Research Vol:177, Nr:2, pp:1275–1288.
  • Puhuong Ta, H. and Yin Har, K., (2000). A study of bank selection decisions in Singapore using the Analytical Hierarchy Process, International Journal of Bank Marketing, Vol:18, Nr:4, pp:170-180. http://dx.doi.org/10.1108/02652320010349058.
  • Sangmi, M. and Nazir, T., (2010). Analyzing financial performance of commercial banks in India:Application of CAMEL model. Pakistan Journal of Commerce and Social Sciences, Vol:4, Nr:1, pp:40-55.
  • Seçme, N.Y., Bayrakdaroğlu, A., and Kahraman, C., (2009). Fuzzy performance evaluation in Turkish banking sector using analytic hierarchy process and TOPSIS. Expert Syst. Appl., Vol:36, Nr:9, pp:11699–11709.
  • Spathis, C., Kosmidou, K., and Doumpos, M., (2002). Assessing profitability factors in the Greek banking system: a multi criteria approach. International Transactions in Operational Research, NR:9, pp:517–530.
  • Stankevičienė, J. and Mencaitė, E., (2012). The evaluation of bank performance using a multi criteria decision making model: a case study on Lithuanian commercial banks. Technological and Economic Development of Economy, Vol:18, Nr:1, pp:189-205.
  • Thagunna, K.S. and Poudel, S., (2012). Measuring bank performance of Nepali banks: A Data envelopment analysis (DEA) perspective. International Journal of Economics and Financial Issues, Vol:3, Nr:1, pp:54-65.
  • Weifeng, X. and Huihuan, G., (2008). Using fuzzy analytic hierarchy process and balanced scorecard for commercial bank performance assessment. In Business and Information Management, IEEE, ISBIM'08. International Seminar on, Vol:1, pp:432-435.

OPERASYONEL REKABET DEĞERLENDİRMESİ (OCRA) YÖNTEMİYLE MEVDUAT BANKALARININ ETKİNLİK ÖLÇÜMÜ

Year 2015, Volume: 10 Issue: 3, 120 - 134, 10.07.2015

AŞIR Özbek

Abstract

Finansal sistemin en temel kurumları olan bankaların etkin ve verimli çalışması hem bankacılık sektörünün karlılığı hem de ülke ekonomisi açısından büyük önem taşımaktadır. Bankalar, tasarrufların yatırıma dönüştürülmesinde aracılık eden finansal kurumlardır. Finans sektöründe hizmet veren bankalar arasında çok ciddi rekabet söz konusudur. Hizmet kalitesini sürekli iyileştirerek verimliliğini artıran bankalar bu yarışta her zaman bir adım önde olacaktır. Operational Competitiveness Rating (OCRA), benzer girdiler kullanarak benzer çıktılar üreten karar verme birimlerinin göreli etkinlik ölçümünde kullanılan oldukça kullanışlı ve yeni sayılabilecek bir yöntemdir. Bu çalışma ile Türkiye’de faaliyet gösteren 32 mevduat bankasının etkinliklerini ölçmek için 2011-2014 yıllarına ait "Bankalarımız Kitabından" alınan veriler kullanılarak OCRA yöntemine dayanan bir etkinlik ölçme model geliştirilmiştir. Modelde kullanılan girdi ve çıktı faktörleri literatür taraması sonucunda belirlenmiştir. Dört yılın tamamı dikkate alındığında en yüksek performansı Yapı ve Kredi Bankasının gösterdiği belirlenmiştir.

 

