Measuring the Efficiency of Financial Cloud Services in the Banking Industry Using the Modified Dynamic DEA with Network Structure: The Case of Iran E-Banking.
محورهای موضوعی : Multi-Criteria Decision Analysis and its Application in Financial Management
Alireza Poordavoodi
1
,
Mohammad Reza Moazami Goudarzi
2
,
Hamid Haj Seyyed Javadi
3
,
Amir Masoud Rahmani
4
1 - Department of Computer Engineering, Borujerd Branch, Islamic Azad University, Borujerd, Iran
2 - Department of Mathematics, Borujerd Branch, Islamic Azad University, Borujerd, Iran.
3 - Department of Mathematics and Computer Science, Shahed University, Tehran, Iran
4 - Department of Computer Science, Khazar University, Baku, Azerbaijan
کلید واژه: Dynamic Network model, Banking industry, Financial Cloud Computing, Data envelopment analysis, QoS Attributes,
چکیده مقاله :
Nowadays, the great benefits of cloud computing have dramatically increased the number of e-banking users. Hence, the competition in the banking industry has boosted and managers need to evaluate their branches on a regular basis. To this end, this study aims to evaluate cloud-based banking systems based on the Quality of Service (QoS) attributes using the Dynamic Network Data Envelopment Analysis (DNDEA) model. The main advantage of this research is that the efficiency of cloud-based bank branches can be estimated more realistically according to their internal structure over a specific time span. To conduct the experiment, 40 bank branches in Iran are analyzed by considering between-period and divisional interactions during 2018-2019. A cloud-based bank branch is conceptualized as a set of three inter-connected divisions including capabilities, intermediate process, and profitabilities. Some outputs of sub-DMUs 2 and 3 are treated as desirable and undesirable carry-overs between consecutive periods. In addition, the cost items and QoS attributes are considered as the inputs and outputs of divisions, respectively. The results indicate that 28 bank branches were efficient and all of the inefficiencies fall in divisions 1 and 3. Moreover, the number of efficient branches has been reduced from 2018 to 2019.
[1] Asadi, S., Nilashi, M., Husin, A.R.C., Yadegaridehkordi, E., Customers Perspectives on Adoption of Cloud Computing in Banking Sector, Information Technology and Management, 2017, 18(4), P. 305-330. Doi: 10.1007/s10799-016-0270-8.
[2] Lian, J. W., Critical Factors for Cloud Based E-Invoice Service Adoption in Taiwan: An Empirical Study, International Journal of Information Management, 2015, 35(1), P. 98-109. Doi: 10.1016/j.ijinfomgt.2014.10.005.
[3] Roy, S.K., Kesharwani, A., Bisht, S.S., The Impact of Trust and Perceived Risk on Internet Banking Adoption in India, International Journal of Bank Marketing, 2012, 30(4), P. 303-322.Doi: 10.1108/02652321211236923.
[4] Huang, C. W., Chiu, Y. H., Lin, C. H., Liu, H. H., Using a Hybrid Systems DEA Model to Analyze the Influence of Automatic Banking Service on Commercial Banks' efficiency, Journal of the Operations Research Society of Japan, 2012, 55(4), P. 209-224. Doi: 10.15807/jorsj.55.209.
[5] Zhou, P., Ang, B.W., Poh, K.-L., A Survey of Data Envelopment Analysis in Energy and Environmental Studies, European journal of operational research, 2008, 189(1), P. 1-18.Doi: 10.1016/j.ejor.2007.04.042.
[6] Kao, C., Network Data Envelopment Analysis: A Review, European journal of operational research, 2014, 239(1), P. 1-16. Doi: 10.1016/j.ejor.2014.02.039.
[7] Izadikhah, M., Saen, R., Evaluating Sustainability of Supply Chains by Two-Stage Range Directional Measure in the Presence of Negative Data, Transportation Research Part D: Transport and Environment, 2016, 49, P. 110-126. Doi: 10.1016/j.trd.2016.09.003.
