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Evaluating the performance of Colombian banks by hybrid multicriteria decision making methods

    Amir Karbassi Yazdi   Affiliation
    ; Thomas Hanne   Affiliation
    ; Juan Carlos Osorio Gómez   Affiliation

Abstract

The aim of the study in this paper is to show how the performance of banks can be evaluated by ranking them based on Balanced Scorecard (BSC) and Multicriteria Decision Making (MCDM) methods. Nowadays, assessing the performance of companies is a vital work for finding their weaknesses and strengths. The banking sector is an important area in the service sector. Many people want to know which bank performs best when entrusting their money to them. For assessing the performance of banks, BSC can be used. This method helps to translate strategic issues to meaningful insights for the respective financial institutions. After that, the banks will be ranked based on performance indicators by the Weighted Aggregated Sum Product Assessment (WASPAS) method. Because this method is based on a decision matrix, weights are required. To find such weights, the Step-wise Weight Assessment Ratio Analysis (SWARA) method is applied. The results show that the International Bank of Colombia has a much better performance than other Colombian banks. Besides, further insights regarding the evaluation process based on BSC, SWARA, and WASPAS are obtained.

Keyword : weighted aggregated sum product assessment, step-wise weight assessment ratio analysis, balanced scorecard, performance evaluation, multicriteria decision analysis, banking sector

How to Cite
Yazdi, A. K., Hanne, T., & Osorio Gómez, J. C. (2020). Evaluating the performance of Colombian banks by hybrid multicriteria decision making methods. Journal of Business Economics and Management, 21(6), 1707-1730. https://doi.org/10.3846/jbem.2020.11758
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Oct 19, 2020
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Akkermans, H. A., & van Oorschot, K. E. (2018). Relevance assumed: A case study of balanced scorecard development using system dynamics. In M. Kunc (Ed.), System dynamics (pp. 107–132). Springer. https://doi.org/10.1057/978-1-349-95257-1_4

Akkoç, S., & Vatansever, K. (2013). Fuzzy performance evaluation with AHP and Topsis methods: evidence from Turkish banking sector after the global financial crisis. Eurasian Journal of Business and Economics, 6(11), 53–74.

Amelec, V., & Carmen, V. (2015a). Relationship between variables of performance social and financial of microfinance institutions. Advanced Science Letters, 21(6), 1931–1934. https://doi.org/10.1166/asl.2015.6163

Amelec, V., & Carmen, V. (2015b). Validation of a model for productivity evaluation for microfinance institutions. Advanced Science Letters, 21(5), 1610–1614. https://doi.org/10.1166/asl.2015.6117

Asli, M. N., Dalfard, V. M., & Poursalik, K. (2013). A combination model using strategic alignment model and balanced scorecard and strategies’ prioritisation based on TOPSIS technique. International Journal of Productivity and Quality Management, 12(3), 313–326. https://doi.org/10.1504/IJPQM.2013.056151

Asmild, M., Paradi, J. C., Aggarwall, V., & Schaffnit, C. (2004). Combining DEA window analysis with the Malmquist index approach in a study of the Canadian banking industry. Journal of Productivity Analysis, 21(1), 67–89. https://doi.org/10.1023/B:PROD.0000012453.91326.ec

Azadeh, A., Haghighi, S. M., Zarrin, M., & Khaefi, S. (2015). Performance evaluation of Iranian electricity distribution units by using stochastic data envelopment analysis. International Journal of Electrical Power & Energy Systems, 73, 919–931. https://doi.org/10.1016/j.ijepes.2015.06.002

Babkin, A. V., Lipatnikov, V. S., & Muraveva, S. V. (2015). Assessing the impact of innovation strategies and R&D costs on the performance of IT companies. Procedia – Social and Behavioral Sciences, 207, 749–758. https://doi.org/10.1016/j.sbspro.2015.10.153

Bazrkar, A., Iranzadeh, S., & Feghhi Farahmand, N. (2017). Total quality model for aligning organization strategy, improving performance, and improving customer satisfaction by using an approach based on combination of balanced scorecard and lean six sigma. Cogent Business & Management, 4(1), 1390818. https://doi.org/10.1080/23311975.2017.1390818

Beheshtinia, M. A., & Omidi, S. (2017). A hybrid MCDM approach for performance evaluation in the banking industry. Kybernetes, 46(8), 1386–1407. https://doi.org/10.1108/K-03-2017-0105

Beheshtinia, M., & Nemati-Abozar, V. (2017). A novel hybrid fuzzy multi-criteria decision-making model for supplier selection problem (A case study in advertising industry). Journal of Industrial and Systems Engineering, 9(4), 65–79.

