Application of data envelopment analysis for measuring financial efficiency of district central cooperative banks

Chia sẻ: Huỳnh Ngọc Gia Hân | Ngày: | Loại File: PDF | Số trang:9

Thêm vào BST

Báo xấu

14
lượt xem 4
download

Download Vui lòng tải xuống để xem tài liệu đầy đủ

The study focused to measure the efficiency of District Co-operative banks of Odisha using DEA approach. The efficiency score are calculated under BCC Mode of DEA which is based on the assumption of variable return to scale and DCCBS are ranked accordingly.

Chủ đề:

Bình luận(0) Đăng nhập để gửi bình luận!

Lưu

Nội dung Text: Application of data envelopment analysis for measuring financial efficiency of district central cooperative banks

International Journal of Management (IJM) Volume 10, Issue 6, November-December 2019, pp. 161–169, Article ID: IJM_10_06_016 Available online at http://www.iaeme.com/ijm/issues.asp?JType=IJM&VType=10&IType=6 Journal Impact Factor (2019): 9.6780 (Calculated by GISI) www.jifactor.com ISSN Print: 0976-6502 and ISSN Online: 0976-6510 © IAEME Publication APPLICATION OF DATA ENVELOPMENT ANALYSIS FOR MEASURING FINANCIAL EFFICIENCY OF DISTRICT CENTRAL COOPERATIVE BANKS Chinmaya Kumar Rout Research Scholar, Faculty of Management Sciences, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India Dr. Prafulla Kumar Swain Professor, Faculty of Management Sciences, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India Dr. Manoranjan Dash* Associate Professor, Faculty of Management Sciences, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India *Corresponding Author Email: manoranjandash@soa.ac.in ABSTRACT Agriculture being the primary sector of Indian economy now a day’s drawn more attention and emphasised has given for the overall development. Efficient credit facilities are essential for the improvement of this sector. Cooperative bank play a vital role in providing the forward as well as backward linkage of agricultural credit to be routed. The study focused to measure the efficiency of District Co-operative banks of Odisha using DEA approach. The efficiency score are calculated under BCC Mode of DEA which is based on the assumption of variable return to scale and DCCBS are ranked accordingly. The findings indicated that majority of DCCB’s are efficient where as others found to be inefficient. Keywords: Financial Efficiency, Cooperative Banks, Data Envelopment Analysis Cite this Article: Chinmaya Kumar Rout, Dr. Prafulla Kumar Swain and Dr. Manoranjan Dash, Application of Data Envelopment Analysis for Measuring Financial Efficiency of District Central Cooperative Banks, International Journal of Management (IJM), 10 (6), 2019, pp. 161–169. http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=10&IType=6 http://www.iaeme.com/IJM/index.asp 161 editor@iaeme.com
Application of Data Envelopment Analysis for Measuring Financial Efficiency of District Central Cooperative Banks 1. INTRODUCTION In India 70% of the gross population are dependent on agriculture. Agricultural Credit act as significant input which support and enhances crop production as well as other allied activities. Co-operative banks all over the country play a significant role in socio-economic development of rural people by supplying timely credit. The Cooperative Credit system has three elements i.e. short term credit, medium-term credit, and long term credit structure. The short term structure focused on rural, district and State as defined as Primary Agricultural Credit Societies (PACS) , District Central Cooperative Banks (DCCBs) and State Cooperative Banks (SCBs) . District Central Co-operative Banks (DCCBs) provides Short Term credit facilities for crop production through Primary Agricultural Credit Societies (PACS) and Medium Term credit directly to farmers for procurement of agricultural equipment’s. In Odisha, there are 17 District Central Cooperative Banks with their 322 branches catering the credit need of rural people. The District Central Co- operative Banks take financial