Keywords

Operational Competitiveness Rating OCRA Etkinlik Ölçümü Mevduat Bankaları

References

  • Akkoç, S. and Vatansever, K., (2013). Fuzzy performance evaluation with AHP and TOPSIS methods: evidence for Turkish banking sector after the global financial crisis. Eurasian J. Bus. Econ. Vol:6, Nr:11, pp:53–74 (Available at: http://www.ejbe.org/EJBE2013Vol06No11p053AKKOC-VATANSEVER.pdf).
  • Ayadi, I. and Ellouze, A., (2013). Market Structure and Performance of Tunisian Banks. International Journal of Economics and Financial Issues, Vol:3, Nr:2, pp:345-354.
  • Bankalarımız 2011, Yayın No:284, G.M. Matbacılık ve Ticaret A.Ş. İstanbul, Mayıs 2012.
  • Bankalarımız 2012, Yayın No:294, G.M. Matbacılık ve Ticaret A.Ş. İstanbul, Mayıs 2013.
  • Bankalarımız 2013, Yayın No:304, G.M. Matbacılık ve Ticaret A.Ş. İstanbul, Mayıs 2014.
  • Bankalarımız 2014, Yayın No:311, G.M. Matbacılık ve Ticaret A.Ş. İstanbul, Mayıs 2015.
  • Bauer, P.W., Berger, A.N., Ferrier, G.D., and Humphrey, D.B., (1998). Consistency conditions for regulatory analysis of financial institutions: A comparison of frontier efficiency methods. Journal of Economic and Business, Vol:50, Nr:2, pp:85–114.
  • Bayyurt, N., (2013). Ownership Effect on Bank's Performance: Multi Criteria Decision Making Approaches on Foreign and Domestic Turkish Banks. Procedia-Social and Behavioral Sciences, 2013, 99: 919-928.
  • Beccalli, E., Casu, B., and Girardone, C., (2006). Efficiency and stock performance in european banking. Journal of Business Finance and Accounting, Vol:33, Br:1-2, pp:245–262.
  • Berger, A.N. and Humphrey, D.B., (1997). Efficiency of financial institutions: International survey and directions for future research. European Journal of Operational Research, Nr:98, pp:175-212.
  • Camanho, A.S. and Dyson, R.G., (2006). Data envelopment analysis and Malmquist indices for measuring performance. Journal of Productivity Analysis Nr:26, pp:35–49.
  • Che, Z.H.,Wang, H.S., and Chuang, C.L., (2010). A fuzzy AHP and DEA approach for marketing bank loan decisions for small and medium enterprises in Taiwan. Expert Syst. Appl., Vol:37, Nr:10, pp:7189–7199.
  • Chen, Y.C., Chiu, Y.H., Huang, C.W., and Tu, C.H., (2013). The analysis of bank business performance and market risk — applying fuzzy DEA. Economic modeling, Nr:32, pp:225–232. http://dx. doi.org/10.1016/j.econmod.2013.02.008.
  • Chitan, G., (2012). Corporate governance and bank performance in the Romanian banking sector. Procedia Economics and Finance, Nr:3,pp:549-554.
  • Colwell, R.J. and Davis, E.P., (1992). Output and productivity in banking. The Scandinavian Journal of Economics, Nr:94, pp:111-129.
  • Daly, K. and Zhang, X., (2014). Comparative analysis of the performance of Chinese Owned Banks’in Hong Kong 2004–2010. Journal of Multinational Financial Management, NT:27, pp:1-10.
  • Demir, Y. and Astarcıoğlu, M., (2007). Determining bank performance via financial prediction: An application in ISE. Suleyman Demirel University. Journal of Business Administration and Economics Faculty, Vol:12, Nr:1, pp:273–292
  • Denizer, C., Dinc, M., and Tarımcılar, M., (2000). Measuring bank efficiency in the pre and post liberalization environment: Evidence from the Turkish banking system. World Bank Policy Research Working Paper, p:2476.
  • Doumpos, M. and Zopounidis, C., (2010), A multi criteria decision support system for bank rating. Decision Support Systems, Vol:50, Nr:1, pp:55-63.
  • Erdem, C. and Erdem, M.S., (2008). Turkish banking efficiency and its relation to stock performance. Applied Economics Letters Nr:15, pp:207–211.
  • García, F., Guijarro, F., and Moya, I., (2010). Ranking Spanish savings banks: A multi criteria approach. Mathematical and Computer Modelling Nr:52, pp:1058–1065.
  • Güven, S. and Persentili, E., (1997). A linear programming model for bank balance sheet management. Omega, Nr:25, pp:449–459.
  • Ho, C.T., (2006). Measuring bank operations performance: an approach based on grey relation analysis. Journal of the Operational Research Society Nr:57, pp:337–349.
  • Huang, T-H. and Wang, M-H., (2002). Comparison of economic efficiency estimation methods: Parametric and non-parametric techniques. The Manchester School, Vol:70, Nr:5, pp:682–709.
  • Ifeacho, C. and Ngalawa, H., (2014). Performance Of The South African Banking Sector Since 1994. Journal of Applied Business Research (JABR), Vol:30, Nr:4, pp:1183-1196.
  • Ishizaka, A. and Nguyen, N.H., (2013). Calibrated fuzzy AHP for current bank selection. Expert Syst. Appl. Vol:40, Nr:9, pp:3775–3783. http://dx.doi.org/10.1016/j.eswa.2012.12.089.
  • Işık, I., Uysal, D., and Meleke, U., (2003). Post-entry performance of de novo banks in Turkey. In 10th Annual conference of the ERF.
  • Jayanthi, S., Kocha, B., and Sinha, K.K., (1999).Competitive analysis of manufacturing plants: An application to the US processed food industry. European Journal of Operational Research, Vol:118, No:2, pp:217-234.
  • Kaya, Y.T., (2001). CAMELS analysis in Turkish banking sector. BRSA MSPD working report 6.