Bennett, M., James, P., & Klinkers, L. (2017). Key themes in environmental, social and sustainability performance evaluation and reporting. In Sustainable measures (pp. 29–74). Routledge. https://doi.org/10.4324/9781351283007-2

Bentes, A. V., Carneiro, J., da Silva, J. F., & Kimura, H. (2012). Multidimensional assessment of organizational performance: Integrating BSC and AHP. Journal of Business Research, 65(12), 1790–1799. https://doi.org/10.1016/j.jbusres.2011.10.039

Bento, R. F., Mertins, L., & White, L. F. (2017). Ideology and the balanced scorecard: An empirical exploration of the tension between shareholder value maximization and corporate social responsibility. Journal of Business Ethics, 142(4), 769–789. https://doi.org/10.1007/s10551-016-3053-6

Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P. K., Brady, M., Tiwari, M. K., & Nudurupati, S. S. (2014). Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: a collaborative decision-making approach. Production Planning & Control, 25(8), 698–714. https://doi.org/10.1080/09537287.2013.798088

Chen, F.-H., Hsu, T.-S., & Tzeng, G.-H. (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. International Journal of Hospitality Management, 30(4), 908–932. https://doi.org/10.1016/j.ijhm.2011.02.001

Chen, Y.-C., Chiu, Y.-H., Huang, C.-W., & Tu, C. H. (2013). The analysis of bank business performance and market risk – Applying Fuzzy DEA. Economic Modelling, 32, 225–232. https://doi.org/10.1016/j.econmod.2013.02.008

Chernikov, V., Kushch, S., & Tikkanen, H. (2015). Customer empowerment and firm performance: Benefits and potential harm. In K. Kubacki (Ed.), Ideas in marketing: Finding the New and polishing the old (p. 138). Springer. https://doi.org/10.1007/978-3-319-10951-0_48

Child, J. (1974). Managerial and organizational factors associated with company performance part I. Journal of Management Studies, 11(3), 175–189. https://doi.org/10.1111/j.1467-6486.1974.tb00693.x

Claessens, S., & Horen, N. (2014). Foreign banks: Trends and impact. Journal of Money, Credit and Banking, 46(s1), 295–326. https://doi.org/10.1111/jmcb.12092

Cooper, D. J., Ezzamel, M., & Qu, S. Q. (2017). Popularizing a management accounting idea: The case of the balanced scorecard. Contemporary Accounting Research, 34(2), 991–1025. https://doi.org/10.1111/1911-3846.12299

Curtis, A., Li, V., & Patrick, P. H. (2018). The use of adjusted earnings in performance evaluation. SSRN. https://doi.org/10.2139/ssrn.2682652

Danesh Asgari, S., Haeri, A., & Jafari, M. (2017). Integration of balanced scorecard and three-stage data envelopment analysis approaches. Iranian Journal of Management Studies, 10(2), 527–550.

Dash, M. (2017). A Model for bank performance measurement integrating multivariate factor structure with multi-criteria PROMETHEE methodology. Asian Journal of Finance & Accounting, 9(1), 310–332. https://doi.org/10.5296/ajfa.v9i1.11073

Diamantini, C., Potena, D., & Storti, E. (2016). Extended drill‐down operator: Digging into the structure of performance indicators. Concurrency and Computation: Practice and Experience, 28(15), 3948-3968. https://doi.org/10.1002/cpe.3726

Dincer, H., & Hacioglu, U. (2013). Performance evaluation with fuzzy VIKOR and AHP method based on customer satisfaction in Turkish banking sector. Kybernetes, 42(7), 1072–1085. https://doi.org/10.1108/K-02-2013-0021

Dinçer, H., Hacıoğlu, Ü., & Yüksel, S. (2017). Balanced scorecard based performance measurement of European airlines using a hybrid multicriteria decision making approach under the fuzzy environment. Journal of Air Transport Management, 63, 17–33. https://doi.org/10.1016/j.jairtraman.2017.05.005

Emrouznejad, A., & Anouze, A. L. (2010). Data envelopment analysis with classification and regression tree – a case of banking efficiency. Expert Systems, 27(4), 231–246. https://doi.org/10.1111/j.1468-0394.2010.00516.x

Emrouznejad, A., & Yang, G. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4–8. https://doi.org/10.1016/j.seps.2017.01.008

