DEA Approaches for Financial Evaluation - A Literature Review

Document Type : Review Paper


Department of Mathematics, College of Science, Arak Branch, Islamic Azad University, Arak, Iran P. O. Box: 38135/567



Financial assessment has been of great interest to both academic and practitioners in the past decades. Among several performance assessment approaches, Data Envelopment Analysis (DEA) has become one of the crucial tools that have been commonly adopted to financially evaluate firms in various fields. The main aim of this review article is to review of DEA models in regarding to evaluation of the financial performance. This paper presents the first comprehensive and structured literature review of the use of DEA models for financially assessment. To this end, this paper reviewed and summarized the different models of DEA models that have been applied around the world to development of financial assessment problems. Consequently, a review of 455 published scholarly papers appearing in 160 journals between 1994 and 2021 have been obtained to achieve a comprehensive review of DEA application in financial efficiency. Accordingly, the selected articles have been categorized based on year of publication, authors, nationalities, scope of study, time duration, application area, study purpose, results, outcomes, etc. The discussion and the findings of this paper can be used as a guideline to analysts to determine the best fit financial assessment method when DEA evaluation is applied to any dataset. Future perspectives and challenges are discussed.


[1] Abbott, M., and Doucouliagos, C., The efficiency of Australian universities: A data envelopment analysis, Econ. Educ. Rev., 2003, 22 (1), P. 89–97. Doi: 10.1016/S0272-7757(01)00068-1
[2] Aggelopoulos, E., and Georgopoulos, A., Bank branch efficiency under environmental change: A bootstrap DEA on monthly profit and loss accounting statements of Greek retail branches, Eur. J. Oper. Res., 2017, 261 (3), P. 1170–1188. Doi: 10.1016/j.ejor.2017.03.009
[3] Ahtikoski, A., Karhu, J., Ahtikoski, R., Haapanen, M., Hynynen, J., and Kärkkäinen, K., Financial assessment of alternative breeding goals using stand-level optimization and data envelopment analysis, Scand. J. For. Res., 2020,  35 (5-6), P. 262–273. Doi: 10.1080/02827581.2020.1795241
[4] Al-Mana, A. A., Nawaz, W., Kamal, A., and Koҫ, M., Financial and operational efficiencies of national and international oil companies: An empirical investigation, Resour. Policy, 2020, 68,
Doi: 10.1016/j.resourpol.2020.101701.
[5] Alexander, W. R. J., Haug, A. A., and Jaforullah, M., A two-stage double-bootstrap data envelopment analysis of efficiency differences of New Zealand secondary schools, J. Product. Anal., 2010, 34, P. 99–110.
 Doi: 10.1007/s11123-010-0173-3
[6] Amersdorffer, F., Buchenrieder, G., Bokusheva, R., and Wolz, A., Efficiency in microfinance: Financial and social performance of agricultural credit cooperatives in Bulgaria, J. Oper. Res. Soc., 2015, 66 (1), P. 57–65. Doi: 10.1057/jors.2013.162
[7] Amin, G. R., and Hajjami, M., Application of Optimistic and Pessimistic OWA and DEA Methods in Stock Selection, Int. J. Intell. Syst., 2016, 31 (12), P. 1220–1233. Doi: 10.1002/int.21824
[8] Arabi, B., Munisamy, S., Emrouznejad, A., Toloo, M., and Ghazizadeh, M. S., Eco-efficiency considering the issue of heterogeneity among power plants, Energy, 2016, 111, P. 722–735. Doi: 10.1016/
[9] Arsad, R., Nasir Abdullah, M., Alias, S., and Isa, Z., Selection Input Output by Restriction Using DEA Models Based on a Fuzzy Delphi Approach and Expert Information, in J. Phys. Conf. Ser., 2017.