assistance from State Cooperative Bank. The main functions of DCCBs provide finance to the PACS, acceptance of deposits, granting of loans/advances, a collection of bills, safe custody of valuable assets and agency services. DCCBs functions as the fulcrum of credit flow for improving rural economy interlinking various other supporting sub- sectors of agriculture. Performance of banks is the product of efficiency, utilization, and productivity. Productivity and efficiency reflect overall performance. As productivity establishes the relationship between input and output while the productivity level is recognized as an efficient situation.Thus efficiency reflects that a firm’s capacity to produce more outputs from a constant set of inputs. In the public sector .it is about to maximize the quality, scope, and timeliness of service delivery with minimum possible input factors. In changing era of competitive environment analyzing the efficiency is not only important for institutions but also obvious for their competitiveness and solvency. In this study, an attempt has been made to measure the financial efficiency of District Central Co-operative Banks (DCCBs) of Odisha with the help of Data Envelopment Analysis (DEA). The organization of article i.e Introduction, Review of Literature, Objective of the study, Methodology, Data Analysis & Discussions, Conclusions. 2. REVIEW OF LITERATURE There are various methods and tools available to measure the efficiency of banks. Data Envelopment Analysis is leading one. Data Envelopment Analysis (DEA) is non-parametric technique used for measuring the efficiency of different Decision Making Units (DMUs). Data Envelopment Analysis concept was first given by Farrell, M.J. (1957) and latter on Charnes, Cooper, and Rhodes (CCR) (1978) improved this model with the assumption of Constant Returns to Scale for measuring the efficiency of various DMUs. Further Banker, Charles, Cooper (BCC) (1984) developed Variable Returns to Scale (VRS) of DEA for measuring the efficiency of DMUs which is an improvement over CCR model. First Sherman and Gold (1985) applied DEA to measure the efficiency of bank branches. Saha.A. and Ravisankar .S.T (2000) applied DEA for rating the Indian Commercial Banks and found it as suitable tool for measuring the efficiency of banks , Manandhar.R and Tang J.C.S. (2000) used DEA to study the efficiency of various branches of bank and identified that the best-performing branch are benchmark for other branches, Sathye .M,(2003) made an attempt to measure the productive efficiency of banks in a developing country that is India by using Data Envelopment Analysis and found that public sector banks are performing well than private sector banks and foreign banks and suggest that policy should be implemented to reduce Non-Performing Assets , rationalization of staffs and branches to make Indian Banks compete globally, Varadi, V.K., Mavaluri, P.K., and Boppana,N. (2006) used Data Envelopment Analysis for measuring the efficiency of all types http://www.iaeme.com/IJM/index.asp 162 editor@iaeme.com
Chinmaya Kumar Rout, Dr. Prafulla Kumar Swain and Dr. Manoranjan Dash of banks i.e. Public Sector Banks, Private Sector Banks and Foreign Banks operating in India on the basis of productivity, profitability, financial management and asset quality. The study reveals that public sector banks are more efficient than others. Sinha,R.P.(2007) evaluate performance of Indian commercial banks Using the Super Efficiency Approach under DEA and found that Private sector banks are more efficient than Public sector banks, Debnath,R.M and Shankar.R.(2008) used DEA to measure the performance of 50 Indian Banks and result states that small and large nationalized banks are showing higher efficiency than medium-sized nationalized banks and suggest that banks should focus on rural area for more development, Chen, T.Y., Chen,C.B., and Peng, S.Y. (2008) conducted a study on a Cooperative Bank of Taiwan by using DEA and Balanced scorecard approach to know the operational performance and found that DEA will help to know the current operational efficiency whereas Balanced Scorecard approach will help to evaluate longer-term strategies and visions, Subramanyam.T and Reddy,C.S.