Fallah Jelodar, M. (2016). Prioritization of the factors affecting bank efficiency using combined data envelopment analysis and analytical hierarchy process methods. Journal of Optimization, 2016, 5259817. https://doi.org/10.1155/2016/5259817

Fan, L. W., Pan, S. J., Liu, G. Q., & Zhou, P. (2017). Does energy efficiency affect financial performance? Evidence from Chinese energy-intensive firms. Journal of Cleaner Production, 151, 53–59. https://doi.org/10.1016/j.jclepro.2017.03.044

Faraglia, E., Marcet, A., & Scott, A. (2008). Fiscal insurance and debt management in OECD economies. The Economic Journal, 118(527), 363–386. https://doi.org/10.1111/j.1468-0297.2007.02125.x

Feng, C.-M., & Wang, R.-T. (2000). Performance evaluation for airlines including the consideration of financial ratios. Journal of Air Transport Management, 6(3), 133–142. https://doi.org/10.1016/S0969-6997(00)00003-X

Fornell, C., Morgeson III, F. V, & Hult, G. T. M. (2016). Stock returns on customer satisfaction do beat the market: Gauging the effect of a marketing intangible. Journal of Marketing, 80(5), 92–107. https://doi.org/10.1509/jm.15.0229

Franklin-Johnson, E., Figge, F., & Canning, L. (2016). Resource duration as a managerial indicator for Circular Economy performance. Journal of Cleaner Production, 133, 589–598. https://doi.org/10.1016/j.jclepro.2016.05.023

Gannon, B., Jones, C., McCabe, A., O’sullivan, R., & Wakai, A. (2017). An economic cost analysis of emergency department key performance indicators in Ireland. European Journal of Emergency Medicine, 24(3), 196–201. https://doi.org/10.1097/MEJ.0000000000000347

Gërguri‐Rashiti, S., Ramadani, V., Abazi‐Alili, H., Dana, L., & Ratten, V. (2017). ICT, innovation and firm performance: The transition economies context. Thunderbird International Business Review, 59(1), 93-102. https://doi.org/10.1002/tie.21772

Guo, H., Zhao, Y., Niu, T., & Tsui, K.-L. (2017). Hong Kong Hospital Authority resource efficiency evaluation: Via a novel DEA-Malmquist model and Tobit regression model. PloS One, 12(9), e0184211. https://doi.org/10.1371/journal.pone.0184211

Ha, M.-H., & Yang, Z. (2017). Comparative analysis of port performance indicators: Independency and interdependency. Transportation Research Part A: Policy and Practice, 103, 264–278. https://doi.org/10.1016/j.tra.2017.06.013

Hachicha, M., Fahad, M., Moalla, N., & Ouzrout, Y. (2016). Performance assessment architecture for collaborative business processes in BPM-SOA-based environment. Data & Knowledge Engineering, 105, 73–89. https://doi.org/10.1016/j.datak.2015.12.002

Hu, Y., Wen, J., & Yan, Y. (2015). Measuring the performance of knowledge resources using a value perspective: integrating BSC and ANP. Journal of Knowledge Management, 19(6), 1250–1272. https://doi.org/10.1108/JKM-10-2014-0431

Huang, A., & Badurdeen, F. (2017). Sustainable manufacturing performance evaluation: Integrating product and process metrics for systems level assessment. Procedia Manufacturing, 8, 563–570. https://doi.org/10.1016/j.promfg.2017.02.072

Kala, D., & Bagri, S. C. (2016). Designing the strategy map for hotels with key performance indicators of balanced scorecard using DEMATEL technique. International Journal of Business Excellence, 10(2), 240–263. https://doi.org/10.1504/IJBEX.2016.078005

Kanyurhi, E. B., & Bugandwa Mungu Akonkwa, D. (2016). Internal marketing, employee job satisfaction, and perceived organizational performance in microfinance institutions. International Journal of Bank Marketing, 34(5), 773–796. https://doi.org/10.1108/IJBM-06-2015-0083

Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard œ measures that drive performance. Harvard Business Review, (January–February).

Kaplan, R. S., & Norton, D. P. (1996a). Linking the balanced scorecard to strategy. California Management Review, 39(1), 53–79. https://doi.org/10.2307/41165876

Kaplan, R. S., & Norton, D. P. (1996b). Strategic learning & the balanced scorecard. Strategy & Leadership, 24(5), 18–24. https://doi.org/10.1108/eb054566

Kaplan, R. S., & Norton, D. P. (1996c). The balanced scorecard: translating strategy into action. Harvard Business Press.