[10] Athanassopoulos, A. D., and Curram, S. P., A comparison of data envelopment analysis and artificial neural networks as tools for assessing the efficiency of decision making units, J. Oper. Res. Soc., 1996, 47 (8), P. 1000–1016. Doi: 10.2307/3010408
[11] Avkiran, N. K., Developing foreign bank efficiency models for DEA grounded in finance theory, Socioecon. Plann. Sci., 2006, 40 (4), P. 275–296. Doi: 10.1016/j.seps.2004.10.006
[12] Aydın, U., Karadayı, M. A., Ülengin, F., and Ülengin, K. B., Enhanced Performance Assessment of Airlines with Integrated Balanced Scorecard, Network-Based Superefficiency DEA and PCA Methods, Contrib. to Manag. Sci., 2021, P. 225–247. Doi: 10.1007/978-3-030-52406-7_9
[13] Azadi, M., Moghaddas, Z., Farzipoor Saen, R., and Hussain, F. K., Financing manufacturers for investing in Industry 4.0 technologies: internal financing vs. External financing, Int. J. Prod. Res. 2021.
 Doi: 10.1080/00207543.2021.1912431
[14] Banker, R. D., Chang, H., Janakiraman, S. N., and Konstans, C., A balanced scorecard analysis of performance metrics, Eur. J. Oper. Res., 2004, 154 (2), P. 423–436. Doi: 10.1016/S0377-2217(03)00179-6
[15] Banker, R. D., Charnes, A., and Cooper, W. W., Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis, Manage. Sci., 1984, 30 (9), P. 1078–1092.
Doi: 10.1287/mnsc.30.9.1078
[16] Bannick, R. R., and Ozcan, Y. A., Efficiency analysis of federally funded hospitals: Comparison of DoD and VA hospitals using data envelopment analysis, Heal. Serv. Manag. Res., 1995, 8, P. 73–85.
Doi: 10.1177/095148489500800201
[17] Barrar, P., Wood, D., Jones, J., and Vedovato, M., The efficiency of accounting service provision, Bus. Process Manag. J., 2002, 8 (3), P. 195–217. Doi: 10.1108/14637150210428925
[18] Basso, A., and Funari, S., A data envelopment analysis approach to measure the mutual fund performance, Eur. J. Oper. Res., 2001, 135 (3), P. 477–492. Doi: 10.1016/S0377-2217(00)00311-8
[19] Basso, A., and Funari, S., Measuring the performance of ethical mutual funds: A DEA approach, J. Oper. Res. Soc., 2003, 54, P. 521–531. Doi: 10.2139/ssrn.300521
[20] Basso, A., and Funari, S., Socially responsible mutual funds: An efficiency comparison among the European countries, in Math. Stat. Methods Actuar. Sci. Financ., 2014, P. 69–79. Doi: 10.1007/978-3-319-02499-8_6
[21] Basso, A., and Funari, S., Constant and variable returns to scale DEA models for socially responsible investment funds, Eur. J. Oper. Res., 2014, 235 (3), P. 775–783. Doi: 10.1016/j.ejor.2013.11.024
[22] Berger, M., Sommersguter-Reichmann, M., and Czypionka, T., Determinants of soft budget constraints: How public debt affects hospital performance in Austria, Soc. Sci. Med., 2020, 249, 112855.
Doi: 10.1016/j.socscimed.2020.112855.
[23] Bhaduri, S. N., Durai, S. R. S., and Fogarty, D., Optimizing the media mix-evaluating the impact of advertisement expenditures of different media, in Adv. Bus. Anal. Essentials Dev. a Compet. Advant., 2016, P. 47–56, Doi: 10.1007/978-981-10-0727-9_4.
[24] Boďa, M., and Zimková, E., A DEA model for measuring financial intermediation, Econ. Chang. Restruct., 2021, 54, P. 339–370, Doi: 10.1007/s10644-020-09281-w.
[25] Bougnol, M.-L., Dulá, J. H., Estellita Lins, M. P., and Moreira da Silva, A. C., Enhancing standard performance practices with DEA, Omega, 2010, 38 (1-2), P. 33–45, Doi: 10.1016/
[26] Bowlin, W. F., Financial analysis of civil reserve air fleet participants using data envelopment analysis, Eur. J. Oper. Res., 2004, 154 (3), P. 691–709, Doi: 10.1016/S0377-2217(02)00814-7.