[8] Kao, C., Efficiency Decomposition in Network Data Envelopment Analysis: A Relational Model, European journal of operational research, 2009, 192(3), P. 949-962. Doi: 10.1016/j.ejor.2007.10.008.
[9] Izadikhah, M., Saen, R., Assessing Sustainability of Supply Chains by Chance-Constrained Two-Stage DEA Model in the Presence of Undesirable Factors, Computers & Operations Research, 2018, 100, P. 343-367. Doi: 10.1016/j.cor.2017.10.002.
[10] Tone, K., Tsutsui, M., Dynamic DEA with Network Structure: A Slacks-Based Measure Approach, Omega, 2014, 42(1), P. 124-131. Doi: 10.1016/j.omega.2013.04.002.
[11] Tone, K., Tsutsui, M., Dynamic DEA: A Slacks-Based Measure Approach, Omega, 2010, 38(3-4), P. 145-156. Doi: 10.1016/j.omega.2009.07.003.
[12] Charnes, A., Cooper, W.W., Rhodes, E., Measuring the Efficiency of Decision Making Units, European journal of operational research, 1978, 2(6), P. 429-444. Doi: 10.1016/0377-2217(78)90138-8.
[13] Stolzer, A.J., Friend, M.A., Truong, D., Tuccio, W.A., Aguiar, M., Measuring and Evaluating Safety Management System Effectiveness Using Data Envelopment Analysis, Safety science, 2018, 104, P. 55-69. Doi: 10.1016/j.ssci.2017.12.037.
[14] Soheilirad, S., Govindan, K., Mardani, A., Zavadskas, E.K., Nilashi, M., Zakuan, N., Application of Data Envelopment Analysis Models in Supply Chain Management: A Systematic Review and Meta-Analysis, Annals of Operations Research, 2018, 271(2), P. 915-969. Doi: 10.1007/s10479-017-2605-1.
[15] Yang, L., Zhang, X., Assessing Regional Eco-Efficiency from the Perspective of Resource, Environmental and Economic Performance in China: A Bootstrapping Approach in Global Data Envelopment Analysis, Journal of Cleaner Production, 2018, 173, P. 100-111. Doi: 10.1016/j.jclepro.2016.07.166.
[16] González-Garay, A., Pozo, C., Galán-Martín, Á., Brechtelsbauer, C., Chachuat, B., Chadha, D., Hale, C., Hellgardt, K., Kogelbauer, A., Matar, O.K., Assessing the Performance of Uk Universities in the Field of Chemical Engineering Using Data Envelopment Analysis, Education for Chemical Engineers, 2019, 29, P. 29-41. Doi: 10.1016/j.ece.2019.06.003.
[17] Esfandiar, M., Saremi, M., Jahangiri Nia, H., Assessment of the Efficiency of Banks Accepted in Tehran Stock Exchange Using the Data Envelopment Analysis Technique, Advances in Mathematical Finance and Applications, 2018, 3(2), P. 1-11. Doi: 10.22034/amfa.2018.540815.
[18] Izadikhah, M., Improving the Banks Shareholder Long Term Values by Using Data Envelopment Analysis Model, Advances in Mathematical Finance and Applications, 2018, 3(2), P. 27-41.Doi: 10.22034/amfa.2018.540829.
[19] Razipour-GhalehJough, S., Lotfi, F.H., Jahanshahloo, G., Rostamy-Malkhalifeh, M., Sharafi, H., Finding Closest Target for Bank Branches in the Presence of Weight Restrictions Using Data Envelopment Analysis, Annals of Operations Research, 2019, P. 1-33. Doi: 10.1007/s10479-019-03166-6.
[20] Nasseri, S.H., Ebrahimnejad, A., Gholami, O., Fuzzy Stochastic Data Envelopment Analysis with Undesirable Outputs and Its Application to Banking Industry, International journal of fuzzy systems, 2018, 20(2), P. 534-548. Doi: 10.1007/s40815-017-0367-1.