Karbassi Yazdi, A., & Abdi, F. (2017). Designing robust model for banks benchmarking based on Rembrandt method and DEA. Benchmarking: An International Journal, 24(2), 431–444. https://doi.org/10.1108/BIJ-01-2015-0001

Kaya, T. (2018). Monitoring brand performance based on household panel indicators using a fuzzy rank-based ORESTE methodology. Journal of Multiple-Valued Logic & Soft Computing, 31, 443–467.

Kerai, S., & Saleh, A. (2017). Applying the balanced scorecard to improve student satisfaction, market share and profitability. The Applied Management Review, 1(1), 27–38.

Keršulienė, V., & Turskis, Z. (2011). Integrated fuzzy multiple criteria decision making model for architect selection. Technological and Economic Development of Economy, 17(4), 645–666. https://doi.org/10.3846/20294913.2011.635718

Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11(2), 243–258. https://doi.org/10.3846/jbem.2010.12

Kim, Y. H., & Kim, M. (2010). A new approach for assessment and comparison of websites: Using the modified balanced scorecard and analytical hierarchy process. Journal of Hospitality Marketing & Management, 19(6), 676–695. https://doi.org/10.1080/19368623.2010.493089

Kumar, M., & Vincent, C. (2011). Benchmarking Indian banks using DEA in post-reform period: A progressive time-weighted mean approach. The Service Industries Journal, 31(14), 2455–2485. https://doi.org/10.1080/02642069.2010.504818

Kumar, P., Singh, R. K., & Vaish, A. (2017). Suppliers’ green performance evaluation using fuzzy extended ELECTRE approach. Clean Technologies and Environmental Policy, 19(3), 809–821. https://doi.org/10.1007/s10098-016-1268-y

LaPlante, A. E., & Paradi, J. C. (2015). Evaluation of bank branch growth potential using data envelopment analysis. Omega, 52, 33–41. https://doi.org/10.1016/j.omega.2014.10.009

Lazzarotti, V., Manzini, R., & Mari, L. (2011). A model for R&D performance measurement. International Journal of Production Economics, 134(1), 212–223. https://doi.org/10.1016/j.ijpe.2011.06.018

Lee, P. T.-W., Lin, C.-W., & Shin, S.-H. (2018). Financial performance evaluation of shipping companies using entropy and grey relation analysis. In Multi-Criteria decision making in maritime studies and logistics (pp. 219–247). Springer. https://doi.org/10.1007/978-3-319-62338-2_9

Lee, S., Brownlee, E., Kim, Y., & Lee, S. (2017). Ticket sales outsourcing performance measures using balanced scorecard and analytic hierarchy process combined model. Sport Marketing Quarterly; Morgantown, 26(2), 110–120.

Lerner, J., Schoar, A., Sokolinski, S., & Wilson, K. (2018). The globalization of angel investments: Evidence across countries. Journal of Financial Economics, 127(1), 1–20. https://doi.org/10.1016/j.jfineco.2017.05.012

MacLeod, N. D., Ash, A. J., & McIvor, J. G. (2004). An economic assessment of the impact of grazing land condition on livestock performance in tropical woodlands. The Rangeland Journal, 26(1), 49–71. https://doi.org/10.1071/RJ04004

Majumdar, S., & Asgari, B. (2017). Performance analysis of listed companies in the UAE-using DEA Malmquist Index Approach. American Journal of Operations Research, 7(02), 133–151. https://doi.org/10.4236/ajor.2017.72010

Malagueño, R., Lopez-Valeiras, E., & Gomez-Conde, J. (2018). Balanced scorecard in SMEs: Effects on innovation and financial performance. Small Business Economics, 51(1), 221–244. https://doi.org/10.1007/s11187-017-9921-3

Mardani, A., Nilashi, M., Zakuan, N., Loganathan, N., Soheilirad, S., Saman, M. Z. M., & Ibrahim, O. (2017). A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments. Applied Soft Computing, 57, 265–292. https://doi.org/10.1016/j.asoc.2017.03.045

Mehralian, G., Nazari, J. A., Nooriparto, G., & Rasekh, H. R. (2017). TQM and organizational performance using the balanced scorecard approach. International Journal of Productivity and Performance Management, 66(1), 111–125. https://doi.org/10.1108/IJPPM-08-2015-0114

Mwencha, M., Rosen, J. E., Spisak, C., Watson, N., Kisoka, N., & Mberesero, H. (2017). Upgrading supply chain management systems to improve availability of medicines in Tanzania: Evaluation of performance and cost effects. Global Health: Science and Practice, 5(3), 399–411. https://doi.org/10.9745/GHSP-D-16-00395

Nielsen, E. H., & Nielsen, S. (2018). System Dynamics Modeling, its concept of causality and particular relevance for providing the Balanced Scorecard thinking with a dynamic analytical framework. Aarhus.