[27] Bozec, R., Dia, M., and Bozec, Y., Governance-performance relationship: A re-examination using technical efficiency measures, Br. J. Manag., 2010, 21 (3), P. 684–700, Doi: 10.1111/j.1467-8551.2008.00624.x.
[28] Cabrera Monroy, F., García Valderrama, T., and Sánchez Ortiz, J., Prioritization of the portfolio of R & D projects by means of Data Envelopment Analysis , Rev. Esp. Financ. y Contab., 2017, 46 (3), P. 369–407.
 Doi: 10.1007/978-1-4615-1001-7_4.
[29] Camanho, A. S., and Dyson, R. G., Efficiency, size, benchmarks and targets for bank branches: An application of data envelopment analysis, J. Oper. Res. Soc., 1999, 50 (9), P. 903–915.
 Doi: 10.1057/palgrave.jors.2600792.
[30] Camanho, A. S., and Dyson, R. G., Cost efficiency measurement with price uncertainty: A DEA application to bank branch assessments, Eur. J. Oper. Res., 2005, 161 (2), P. 432–446, Doi: 10.1016/j.ejor.2003.07.018.
[31] Capobianco, H. M. P., and Fernandes, E., Capital structure in the world airline industry, Transp. Res. Part A Policy Pract., 2004, 38 (6), P. 421–434, Doi: 10.1016/j.tra.2004.03.002.
[32] Chandra, B., and Gupta, M., Novel multivariate time series clustering approach for e-governance of crime data, in Proc. - 2013 6th Int. Conf. Dev. ESystems Eng. DeSE, 2013, P. 311–316, Doi: 10.1109/DeSE.2013.62.
[33] Chang, T.-S., Tone, K., and Wu, C.-H., Nested dynamic network data envelopment analysis models with infinitely many decision making units for portfolio evaluation, Eur. J. Oper. Res., 2021, 291 (2), P. 766–781.
 Doi: 10.1016/j.ejor.2020.09.044.
[34] Charnes, A., Cooper, W. W., and Rhodes, E., Measuring the efficiency of decision making units, Eur. J. Oper. Res., 1978, 2 (6), P. 429–444, Doi: 10.1016/0377-2217(78)90138-8.
[35] Izadikhah, M., Farzipoor Saen, R., Ranking sustainable suppliers by context-dependent data envelopment analysis. Ann Oper Res, 2020, 293, P. 607–637, Doi: 10.1007/s10479-019-03370-4
[36] Chen, C., Maximizing Efficiency in State Infrastructure Finance: The Role of Competition, Citizen Monitoring Capacity, and Institutions, Am. Rev. Public Adm., 2018, 48 (2), P. 915–928.
Doi: 10.1177/0275074017746755.
[37] Chen, T.-Y., A comparison of chance-constrained dea and stochastic frontier analysis: Bank efficiency in taiwan, J. Oper. Res. Soc., 2002, 53 (5), P. 492–500.
[38] Cheng, E. W. L., Chiang, Y. H., and Tang, B. S., Alternative approach to credit scoring by DEA: Evaluating borrowers with respect to PFI projects, Build. Environ., 2007, 42 (4), P. 1752–1760.
Doi: 10.1016/j.buildenv.2006.02.012.
[39] Chiang, T.-C., Cheng, P.-Y., and Leu, F.-Y., Prediction of technical efficiency and financial crisis of Taiwan’s information and communication technology industry with decision tree and DEA, Soft Comput., 2017, 21, P. 5341–5353, Doi: 10.1007/s00500-016-2117-y.
[40] Chien, F., Chau, K. Y., Ady, S. U., Zhang, Y. Q., Tran, Q. H., and Aldeehani, T. M., Does the combining effects of energy and consideration of financial development lead to environmental burden: social perspective of energy finance?, Environ. Sci. Pollut. Res., 2021, 28, P. 40957–40970. Doi: 10.1007/s11356-021-13423-6
[41] Cho, T.-Y., and Chen, Y.-S., The impact of financial technology on China’s banking industry: An application of the metafrontier cost Malmquist productivity index, North Am. J. Econ. Financ., 2021, 57.