[21] Kao, C., Liu, S. T., Multi-Period Efficiency Measurement in Data Envelopment Analysis: The Case of Taiwanese Commercial Banks, Omega, 2014, 47, P. 90-98. Doi: 10.1016/j.omega.2013.09.001.
[22] Shafiee, M., Sangi, M., and Ghaderi, M., Bank Performance Evaluation Using Dynamic DEA: A Slacks-Based Measure Approach, Journal of Data Envelopment Analysis and Decision Science, 2013, 26, P. 1-12. Doi: 10.5899/2013/dea-00026.
[23] Izadikhah, M., Tavana, M., Di Caprio, D., and Santos-Arteaga, F.J., A Novel Two-Stage DEA Production Model with Freely Distributed Initial Inputs and Shared Intermediate Outputs, Expert Systems with Applications, 2018, 99, P. 213-230. Doi: 10.1016/j.eswa.2017.11.005.
[24] Mahmoudabadi, M.Z., Emrouznejad, A., Comprehensive Performance Evaluation of Banking Branches: A Three-Stage Slacks-Based Measure (SBM) Data Envelopment Analysis, International Review of Economics & Finance, 2019, 64, P. 359-376. Doi: 10.1016/j.iref.2019.08.001.
[25] Akbari, S., Heydari, J., Keramati, M., Keramati, A., Designing a Mixed System of Network Dea for Evaluating the Efficiency of Branches of Commercial Banks in Iran, Advances in Mathematical Finance and Applications, 2019, 4(1), P. 1-13. Doi: 10.22034/amfa.2019.582260.1165.
[26] Huang, T. H., Chen, K. C., and Lin, C. I., An Extension from Network DEA to Copula-Based Network Sfa: Evidence from the Us Commercial Banks in 2009, The Quarterly Review of Economics and Finance, 2018, 67, P. 51-62. Doi: 10.1016/j.qref.2017.04.007.
[27] Barat, M., Tohidi, G., Sanei, M., Razavyan, S., Data Envelopment Analysis for Decision Making Unit with Nonhomogeneous Internal Structures: An Application to the Banking Industry, Journal of the Operational Research Society, 2019, 70(5), P. 760-769. Doi: 10.1080/01605682.2018.1457483.
[28] Mahmoudi, R., Emrouznejad, A., Rasti-Barzoki, M., A Bargaining Game Model for Performance Assessment in Network DEA Considering Sub-Networks: A Real Case Study in Banking, Neural Computing and Applications, 2019, 31(10), P. 6429-6447. Doi: 10.1007/s00521-018-3428-y.
[29] Zhong, S., Du, L., Wang, H., China City Commercial Bank Based on Network DEA Empirical Study of Operational Efficiency, 2019. Doi: 10.25236/meici.2019.047.
[30] Chao, C. M., Yu, M. M., Hsiung, N. H., Chen, L. H., Profitability Efficiency, Marketability Efficiency and Technology Gaps in Taiwan’s Banking Industry: Meta-Frontier Network Data Envelopment Analysis, Applied Economics, 2018, 50(3), P. 233-250. Doi: 10.1080/00036846.2017.1316827.
[31] Tavana, M., Izadikhah, M., Di Caprio, D., Saen, R.F., A New Dynamic Range Directional Measure for Two-Stage Data Envelopment Analysis Models with Negative Data, Computers & Industrial Engineering, 2018, 115, P. 427-448. Doi: 10.1016/j.cie.2017.11.024.
[32] Yu, Y., Huang, J., Shao, Y., The Sustainability Performance of Chinese Banks: A New Network Data Envelopment Analysis Approach and Panel Regression, Sustainability, 2019, 11(6), P. 1622. Doi: 10.3390/su11061622.
[33] Niknafs, J., Keramati, M.A., Monfared, J.H., Estimating Efficiency of Bank Branches by Dynamic Network Data Envelopment Analysis and Artificial Neural Network, Advances in Mathematical Finance and Applications, 5(3), P. 1-15. Doi: 10.22034/amfa.2019.1585957.1192.