  • Lin, T.-Y. and Chiu, S.-H., (2013). Using independent component analysis and network DEA to improve bank performance evaluation. Economic Modelling, Nr:32, pp:608-616.
  • Mahrooz, A., Maedeh, S., and Morteza, P., (2013). Performance evaluation of banks using fuzzy AHP and TOPSIS, case study: state-owned banks, particularly private and private banks in Iran. Caspain J. Appl. Sci. Res., Vol:2, Nr:3, pp:128.
  • Mandic, K., Delibasic, B., Knezevic, S., and Benkovic, S., (2014). Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Economic Modelling, Nr:43, pp:30-37.
  • Mareschal, B. and Brans, J., (1991). BANKADVISER: an industrial evaluation system, European Journal of Operational Research Vol:54, Nr:3, pp:318–324.
  • Mareschal. B., and Mertens, D., (1992). BANKS: a multi criteria decision support system for financial evaluation in the international banking sector, Journal of Decision Systems, Vol:50, Nr:1, pp:175–189.
  • Özbek, A., (2015). Performance Analysis of Public Banks in Turkey, International Journal of Business Management and Economic Research(IJBMER), Vol:6, Nr:3, pp:178-186.
  • Parkan, C., (1999). Performance measurement in government services. Managing Service Quality, Vol:9, No:2, pp:121-135.
  • Parkan, C., (2002). Measuring the operational performance of a public transit company. International Journal of Operations & Production Management, Vol:22, No:6, pp:693-720.
  • Parkan, C., (2005). Benchmarking operational performance: the case of two hotels. International Journal of Productivity and Performance Management, Vol:54. No:8, pp:679-696.
  • Parkan, C., Lam, K., and Hang, G., (1997). Operational competitiveness analysis on software development. Journal of the Operational Research Society, Vol:48, No:9, pp:892-905.
  • Parkan, C. and Wu, M., (1999). Measurement of the performance of an investment bank using the operational competitiveness rating procedure. Omega, Vol:27, Nr:2, pp:210–217.
  • Parkan, C. and Wu, M.-L., (1999a). Measuring the performance of operations of Hong Kong’s manufacturing industries. European Journal of Operational Research, Vol:118, No:2, pp:235-258.
  • Parkan, C., Wu, M.-L., (1999b).Measurement of the performance of an investment bank using the operational competitiveness rating procedure. Omega, Vol:27, No:2, pp:201-217.
  • Parkan, C., (1994). Operational Competitiveness Ratings of Production Units. Managerial and Decision Economics, Vol:15, No:3, pp:201-221.
  • Parkan, C., (2003). Measuring the effect of a new point of sale system on the performance of drugstore operations. Computers & Operations Research, Vol:30, No:5, pp:729-744.
  • Parkan, C. and WU, M.-L., (2000). Comparison of three modern multi criteria decision-making tools. International Journal of Systems Science, 2000, Vol:31, Nr:4, pp:497-517.
  • Pasiouras, F., Liadaki, A., and Zopounidis, C., (2008). Bank efficiency and share performance: Evidence from Greece. Applied Financial Economics Vol:18, Nr:14, pp:1121–1130.
  • Peters, M.L. and Zelewski, S., (2010). Performance Measurement mithilfe des Operational Competitiveness Ratings (OCRA). WiSt, Wirtschaftswissenschaftliches Studium, Vol:39, Nr:5, p.224.
  • Portela, M.C.A.S. and Thanassoulis, E., (2007). Comparative efficiency analysis of Portuguese bank branches. European Journal of Operational Research Vol:177, Nr:2, pp:1275–1288.
  • Puhuong Ta, H. and Yin Har, K., (2000). A study of bank selection decisions in Singapore using the Analytical Hierarchy Process, International Journal of Bank Marketing, Vol:18, Nr:4, pp:170-180. http://dx.doi.org/10.1108/02652320010349058.
  • Sangmi, M. and Nazir, T., (2010). Analyzing financial performance of commercial banks in India:Application of CAMEL model. Pakistan Journal of Commerce and Social Sciences, Vol:4, Nr:1, pp:40-55.
  • Seçme, N.Y., Bayrakdaroğlu, A., and Kahraman, C., (2009). Fuzzy performance evaluation in Turkish banking sector using analytic hierarchy process and TOPSIS. Expert Syst. Appl., Vol:36, Nr:9, pp:11699–11709.
  • Spathis, C., Kosmidou, K., and Doumpos, M., (2002). Assessing profitability factors in the Greek banking system: a multi criteria approach. International Transactions in Operational Research, NR:9, pp:517–530.
  • Stankevičienė, J. and Mencaitė, E., (2012). The evaluation of bank performance using a multi criteria decision making model: a case study on Lithuanian commercial banks. Technological and Economic Development of Economy, Vol:18, Nr:1, pp:189-205.
  • Thagunna, K.S. and Poudel, S., (2012). Measuring bank performance of Nepali banks: A Data envelopment analysis (DEA) perspective. International Journal of Economics and Financial Issues, Vol:3, Nr:1, pp:54-65.
  • Weifeng, X. and Huihuan, G., (2008). Using fuzzy analytic hierarchy process and balanced scorecard for commercial bank performance assessment. In Business and Information Management, IEEE, ISBIM'08. International Seminar on, Vol:1, pp:432-435.
There are 55 citations in total.