 Doi: 10.1016/j.najef.2021.101414
[42] Curi, C., and Lozano-Vivas, A., Productivity of foreign banks: Evidence from a financial center, in Effic. Product. Growth Model. Financ. Serv. Ind., 2013, P. 95–121. Doi: 10.1002/9781118541531.ch5
[43] Davidova, S., and Latruffe, L., Relationships between technical efficiency and financial management for Czech Republic Farms, J. Agric. Econ., 2007, 58 (2), P. 269–288. Doi: 10.1111/j.1477-9552.2007.00109.x
[44] Davutyan, N., and Kavut, L., An application of data envelopment analysis to the evaluation of audit risk: A reinterpretation, Abacus, 2005, 41 (3), P. 290–306. Doi: 10.1111/j.1467-6281.2005.00183.x
[45] Deng, Y., Zou, S., and You, D., Financial performance evaluation of nuclear power-related enterprises from the perspective of sustainability, Environ. Sci. Pollut. Res., 2020, 27 (1), P. 11349–11363. Doi: 10.1007/s11356-019-07545-1
[46] Desta, T. S., Are the best African banks really the best? A Malmquist data envelopment analysis, Meditari Account. Res., 2016, 24 (4), P. 588–610. Doi: 10.1108/MEDAR-02-2016-0016
[47] Dibachi, H., Behzadi, M. H., and Izadikhah, M., Stochastic multiplicative DEA model for measuring the efficiency and ranking of DMUs under VRS technology, Indian J. Sci. Technol., 2014, 7(11), P. 1765–1773.
 Doi: 10.17485/ijst/2014/v7i11.19
[48] Dluhošová, D., and Zmeškal, Z., Companies financial performance determanitation applying the data envelopment analysis (DEA) method, in Met. 2013 - 22nd Int. Conf. Metall. Mater. Conf. Proc., 2013, P. 1860–1866.
[49] Dong, X., and Ma, J., Study on the indicator system for evaluating economic development efficiency in Shandong Province, in Energy Procedia, 2011, P. 900–904. Doi: 10.1016/j.egypro.2011.03.159
[50] Durana, P., Zauskova, A., Vagner, L., and Zadnanova, S., Earnings drivers of slovak manufacturers: Efficiency assessment of innovation management, Appl. Sci., 2020, 10, Doi: 10.3390/app10124251
[51] Durand, R., and Vargas, V., Ownership, organization, and private firms’ efficient use of resources, Strateg. Manag. J., 2003, 24, P. 667–675. Doi: 10.1002/smj.321
[52] Dutta, P., Jain, A., and Gupta, A., Performance analysis of non-banking finance companies using two-stage data envelopment analysis, Ann. Oper. Res., 2020, 295, P. 91–116. Doi: 10.1007/s10479-020-03705-6
[53] Elyasiani, E., and Wang, Y., Bank holding company diversification and production efficiency, Appl. Financ. Econ., 2012, 22, P. 1409–1428. Doi: 10.1080/09603107.2012.657351
[54] Evangelinos, C., Wieland, B., and Kuhnhausen, T., Baumol’s cost disease in the local transit sector: A comparative analysis for germany and the USA, Int. J. Transp. Econ., 2012, 39 (1), P. 83–104.
[55] Fernandes, E., Pires, H. M., Lins, M. P. E., and Silva, A. C. M., Financial performance of air transport companies: An analysis of the non-Pareto-efficient space in data envelopment analysis, in WIT Trans. Inf. Commun. Technol., 2008, P. 185–194. Doi: 10.2495/DATA080181
[56] Feroz, E. H., Kim, S., and Raab, R., Performance Measurement in Corporate Governance: Do Mergers Improve Managerial Performance in the Post-Merger Period?, Rev. Account. Financ., 2005, 4, P. 86–100.