[34] Wanke, P., Azad, M.A.K., Emrouznejad, A., Antunes, J., A Dynamic Network DEA Model for Accounting and Financial Indicators: A Case of Efficiency in Mena Banking, International Review of Economics & Finance, 2019, 61, P. 52-68. Doi: 10.1016/j.iref.2019.01.004.
[35] Kweh, Q.L., Lu, W.-M., Nourani, M., and Ghazali@ Mohd Zain, M.H., Risk Management and Dynamic Network Performance: An Illustration Using a Dual Banking System, Applied Economics, 2018, 50(30), P. 3285-3299. Doi: 10.1080/00036846.2017.1420889.
[36] Zhou, Z., Amowine, N., Huang, D., Quantitative Efficiency Assessment Based on the Dynamic Slack-Based Network Data Envelopment Analysis for Commercial Banks in Ghana, South African Journal of Economic and Management Sciences, 2018, 21(1), P. 1-11. Doi: 10.4102/sajems.v21i1.1717
[37] Thabit, F., Alhomdy, S.A.H., and Jagtap, S.B., Toward a Model for Cloud Computing Banking in Yemen, International Journal of Research in Advanced Engineering and Technology, 2019, 5(4), P. 14-18. Doi: 10.2139/ssrn.3484881.
[38] Willcocks, L., Reynolds, P., The Commonwealth Bank of Australia–Strategizing from Outsourcing to the Cloud Part 1: Perennial Challenges Amidst Turbulent Technology, Journal of Information Technology Teaching Cases, 2015, 4(2), P. 86-98. Doi: 10.1057/jittc.2014.6.
[39] Jatoth, C., Gangadharan, G., Fiore, U., Evaluating the Efficiency of Cloud Services Using Modified Data Envelopment Analysis and Modified Super-Efficiency Data Envelopment Analysis, Soft Computing, 2017, 21(23), P. 7221-7234. Doi: 10.1007/s00500-016-2267-y.
[40] Kao, H.Y., Wu, D. J., Huang, C.H., Evaluation of Cloud Service Industry with Dynamic and Network DEA Models, Applied Mathematics and Computation, 2017, 315, P. 188-202.Doi: 10.1016/j.amc.2017.07.059.
[41] Poordavoodi, A., Goudarzi, M. R. M., Javadi, H. H. S., Rahmani, A. M., Izadikhah, M., Toward a More Accurate Web Service Selection Using Modified Interval DEA Models with Undesirable Outputs, Computer Modeling in Engineering & Sciences, 2020, 123(2), P. 525--570. Doi: 10.32604/cmes.2020.08854.
[42] Azadi, M., Izadikhah, M., Ramezani, F., Hussain, F., A Mixed Ideal and Anti-Ideal DEA Model: An Application to Evaluate Cloud Service Providers, IMA Journal of Management Mathematics, 2020, 31(2), P. 233-256. Doi: 10.1093/imaman/dpz012.
[43] Tone, K., A Slacks-Based Measure of Efficiency in Data Envelopment Analysis, European journal of operational research, 2001, 130(3), P. 498-509.
[44] Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I., Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility, Future Generation computer systems, 2009, 25(6), P. 599-616. Doi: 10.1016/j.future.2008.12.001.
[45] Mell, P., Grance, T., The Nist Definition of Cloud Computing, 2011. Doi: 10.6028/NIST.SP.800-145.
[46] Chang, V., Ramachandran, M., Financial Modeling and Prediction as a Service, Journal of Grid Computing, 2017, 15(2), P. 177-195. Doi: 10.1007/s10723-017-9393-3.
[47] Doelitzscher, F., Sulistio, A., Reich, C., Kuijs, H., Wolf, D., Private Cloud for Collaboration and E-Learning Services: From IaaS to SaaS, Computing, 2011, 91(1), P. 23-42. Doi: 10.1007/s00607-010-0106-z.