(2008) measured the risk efficiency of commercial banks in India through Data Envelopment Analysis and indicated that Foreign banks showing risk efficiency than Public and Private Sector Banks, hence these banks should strengthen their internal risk control system. Shirvani,H., Taj.S and Mirshab.B. (2011) applied Data Envelopment Analysis to develop a new model for measuring the banking efficiency in Turkey and found that non-linear input-output combination is more appropriate and efficient than linear input-output combination under standard DEA measure, Aryanezhada.M.B, Najafib.E ,and Farkousha.S.B (2011) used DEA and Balanced Scorecard approach for measuring the efficiency of service industry with reference to a private bank of Iran and their study resulted a new model which can be implemented for other financial sectors. Joshi,P.V and Bhalerao,J.V.(2011) applied DEA to evaluate the efficiency of the Indian banking sector and found that majority of the banks are efficient, Anjum.S (2012) studied various technique’s for measuring banking efficiency and found that DEA is a suitable technique for measuring the efficiency of the decision-making unit, Sharma.A.K, Sharma.D, and Baru.M.K.(2012) applied DEA and Tobit regression to measure the efficiency and productivity of Indian banks and found that average annual efficiency scores of Public sector bnaks are relatively more than private and foreign banks, Shahwan,T.M and Hassan,Y.M.(2013) analyzed the efficiency of UAE banks using DEA on the basis of three parameters i.e. profitability, marketability, and social disclosure and found that banks in UAE are performing in terms of profitability and social disclosure than marketability , Titko, J., Stankeviciene. J., and Lace.N. (2014) used DEA for measuring the efficiency of Latvian banks and found that current financial database required for further investigation of efficiency, Fujii,H., Managi, S., and Matousek, R.(2014) analysed the efficiency of Indian banks and productive changes with undesirable outputs by using Weighted Russell Directional Distance Model (WDM) and found that foreign banks are showing more efficiency than public and private sector banks. So policymakers should take the necessary steps to improve their efficiency, Agarwal, N., Guha,B., Dutta, A., and Bandyopadhyay, G.(2014) evaluate the performance of banks in India using DEA and found that private sector banks are showing more efficiency than public sector bnaks, so public sector banks should improve their performance. Rao.N .E.S.V. and Gudala.C (2015) made performance appraisal and ranking of District Central Cooperative Banks through the Malmquist Index and Super-Efficiency model of DEA of Andhra Pradesh. They found that 32 % of DCCBs are showing an increasing trend and rest of the DCCBs are showing a mixed trend and they need more funds to achieve optimality. Kaur, S., & Gupta, P.K.(2015) measure the efficiency of the Indian banks with the help of DEA and found that State bank and it’s associates showing higher efficiency in comparison to private banks. Akinsoyinu,A.C.(2015) evaluate the efficiency European Financial Cooperative sector by using Data Envelopment Analysis and found that Financial Cooperative sector in Europe are http://www.iaeme.com/IJM/index.asp 163 editor@iaeme.com