Doi: 10.1108/eb043432
[57] Ferraz, D., Mariano, E. B., Rebelatto, D., and Hartmann, D., Linking Human Development and the Financial Responsibility of Regions: Combined Index Proposals Using Methods from Data Envelopment Analysis, Soc. Indic. Res., 2020, 150, P. 439–478. Doi: 10.1007/s11205-020-02338-3
[58] Fijałkowska, J., Zyznarska-Dworczak, B., and Garsztka, P., Corporate social-environmental performance versus financial performance of banks in Central and Eastern European Countries, Sustain., 2018, 10 (3).
 Doi: 10.3390/su10030772
[59] Fiordelisi, F., and Molyneux, P., Efficiency in the factoring industry, Appl. Econ., 2004, 36 (9), P. 947–959. Doi: 10.1080/00036884042000233177
[60] Franco Miguel, J. L., and Fullana Belda, C., New hospital management models as an alternative for the sustainability of public hospital system: An analysis of efficiency in health expenditure, J. Healthc. Qual. Res., 2019, 34 (3),  P. 131–147. Doi: 10.1016/j.jhqr.2019.01.009
[61] Franco Miguel, J. L., Fullana Belda, C., and Rúa Vieites, A., Analysis of the technical efficiency of the forms of hospital management based on public-private collaboration of the Madrid Health Service, as compared with traditional management, Int. J. Health Plann. Manage., 2019, 34 (10), P. 414–442, Doi: 10.1002/hpm.2678.
[62] Friis Pedersen, M., and Vesterlund Olsen, J., Measuring credit capacity on Danish farms using DEA, Agric. Financ. Rev., 2013, 73 (2013), P. 393–412, Doi: 10.1108/AFR-08-2012-0040.
[63] Gardijan Kedžo, M., and Lukač, Z., The financial efficiency of small food and drink producers across selected European Union countries using data envelopment analysis, Eur. J. Oper. Res., 2021, 291 (2), P. 586–600.
 Doi: 10.1016/j.ejor.2020.01.066.
[64] Gatimbu, K. K., Ogada, M. J., and Budambula, N. L. M., Environmental efficiency of small-scale tea processors in Kenya: an inverse data envelopment analysis (DEA) approach, Environ. Dev. Sustain., 2020, 22 (5), P. 3333–3345, Doi: 10.1007/s10668-019-00348-x.
[65] Gebremichael, B. Z., and Gessesse, H. T., Technical efficiency of Microfinance Institutions (MFIs): Does ownership matter? Evidence from African MFIs, Int. J. Dev. Issues, 2016, 15 (3), P. 224–239, Doi: 10.1108/IJDI-04-2016-0026.
[66] Ghani, J. A., Grewal, B., Ahmed, A. D., and Noor, N. M., Efficiency analysis of state governments in the Malaysian fiscal federalism, Int. J. Econ. Manag., 2017, 11 (2), P. 449–466.
[67] Goto, M., Financial performance analysis of US and world telecommunications companies: Importance of Information Technology in the telecommunications industry after the AT&T breakup and the NTT divestiture, Decis. Support Syst., 2010, 48 (3), P. 447–456, Doi: 10.1016/j.dss.2009.06.003.
[68] Gregoriou, G., Messier, J., and Sedzro, K., Assessing the relative efficiency of credit unionbranches using data envelopment analysis, INFOR, 2004, 42 (4), P. 281–297, Doi: 10.1080/03155986.2004.11732709.
[69] Gregoriou, G. N., Pascalau, R., and Chen, Y., Congestion in commodity trading advisors, INFOR, 2011,  49 (1), P. 63–74, Doi: 10.3138/infor.49.1.063.
[70] Gregoriou, G. N., and Ramiah, V., Efficiency of U.S. State EPA Emission Rate Goals for 2030: A Data Envelopment Analysis Approach, in Handb. Environ. Sustain. Financ., 2016, P. 55–64, Doi: 10.1016/B978-0-12-803615-0.00003-0.
[71] Grosskopf, S., and Moutray, C., Evaluating performance in Chicago public high schools in the wake of decentralization, Econ. Educ. Rev., 2001, 20 (1), P. 1–14, Doi: 10.1016/S0272-7757(99)00065-5.