Details

Primary Language Turkish
Journal Section Operation
Authors

AŞIR Özbek

Publication Date July 10, 2015
Published in Issue Year 2015 Volume: 10 Issue: 3

Cite

APA Özbek, A. (2015). OPERASYONEL REKABET DEĞERLENDİRMESİ (OCRA) YÖNTEMİYLE MEVDUAT BANKALARININ ETKİNLİK ÖLÇÜMÜ. Social Sciences, 10(3), 120-134. https://doi.org/10.12739/NWSA.2015.10.3.3C0132
AMA Özbek A. OPERASYONEL REKABET DEĞERLENDİRMESİ (OCRA) YÖNTEMİYLE MEVDUAT BANKALARININ ETKİNLİK ÖLÇÜMÜ. Social Sciences. July 2015;10(3):120-134. doi:10.12739/NWSA.2015.10.3.3C0132
Chicago Özbek, AŞIR. “OPERASYONEL REKABET DEĞERLENDİRMESİ (OCRA) YÖNTEMİYLE MEVDUAT BANKALARININ ETKİNLİK ÖLÇÜMÜ”. Social Sciences 10, no. 3 (July 2015): 120-34. https://doi.org/10.12739/NWSA.2015.10.3.3C0132.
EndNote Özbek A (July 1, 2015) OPERASYONEL REKABET DEĞERLENDİRMESİ (OCRA) YÖNTEMİYLE MEVDUAT BANKALARININ ETKİNLİK ÖLÇÜMÜ. Social Sciences 10 3 120–134.
IEEE A. Özbek, “OPERASYONEL REKABET DEĞERLENDİRMESİ (OCRA) YÖNTEMİYLE MEVDUAT BANKALARININ ETKİNLİK ÖLÇÜMÜ”, Social Sciences, vol. 10, no. 3, pp. 120–134, 2015, doi: 10.12739/NWSA.2015.10.3.3C0132.
ISNAD Özbek, AŞIR. “OPERASYONEL REKABET DEĞERLENDİRMESİ (OCRA) YÖNTEMİYLE MEVDUAT BANKALARININ ETKİNLİK ÖLÇÜMÜ”. Social Sciences 10/3 (July 2015), 120-134. https://doi.org/10.12739/NWSA.2015.10.3.3C0132.
JAMA Özbek A. OPERASYONEL REKABET DEĞERLENDİRMESİ (OCRA) YÖNTEMİYLE MEVDUAT BANKALARININ ETKİNLİK ÖLÇÜMÜ. Social Sciences. 2015;10:120–134.
MLA Özbek, AŞIR. “OPERASYONEL REKABET DEĞERLENDİRMESİ (OCRA) YÖNTEMİYLE MEVDUAT BANKALARININ ETKİNLİK ÖLÇÜMÜ”. Social Sciences, vol. 10, no. 3, 2015, pp. 120-34, doi:10.12739/NWSA.2015.10.3.3C0132.
Vancouver Özbek A. OPERASYONEL REKABET DEĞERLENDİRMESİ (OCRA) YÖNTEMİYLE MEVDUAT BANKALARININ ETKİNLİK ÖLÇÜMÜ. Social Sciences. 2015;10(3):120-34.