Application of Data Envelopment Analysis for Measuring Financial Efficiency of District Central Cooperative Banks showing higher efficiency during the study period, Gayval,B.K, and Bajaj,V.H. (2015) measure the efficiency of Indian Banks by using Data Envelopment Analysis and Stochastic Frontier Analysis and found that the efficiency level of Indian banks are same under both the approach. Syamni, G., and Abd Majid,M.S.(2016) studied the efficiency of Saving and Credit Cooperative Units in North Aceh of Indonesia by using Data Envelopment Analysis and found that cooperative units are not operating efficiently . So various steps should be taken to improve the efficiency level and increase capital. Othman, F.M., Mohd-Zamil, N.A., Rasid, S. Z. A., Vakilbashi, A.,and Mokhber, M. (2016) studied extensive literatures available on the use of DEA as a technique for measuring efficiency of the banking sector. They found that generally two methods of Data Envelopment Analysis are used DEA-CCR (Charnes-Cooper-Rhodes- 1979) method with the assumption of constant return to scale and DEA –BCC (Banker- Charnes-Cooper-1984) method with the assumption of variable return to scale are used by different researchers. Madhvi and Srivastava.A. (2017) measure the efficiency of Indian commercial banks using Data Envelopment Analysis and found that merely generating more profits is not a significant parameter of banking efficiency but the path of growth is also important. Kaur.R and Aggarwal.M. (2017) measure the performance of public sector banks in India with the help of DEA and found that majority of Public Sector Banks are inefficient because they use excess input variables to produce more outputs. Rezaeiani, M. J., and Foroughi, A. A. (2018) conducted a study to find the criteria of differentiating between efficient DMUs under DEA approach and develop reference frontier concept. They have given a new model to measure the reference frontier which has the capability for ranking extreme and non- extreme efficient DMUs again it has no problem in dealing with negative data. From the study of the above literatures, we have observed that very less work has been undertaken on measuring the efficiency of cooperative banks. Agriculture development is possible through timely credit which provided by cooperative banks. So knowing the efficiency of cooperative banks is prime importance. In this paper we have attempted to measure the efficiency of District Central Cooperative Banks (DCCBs) operating in Odisha by applying Data Envelopment Analysis (DEA). The present study analyzed the efficiency of DCCBs operating in Odisha and ranks the DCCBs according to their performance. 3. RESEARCH METHODOLOGY 3.1. Data We have taken 17 District Central Cooperative Banks (DCCBs) operating in Odisha. The data collected from the Annual report of State Cooperative Bank and reports published by the National Federation of State Cooperative Banks (NAFSCOB). Data for 5 years (2012-13 to 2016-17) are taken for study. For measuring the efficiency of DCCBs, we have used Data Envelopment Analysis (DEA). 3.2. Data Envelopment Analysis (DEA) The model can also indicate directions for inefficient DMUs to become efficient. This model is popularly known as CCR model.This is based on the assumption of Constant Returns to Scale (CRS) of Various DMUs. Constant returns to scale occur when increasing the number of inputs leads to an equivalent increase in the output. max ℎk = ∑𝑚 𝑟=1 ur yrk 1 Subject to: 𝑛 ∑ vi xik = 1 𝑖=1 http://www.iaeme.com/IJM/index.asp 164 editor@iaeme.com
Chinmaya Kumar Rout, Dr. Prafulla Kumar Swain and Dr. Manoranjan Dash 𝑚 𝑛 ∑ ur yrj − ∑ vi xij ≤ 0, ∀ j 𝑟=1 𝑖=1 ur vi ≥ 0, ∀ r, i With: Y= outputs ,x = inputs u ,v = weights; r=1,........,m; i=1,......,n; j=1,.......N BCC model developed by Banker, Charles, Cooper (BCC) (1984) developed which are based on the assumption of Variable Return to Scale (VRS) is an improvement of the CCR model of DEA. Variable returns to scale occurs when an increase in inputs does not result in a proportional change in the outputs. max ℎk = ∑𝑚 𝑟=1 ur yrk − uk 2 Subject to: 𝑛 ∑ vi xik = 1 𝑖=1 𝑚 𝑛 ∑ ur yrj − ∑ vi xij − ur ≤ 0, 𝑟=1 𝑖=1 ur vi ≥ 0, With: Y= outputs, x = inputs u ,v = weights; r=1,........,m; i=1,......,n; j=1,.......N 4. DATA ANALYSIS AND DISCUSSION Here Input oriented model of BCC-DEA is used. The 5 years average (2012-13 to 2016-17) is taken for each of the variables and presented in table .1 Table 1 Input Data for DEA Deposits Borrowings Loan & Advances