[72] Gu, W., Basu, M., Chao, Z., and Wei, L., A unified framework for credit evaluation for internet finance companies: Multi-criteria analysis through AHP and DEA, Int. J. Inf. Technol. Decis. Mak., 2017, 16 (3), P. 597–624, Doi: 10.1142/S0219622017500134.
[73] Guan, J., and Chen, K., Modeling macro-R&D production frontier performance: An application to Chinese province-level R&D, Scientometrics, 2010, 82, P. 165–173, Doi: 10.1007/s11192-009-0030-1.
[74] Gutiérrez-Nieto, B., Serrano-Cinca, C., and Mar Molinero, C., Social efficiency in microfinance institutions, J. Oper. Res. Soc., 2009, 60 (1), P. 104–119, Doi: 10.1057/palgrave.jors.2602527.
[75] Halkos, G. E., and Tzeremes, N. G., Analyzing the Greek renewable energy sector: A Data Envelopment Analysis approach, Renew. Sustain. Energy Rev., 2012, 16 (5), P. 2884–2893, Doi: 10.1016/j.rser.2012.02.003.
[76] Halkos, G. E., and Tzeremes, N. G., Industry performance evaluation with the use of financial ratios: An application of bootstrapped DEA, Expert Syst. Appl., 2012, 39 (5), P. 5872–5880.
 Doi: 10.1016/j.eswa.2011.11.080.
[77] Halkos, G., and Tsionas, M. G., Accounting for Heterogeneity in Environmental Performance Using Data Envelopment Analysis, Comput. Econ., 2019, 54, P. 1005–1025, Doi: 10.1007/s10614-018-9861-2.
[78] Haq, M., Skully, M., and Pathan, S., Efficiency of microfinance institutions: A data envelopment analysis, Asia-Pacific Financ. Mark., 2010, 17 (1), P. 63–97, Doi: 10.1007/s10690-009-9103-7.
[79] Hassan, M., How bank regulations impact efficiency and performance?, J. Financ. Econ. Policy, 2020, 12 (4), P. 545–575, Doi: 10.1108/JFEP-06-2019-0119.
[80] Haug, A. A., and Blackburn, V. C., Government secondary school finances in New South Wales: accounting for students’ prior achievements in a two-stage DEA at the school level, J. Product. Anal., 2017, 48 (1), P. 69–83, Doi: 10.1007/s11123-017-0502-x.
[81] Havlíček, J., Dömeová, L., Smutka, L., Řezbová, H., Severová, L., Šubrt, T., et al., Efficiency of pig production in the Czech Republic and in an international context, Agric., 2020, 10 (12), P. 1–18.
 Doi: 10.3390/agriculture10120597.
[82] He, P.-L., Yu, Z.-F., and Tao, J., The application of DEA model in predicting corporate financial risks, in Proc. - Int. Conf. Manag. Serv. Sci. MASS, 2009, Doi: 10.1109/ICMSS.2009.5301238.
[83] Henriques, I. C., Sobreiro, V. A., Kimura, H., and Mariano, E. B., Two-stage DEA in banks: Terminological controversies and future directions, Expert Syst. Appl., 2020, 161, 113632, Doi: 10.1016/j.eswa.2020.113632.
[84] Hoe, L. W., Siew, L. W., and Fai, L. K., Improvement on the efficiency of technology companies in Malaysia with data envelopment analysis model, Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 2017, 10645 LNCS (2017), P. 19–30, Doi: 10.1007/978-3-319-70010-6_2.
[85] Hosseinian, A. H., and Baradaran, V., P-GWO and MOFA: two new algorithms for the MSRCPSP with the deterioration effect and financial constraints (case study of a gas treating company), Appl. Intell., 2020, 50, P. 2151–2176, Doi: 10.1007/s10489-020-01663-x.
[86] Houshyar, E., Chen, B., and Chen, G. Q., Environmental impacts of rice production analyzed via social capital development: An Iranian case study with a life cycle assessment/data envelopment analysis approach, Ecol. Indic., 2019, 105, P. 675–687, Doi: 10.1016/j.ecolind.2018.07.040.