Ozkan, N., Cakan, S., & Kayacan, M. (2017). Intellectual capital and financial performance: A study of the Turkish Banking Sector. Borsa Istanbul Review, 17(3), 190–198. https://doi.org/10.1016/j.bir.2016.03.001

Paradi, J. C., Rouatt, S., & Zhu, H. (2011). Two-stage evaluation of bank branch efficiency using data envelopment analysis. Omega, 39(1), 99–109. https://doi.org/10.1016/j.omega.2010.04.002

Paradi, J. C., & Zhu, H. (2013). A survey on bank branch efficiency and performance research with data envelopment analysis. Omega, 41(1), 61–79. https://doi.org/10.1016/j.omega.2011.08.010

Patiar, A., & Wang, Y. (2016). The effects of transformational leadership and organizational commitment on hotel departmental performance. International Journal of Contemporary Hospitality Management, 28(3), 586–608. https://doi.org/10.1108/IJCHM-01-2014-0050

Podgórski, D. (2015). Measuring operational performance of OSH management system – A demonstration of AHP-based selection of leading key performance indicators. Safety Science, 73, 146–166. https://doi.org/10.1016/j.ssci.2014.11.018

Rabbani, A., Zamani, M., Yazdani-Chamzini, A., & Zavadskas, E. K. (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 Systems with Applications, 41(16), 7316–7327. https://doi.org/10.1016/j.eswa.2014.05.023

Sedady, F., & Beheshtinia, M. A. (2019). A novel MCDM model for prioritizing the renewable power plants’ construction. Management of Environmental Quality: An International Journal, 30(2), 383– 399. https://doi.org/10.1108/MEQ-05-2018-0102

Sadeghi, M., Razavi, S. H., & Saberi, N. (2013). Application of grey TOPSIS in preference ordering of action plans in balanced scorecard and strategy map. Informatica, 24(4), 619–635. https://doi.org/10.15388/Informatica.2013.07

Saeidi, S. P., Sofian, S., Saeidi, P., Saeidi, S. P., & Saaeidi, S. A. (2015). How does corporate social responsibility contribute to firm financial performance? The mediating role of competitive advantage, reputation, and customer satisfaction. Journal of Business Research, 68(2), 341–350. https://doi.org/10.1016/j.jbusres.2014.06.024

Sainaghi, R., Phillips, P., & d’Angella, F. (2018). The balanced scorecard of a new destination product: Implications for lodging and skiing firms. International Journal of Hospitality Management, 76 (Part A), 216–230. https://doi.org/10.1016/j.ijhm.2018.05.011

Šaparauskas, J., Kazimieras Zavadskas, E., & Turskis, Z. (2011). Selection of facade’s alternatives of commercial and public buildings based on multiple criteria. International Journal of Strategic Property Management, 15(2), 189–203. https://doi.org/10.3846/1648715X.2011.586532

Shafiee, M., Lotfi, F. H., Saleh, H., & Ghaderi, M. (2016). A mixed integer bi-level DEA model for bank branch performance evaluation by Stackelberg approach. Journal of Industrial Engineering International, 12(1), 81–91. https://doi.org/10.1007/s40092-015-0131-9

Shaverdi, M., Akbari, M., & Tafti, S. F. (2011). Combining fuzzy MCDM with BSC approach in performance evaluation of Iranian private banking sector. Advances in Fuzzy Systems, 2011, 148712. https://doi.org/10.1155/2011/148712

Singh, S., Darwish, T. K., & Potočnik, K. (2016). Measuring organizational performance: A case for subjective measures. British Journal of Management, 27(1), 214–224. https://doi.org/10.1111/1467-8551.12126

Sofiyabadi, J., Kolahi, B., & Valmohammadi, C. (2016). Key performance indicators measurement in service business: a fuzzy VIKOR approach. Total Quality Management & Business Excellence, 27(9–10), 1028–1042.