[48] Lin, K.W., Deng, D. J., A Novel Parallel Algorithm for Frequent Pattern Mining with Privacy Preserved in Cloud Computing Environments, International Journal of Ad Hoc and Ubiquitous Computing, 2010, 6(4), P. 205-215. Doi: 10.1504/IJAHUC.2010.035533.
[49] Az-Zahra, T.S., The Advantages from Cloud Computing Application Towards Smme (Umkm), Jurnal Online Informatika, 2019, 4(1), P. 28-32. Doi: 10.15575/join.v4i1.307.
[50] Soltanzadeh, E., Omrani, H., Dynamic Network Data Envelopment Analysis Model with Fuzzy Inputs and Outputs: An Application for Iranian Airlines, Applied Soft Computing, 2018, 63, P. 268-288. Doi: 10.1016/j.asoc.2017.11.031.
[51] Mehrabian, S., Jahanshahloo, G.R., Alirezaee, M.R., Amin, G.R., An Assurance Interval for the Non-Archimedean Epsilon in DEA Models, Operations Research, 2000, 48(2), P. 344-347. Doi: 10.1287/opre.48.2.344.12381.
[52] Toloo, M., The Role of Non-Archimedean Epsilon in Finding the Most Efficient Unit: With an Application of Professional Tennis Players, Applied Mathematical Modelling, 2014, 38(21-22), P. 5334-5346. Doi: 10.1016/j.apm.2014.04.010.
[53] Tone, K., Toloo, M., Izadikhah, M., A Modified Slacks-Based Measure of Efficiency in Data Envelopment Analysis, European Journal of Operational Research, 2020. Doi: 10.1016/j.ejor.2020.04.019.
[54] Tone, K., Chang, T.S., Wu, C.-H., Handling Negative Data in Slacks-Based Measure Data Envelopment Analysis Models, European Journal of Operational Research, 2020, 282(3), P. 926-935. Doi: 10.1016/j.ejor.2019.09.055.
[55] Zeng, L., Benatallah, B., Ngu, A.H., Dumas, M., Kalagnanam, J., Chang, H., Qos-Aware Middleware for Web Services Composition, IEEE Transactions on software engineering, 2004, 30(5), P. 311-327. Doi: 10.1109/TSE.2004.11.
[56] Garg, S.K., Versteeg, S.,Buyya, R., A Framework for Ranking of Cloud Computing Services, Future Generation Computer Systems, 2013, 29(4), P. 1012-1023. Doi: 10.1016/j.future.2012.06.006.
[57] Al-Masri, E., Mahmoud, Q.H., Discovering the Best Web Service, in Proceedings of the 16th international conference on World Wide Web, 2007. Doi: 10.1145/1242572.1242795.
[58] Villasenor Alva, J.A., Estrada, E.G., A Generalization of Shapiro–Wilk's Test for Multivariate Normality, Communications in Statistics—Theory and Methods, 2009, 38(11), P. 1870-1883. Doi: 10.1080/03610920802474465.
[59] Massey Jr, F.J., The Kolmogorov-Smirnov Test for Goodness of Fit, Journal of the American statistical Association, 1951, 46(253), P. 68-78. Doi: 10.1080/01621459.1951.10500769.
[60] Chen, F., Dou, R., Li, M., and Wu, H., A Flexible Qos-Aware Web Service Composition Method by Multi-Objective Optimization in Cloud Manufacturing, Computers & Industrial Engineering, 2016, 99, P. 423-431. Doi: 10.1016/j.cie.2015.12.018.
[61] Despotis, D.K., Improving the Discriminating Power of DEA: Focus on Globally Efficient Units, Journal of the Operational Research Society, 2002, 53(3), P. 314-323. Doi: 10.1057/palgrave.jors.2601253.
[62] da Silva, A.F., Marins, F.A.S., Dias, E.X., Improving the Discrimination Power with a New Multi-Criteria Data Envelopment Model, Annals of Operations Research, 2020, 287(1), P. 127-159. Doi: 10.1007/s10479-019-03446-1.