Investments Name of the DCCBs (In lakhs) (In lakhs) (In lakhs) (In lakhs) Angul DCCB 60,578.90 36382.58 50059.17 53127.82 Aska DCCB 18,748.40 15767.64 22995.74 14339.94 Balasore DCCB 1,20,349.86 82159.15 120947.88 47737.01 Banki DCCB 14,118.53 9269.72 15944.62 12529.62 Bhawanipatna DCCB 15,912.91 17265.36 23739.62 11456.74 Berhampur DCCB 37,151.11 18797.95 28505.3 24532.41 Bolangir DCCB 47,564.59 21492.53 60229.01 22710.48 Boudh DCCB 14,277.74 15108.99 23833.92 23521.37 Cuttack DCCB 94,848.79 83371.77 111018.2 82867.19 Keonjhar DCCB 31,764.87 17596.34 23639.45 25056.27 http://www.iaeme.com/IJM/index.asp 165 editor@iaeme.com
Application of Data Envelopment Analysis for Measuring Financial Efficiency of District Central Cooperative Banks Khurda DCCB 27,463.17 28462.02 37712.58 16584.32 Koraput DCCB 47,813.28 35111.38 51104.01 43043.48 Mayurbhanj DCCB 26,856.16 17377.71 29080.95 20313.48 Nayagarh DCCB 15,962.10 18476.16 28126.46 12178.09 Sambalpur DCCB 91,827.61 90797.72 136958.64 66093.63 Sundargarh DCCB 52,639.43 32930.57 51698.23 44178.09 Puri Nimapara DCCB 7,582.54 11940.55 15751.4 5592.19 (Authors design) The of table .1data run through DEA model DEA Solver LV8.0/ BCC(BCC-I) Problem = Name of the DCCBs No. of DMUs = 17 Returns to Scale = Variable (Sum of Lambda = 1) Table 2 Input Output variables S.no Input Variables Output Variables 1 Deposits Loan & Advances 2 Borrowings Investments Banks secure score 1 will be efficient and bank secure score less than 1 will be inefficient. Table 3 Results No. Decision Making Units Score Return To Scale of DMUs 1 Angul DCCB 1 Decreasing 2 Aska DCCB 0.8348 Increasing 3 Balasore DCCB 0.9291 Decreasing 4 Banki DCCB 1 Increasing 5 Bhawanipatna DCCB 0.8707 Decreasing 6 Berhampur DCCB 0.8801 Decreasing 7 Bolangir DCCB 1 Constant 8 Boudh DCCB 1 Constant 9 Cuttack DCCB 1 Decreasing 10 Keonjhar DCCB 0.9213 Decreasing 11 Khurda DCCB 0.8411 Decreasing 12 Koraput DCCB 0.9744 Decreasing 13 Mayurbhanj DCCB 0.8514 Increasing 14 Nayagarh DCCB 1 Decreasing 15 Sambalpur DCCB 1 Decreasing 16 Sundargarh DCCB 1 Decreasing 17 Puri Nimapara DCCB 1 Constant The result of analysis shown in Table.3. Variable return to scale model of Data Envelopment Analysis are categorized into three i.e Increasing Return to Scale, Decreasing Return to Scale and Constant Return to Scale. Those banks have more increase of output variables than input variables, they are coming under an Increasing Return to Scale, DMUs having more increase of input variables than output variables are coming under Decreasing Return to Scale and DMUs having an increase of input and output variables at equal proportion are coming under a Constant Return to Scale. Out of 17 District Central Cooperative Banks (DCCBs) in Odisha 3 DCCBs are coming under an Increasing Return to Scale, 11 DCCBs are coming under Decreasing Return to Scale and 3 DCCBs are coming under a Constant Return to Scale. http://www.iaeme.com/IJM/index.asp 166 editor@iaeme.com
Chinmaya Kumar Rout, Dr. Prafulla Kumar Swain and Dr. Manoranjan Dash Table 4 Ranking DMU Score Rank Angul DCCB 1 1 Banki DCCB 1 1 Bolangir DCCB 1 1 Boudh DCCB 1 1 Cuttack DCCB 1 1 Nayagarh DCCB 1 1 Sambalpur DCCB 1 1 Sundargarh DCCB 1 1 Puri Nimapara DCCB 1 1 Koraput DCCB 0.9744 10 Balasore DCCB 0.9291 11 Keonjhar DCCB 0.9213 12 Berhampur DCCB 0.8801 13 Bhawanipatna DCCB 0.8707 14 Mayurbhanj DCCB 0.8514 15 Khurda DCCB 0.8411 16 Aska DCCB 0.8348 17 Table-4 shows the score of DCCBs operating in Odisha basing upon the BCC model of DEA. It is observed that 9 District Central Cooperative Banks (DCCBs) of Odisha are efficient, because they have scored 1 and other 8 District Central Cooperative Banks (DCCBs) of Odisha are inefficient because they have scored less than 1. We have taken Deposits and Borrowings of DCCBs as input variables, these are a source of funds to banks. Loan & Advances, Investments of DCCBs as output variable these are two major utilization of funds by banks. The banks recorded inefficiencies are not maintaining equilibrium