Sun, J., Wang, C., Ji, X., & Wu, J. (2017). Performance evaluation of heterogeneous bank supply chain systems from the perspective of measurement and decomposition. Computers & Industrial Engineering, 113, 891–903. https://doi.org/10.1016/j.cie.2017.05.028

Sundharam, V. N., Sharma, V., & Stephan Thangaiah, I. S. (2013). An integration of BSC and AHP for sustainable growth of manufacturing industries. International Journal of Business Excellence, 6(1), 77–92. https://doi.org/10.1504/IJBEX.2013.050577

Tan, H. X., Yeo, Z., Ng, R., Tjandra, T. B., & Song, B. (2015). A sustainability indicator framework for Singapore small and medium-sized manufacturing enterprises. Procedia Cirp, 29, 132–137. https://doi.org/10.1016/j.procir.2015.01.028

Tayeh, M., Al-Jarrah, I. M., & Tarhini, A. (2015). Accounting vs. market-based measures of firm performance related to information technology investments. International Review of Social Sciences and Humanities, 9(1), 129–145.

Torkzad, A., & Beheshtinia, M. A. (2019). Evaluating and prioritizing hospital service quality. International Journal of Health Care Quality Assurance, 32(2), 332–346. https://doi.org/10.1108/IJHCQA-03-2018-0082

Valmohammadi, C., & Sofiyabadi, J. (2015). Modeling cause and effect relationships of strategy map using fuzzy DEMATEL and fourth generation of balanced scorecard. Benchmarking: An International Journal, 22(6), 1175–1191. https://doi.org/10.1108/BIJ-09-2014-0086

Van der Westhuizen, G. (2008). Estimating technical and scale efficiency and sources of efficiency change in banks using balance sheet data: A South African study. Studies in Economics and Econometrics, 32(1), 23–46.

Varmazyar, M., Dehghanbaghi, M., & Afkhami, M. (2016). A novel hybrid MCDM model for performance evaluation of research and technology organizations based on BSC approach. Evaluation and Program Planning, 58, 125–140. https://doi.org/10.1016/j.evalprogplan.2016.06.005

Vilanova, M. R. N., Magalhães Filho, P., & Balestieri, J. A. P. (2015). Performance measurement and indicators for water supply management: Review and international cases. Renewable and Sustainable Energy Reviews, 43, 1–12. https://doi.org/10.1016/j.rser.2014.11.043

Wu, J., Zhu, Q., Yin, P., & Song, M. (2017). Measuring energy and environmental performance for regions in China by using DEA-based Malmquist indices. Operational Research, 17(3), 715–735. https://doi.org/10.1007/s12351-015-0203-z

Yang, C., & Liu, H.-M. (2012). Managerial efficiency in Taiwan bank branches: A network DEA. Economic Modelling, 29(2), 450–461. https://doi.org/10.1016/j.econmod.2011.12.004

Yazdi, A. K., Wang, Y. J., & Alirezaei, A. (2018). Analytical insights into firm performance: a fuzzy clustering approach for data envelopment analysis classification. International Journal of Operational Research, 33(3), 413–429. https://doi.org/10.1504/IJOR.2018.095630

Zahoor, A., & Sahaf, M. A. (2018). Investigating causal linkages in the balanced scorecard: an Indian perspective. International Journal of Bank Marketing, 36(1), 184–207. https://doi.org/10.1108/IJBM-09-2016-0128

Zavadskas, E. K., Antucheviciene, J., Saparauskas, J., & Turskis, Z. (2013). MCDM methods WASPAS and MULTIMOORA: verification of robustness of methods when assessing alternative solutions. Economic Computation and Economic Cybernetics Studies and Research, 47(2), 5–20.

Zavadskas, E. K., Antucheviciene, J., Šaparauskas, J., & Turskis, Z. (2013). Multi-criteria assessment of facades’ alternatives: peculiarities of ranking methodology. Procedia Engineering, 57, 107–112. https://doi.org/10.1016/j.proeng.2013.04.016

Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir Elektrotechnika, 122(6), 3–6. https://doi.org/10.5755/j01.eee.122.6.1810

Zhao, H., & Li, N. (2015). Evaluating the performance of thermal power enterprises using sustainability balanced scorecard, fuzzy Delphic and hybrid multi-criteria decision making approaches for sustainability. Journal of Cleaner Production, 108(Part A), 569–582. https://doi.org/10.1016/j.jclepro.2015.07.141

Zolfani, S. H., & Ghadikolaei, A. S. (2013). Performance evaluation of private universities based on balanced scorecard: empirical study based on Iran. Journal of Business Economics and Management, 14(4), 696–714. https://doi.org/10.3846/16111699.2012.665383