[63] Ebrahimnejad, A., Ziari, S., New Model for Improving Discrimination Power in DEA Based on Dispersion of Weights, International Journal of Mathematics in Operational Research, 2019, 14(3), P. 433-450. Doi: 10.1504/IJMOR.2019.099388.
[64] Peykani, P., Mohammadi, E., Emrouznejad, A., Pishvaee, M.S., Rostamy-Malkhalifeh, M., Fuzzy Data Envelopment Analysis: An Adjustable Approach, Expert Systems with Applications, 2019, 136, P. 439-452. Doi: 10.1016/j.eswa.2019.06.039.
[65] Peykani, P., Mohammadi, E., Pishvaee, M.S., Rostamy-Malkhalifeh, M., and Jabbarzadeh, A., A Novel Fuzzy Data Envelopment Analysis Based on Robust Possibilistic Programming: Possibility, Necessity and Credibility-Based Approaches, RAIRO-Operations Research, 2018, 52(4-5), P. 1445-1463. Doi: 10.1051/ro/2018019
[66] Peykani, P., Mohammadi, E., Rostamy-Malkhalifeh, M., Hosseinzadeh Lotfi, F., Fuzzy Data Envelopment Analysis Approach for Ranking of Stocks with an Application to Tehran Stock Exchange, Advances in Mathematical Finance and Applications, 2019, 4(1), P. 31-43. Doi: 10.22034/amfa.2019.581412.1155.
[67] Rostamy-Malkhalifeh, M., Mollaeian, E., Evaluating Performance Supply Chain by a New Non-Radial Network DEA Model with Fuzzy Data, Science, 2012, 9. Doi: 10.5899/2012/dea-00005.
[68] Peykani, P., Mohammadi, E., Interval Network Data Envelopment Analysis Model for Classification of Investment Companies in the Presence of Uncertain Data, Journal of Industrial and Systems Engineering, 2018, 11(Special issue: 14th International Industrial Engineering Conference), P. 63-72.
[69] Lotfi, F.H., Navabakhs, M., Tehranian, A., Rostamy-Malkhalifeh, M., Shahverdi, R., Ranking Bank Branches with Interval Data—the Application of DEA, in International Mathematical Forum, 2007. Citeseer.
[70] Peykani, P., Mohammadi, E., Saen, R.F., Sadjadi, S.J., Rostamy‐Malkhalifeh, M., Data Envelopment Analysis and Robust Optimization: A Review, Expert Systems, 2020, P. e12534. Doi: 10.1111/exsy.12534.
[71] Peykani, P., Mohammadi, E., Jabbarzadeh, A., Jandaghian, A., Utilizing Robust Data Envelopment Analysis Model for Measuring Efficiency of Stock, a Case Study: Tehran Stock Exchange, Journal of New Researches in Mathematics, 2016, 1(4), P. 15-24.
[72] Peykani, P., Mohammadi, E., Seyed Esmaeili, F.S., Stock Evaluation under Mixed Uncertainties Using Robust DEA Model, Journal of Quality Engineering and Production Optimization, 2019, 4(1), P. 73-84. Doi: 10.22070/jqepo.2019.3652.1080.
[73] Rostamy-Malkhalifeh, M., Mollaeian, E., and Mamizadeh-Chatghayeh, S., A New Non-Radial Network DEA Model for Evaluating Performance Supply Chain, Indian Journal of Science and Technology, 2013, 6(3), P. 4188-4192.
[74] Peykani, P., Mohammadi, E., Window Network Data Envelopment Analysis: An Application to Investment Companies, International Journal of Industrial Mathematics, 2020, 12(1), P. 89-99.
[75] Nikfarjam, H., Rostamy-Malkhalifeh, M., Mamizadeh-Chatghayeh, S., Measuring Supply Chain Efficiency Based on a Hybrid Approach, Transportation Research Part D: Transport and Environment, 2015, 39, P. 141-150. Doi: 10.1016/j.trd.2015.06.004.