between in and out the flow of funds. The banks recorded inefficiencies are in the tribal and backward area of the state, so awareness should be created by banks to attract the rural farmer to avail timely credit and take the advantages of various schemes. The DCCBs are balancing center between Primary Agriculture Cooperative Societies and State Cooperative Banks (StCB). Cooperative banks are facing top competition from Commercial Banks. Political interference also affecting smooth functioning Cooperative Banks. Out 17 DCCBs more than 50 % of them are operating at efficiency level whereas others operating at lower than efficiency. 5. CONCLUSION The result of the study indicated that District Central Cooperative Banks (DCCBs) in Odisha are showing a moderate level of efficiency. Out of 17 DCCBs 9 of them are efficient and 8 of them are showing inefficiency in performance on basis of BCC model of DEA. The study has the limitation of considering less number of input and output variables. Other researchers may take more variables for future study. The policymakers should focus on fund utilization and timely credit disbursement to make inefficient DCCBs to make them an efficient. The banks should implement modern banking facilities like ATM facilities, Internet banking, issue more number of Kisan credit card , new banking schemes for customer to make themselves competitive in market. REFERENCES [1] Agarwal. N., Guha, B., Dutta, A., & Bandyopadhyay, G, Performance Measurement of Indian Banks using Data Envelopment Analysis. Lecture Notes on Information Theory Vol, 2(3), 2014 http://www.iaeme.com/IJM/index.asp 167 editor@iaeme.com
Application of Data Envelopment Analysis for Measuring Financial Efficiency of District Central Cooperative Banks [2] Akinsoyinu, A.C, Efficiency Evaluation of European Financial Cooperative Sector: A Data Envelopment Analysis Approach. International Journal of Academic Research in Accounting, Finance and Management Science, 5(4), 2015, pp 11-21. [3] Banker, R.D.,Charnes, A.,& Cooper, W.W, Some Models for Esimating Technical and scale inefficiencies in Data Envelopment Analysis. Management Science, 30 (9), 1984, pp 1078- 1092. [4] Charnes, A., W.W. Cooper, & E.Rhodes, Measuring the Efficiency of Decision Making Units, Eoropean Journal of Operation Research2, 1978, pp 429-444. [5] Chen T.Y., Chen. C.B., & Peng, S.Y, Firm operation performance analysis using Data Envelopment Analysis and Balanced Scorecard; Acase Study of a Credit Cooperative Bank. International Journal of Productivity and Performance Management, 57(7), 2008, pp 523- 539. [6] Debnath, R.M., & Shankar,R, Measuring the Perfromance of Indian Banks: An Application of Data Envelopment Analysis. International Journal of Business Performance Management, 10 (1), 2008, pp 57-85. [7] Farrell, M.J, the Measurement of Productive Efficiency, a Journal of Royal Statistical Society, 120, 1957, pp 253-281. [8] Fujii, H., Managi, S., & Matousek, R, Indian Bank Efficiency and Productivity Changes with Undesirable Outputs:A Disaggregated Approach. Journal of Banking & Finance, 38, 2014, pp 41-50. [9] Gayval,B.K. & Bajaj.V.H, Measuring Efficiency of Indian Banks: A DEA-Stochastic Frontier Analysis. International Journal of Innovative Research in Science, Engineering and Technology, 4(12), 2015, pp 12602-12608. [10] Joshi, P.V., & Bhaerao,M.J, Efficiency Evaluation of Banking Sector in India Based on Data Envelopment Analysis. Indian Journal of Commerce & Management Studies ,2(3),2011, pp 31-40 [11] Kaur, S., & Gupta, P.K, Productive Efficiency Mapping of the Indian Banking System Using Data Envelopment Analysis. Procedia Economics and Finance, 25, 2015, pp.227- 238 [12] Kaur.R., & Aggarwal.M, Performance Measurement with Data Envelopment Analysis: An Application to Indian Public Sector Banks, Intenational Journal of Science Technology and Management, 6(1), 2017, pp 912-922. [13] Manandhar, R., & Tang, J.C, The Evaluation Bnak Branch Performance using Data Envelopment Analysis: A Framework. The Journal of High Technology Management Research, 13(1), 2002, pp 1-17. [14] Madhvi & Srivastava.A, Measuring Efficiency of Commercial Banks in India - A DEA Study, British Journal of Economics, Finance and Management Sciences 82,13 (2),2017, pp 82-92. [15] Mohamed Shahwan, T., & Mohammed Hassan, Y, Efficiency Analysis of UAE Banks Using Data Envelopment Analysis. Journal of Economics and Administrative Science, 29(1), 2013, pp 4-20. [16] Najafi, E., & Aryanezhad, M, A BSC-DEA Approach to Measuring the Relative Efficiency of the Service Industry: A case study of the banking Sector. International Journal of Industrial Engineering Computations, 2(2), 2011, pp 273-282 [17] Othman, F.M.,Mohd-Zamil, N. A., Rasid, S. Z. A., Vakilbashi, A.,& Mokhber,M, Data Envelopment Analysis: A Tool of Measuring Efficiency in Banking Sector. International Journal of Economics and Financial Issues, 6(3), 2016, pp 911-916. http://www.iaeme.com/IJM/index.asp 168 editor@iaeme.com
Chinmaya Kumar Rout, Dr. Prafulla Kumar Swain and Dr. Manoranjan Dash [18] Rao.N.E.S.V. & Gudala.C, Performance Appraisal and Ranking of DCCBs through Malmquist Index and Super-Efficiency , IOSR Journal of Economics and Finance (IOSR- JEF), 6(1), 2015, pp 88-96. [19] Rezaeiani, M. J., & Foroughi, A.A, Ranking efficient decision making units in Data Envelopment Analysis based on reference frontier share. European Journal of Operational Reserach, 264(2), 2018, pp 665-674. [20] Saha, A., & Ravisankar,T.S, Rating of Indian Commercial Banks: A DEA Approach. European Journal of Operational Research, 124(1), 2000, pp 187-203. [21] Sathye,M, The Efficiency of bank in Developing Economy: The case of India. European Journal of Operational Research,148 (3), 2003, pp 662-671. [22] S. Sathyakala, Umaya Salma Shajahan and P. Kamalakannan, An Examination of the Relationship Between Spread and Burden in Determining the Financial Efficiency: A Study of New Generation Private Banks in India, International Journal of Mechanical Engineering and Technology 10(3), 2019, pp. 1713–1724 [23] Sharma, A. K., Sharma, D., & Barua, M. K. (2012). Efficiency and Productivity of Indian Banks: An Application of Data Envelopment Analysis and Tobit Regression. In National Conference on Emerging challengess for Sustainable Business. 2012, pp.81-90. [24] Shirvani, H., Taj, S., & Mirshab. B, A new approach to Data Evvelopment Analysis with an application to bank efficiency in Turkey. Banks and Bank System. 6(1), 2011, pp 5-10. [25] Sanobar Anjum, A Study of Various Technique’s for Measuring Banking Efficiency – A Significant Look at Data Envelopment Analysis, International Journal of Management (IJM), Volume 3, Issue 2, May-August (2012) [26] Sinha,R.P, Performance Evaluation of Indian Commercial Banks Using the Super Efficiency Approach. Management and Labour Studies, 32(4), 2007, pp 552-564. [27] Siddiqui, S.A, A Study of Various Technique’s for Measuring Banking Efficiency-A Significant look at Data Envelopment Analysis. International Journal of Management,3 (2),2012, pp 381-386. [28] Subramanyam, T., & Reddy, C.S, Measuring the risk Efficiency in Indian Commercial Banking- A DEA Approach. East-West Journal of Economics and Business, 11 (1-2), 2008, pp 76-105. [29] Satyendra Sharma and Dr.Jayant Negi, Prioritization of Voice of Customers by Using Kano Questionnaire And Data Envelopment Analysis, International Journal of Industrial Engineering Research and Development (IJIERD), Volume 4, Issue 1, January - April (2013) [30] Syamni, G., & Abd Majid, M.S, Efficiency of Saving and Credit Cooperative Units In North Aceh, Indonesia. Signfiikan: Jurnal llmu Ekonomi, 5(2), 2016, pp 99-118. [31] Titko, J.,Stankeviciene, J., & Lace,N, Measuring Bank Efficiency: DEA Application. Technological and Economic Development of Economy,20(4), 2014, pp 739-757. [32] Varadi. V.K., Mavaluri, P. K., & Boppana, N, Measurement of Efficiency of Banks In India. Retrieved from.https://mpra.ub.uni-muenchen.de/17350/, 2006 http://www.iaeme.com/IJM/index.asp 169 editor@iaeme.com