[87] Hwang, S.-N., An application of data envelopment analysis to measure the managerial performance of electronics industry in Taiwan, Int. J. Technol. Manag., 2007, 40 (1-2-3), P. 215–228.
 Doi: 10.1504/IJTM.2007.013535.
[88] Inoue, K., Ichinotsubo, T., and Aoki, S., DEA based hierarchical structure evaluation and visualization method, in IEEE Int. Conf. Fuzzy Syst., 2011, P. 1701–1704, Doi: 10.1109/FUZZY.2011.6007530.
[89] Izadikhah, M., Financial Assessment of Banks and Financial Institutes in Stock Exchange by Means of an Enhanced Two stage DEA Model, Adv. Math. Financ. Appl., 2021, 6, P. 207–232,
Doi: 10.1109/FUZZY.2011.6007530.
[90] Izadikhah, M., and Farzipoor Saen, R., Assessing sustainability of supply chains by chance-constrained two-stage DEA model in the presence of undesirable factors, Comput. Oper. Res., 2018, 100, P. 343–367,
Doi: 10.1016/j.cor.2017.10.002.
[91] Izadikhah, M., and Farzipoor Saen, R., Solving voting system by data envelopment analysis for assessing sustainability of suppliers, Gr. Decis. Negot., 2019, 28 (3), P. 641–669, Doi: 10.1007/s10726-019-09616-7.
[92] Jahangoshai Rezaee, M., and Shokry, M., Game theory versus multi-objective model for evaluating multi-level structure by using data envelopment analysis, Int. J. Manag. Sci. Eng. Manag., 2017, 12 (4), P. 245–255, Doi: 10.1080/17509653.2016.1249425.
[93] Jiang, Y., Li, M., and Xia, P., Bank supply chain efficiency analysis based on regional heterogeneity: a data-driven empirical study, Ind. Manag. Data Syst., 2021, 121 (4), P. 940–963, Doi: 10.1108/IMDS-10-2019-0541.
[94] Joro, T., and Na, P., Portfolio performance evaluation in a mean-variance-skewness framework, Eur. J. Oper. Res., 2006, 175 (1), P. 446–461, Doi: 10.1016/j.ejor.2005.05.006.
[95] Kaffash, S., Azizi, R., Huang, Y., and Zhu, J., A survey of data envelopment analysis applications in the insurance industry 1993–2018, Eur. J. Oper. Res., 2020, 284 (3), P. 801–813, Doi: 10.1016/j.ejor.2019.07.034.
[96] Kao, C., Network data envelopment analysis: A review, Eur. J. Oper. Res., 2014, 239 (1), P. 1–16.
 Doi: 10.1016/j.ejor.2014.02.039.
[97] Kevork, I. S., Pange, J., Tzeremes, P., and Tzeremes, N. G., Estimating Malmquist productivity indexes using probabilistic directional distances: An application to the European banking sector, Eur. J. Oper. Res., 2017, 261 (3), P. 1125–1140, Doi: 10.1016/j.ejor.2017.03.012.
[98] Khanam, D., Parvin, S. S., Mohiuddin, M., Hoque, A., and Su, Z., Financial sustainability of non-governmental microfinance institutions (MFIs): A cost-efficiency analysis of Brac, Asa, and proshika from Bangladesh, Rev. Econ. Financ., 2018, 12, P.  43–56.
[99] Khoshroo, A., and Izadikhah, M., Improving efficiency of farming products through benchmarking and data envelopment analysis, Int. J. Manag. Decis. Mak., 2019, 18 (1), Doi: 10.1504/IJMDM.2019.096691.
[100] Khoshroo, A., Izadikhah, M., and Emrouznejad, A., Improving energy efficiency considering reduction of CO2 emission of turnip production: A novel data envelopment analysis model with undesirable output approach, J. Clean. Prod., 2018, 187, P. 605-615. Doi: 10.1016/j.jclepro.2018.03.232
[101] Kim, J. M., Sustainable farm policy in the Republic of Korea with special reference to rice, vegetable and apple production, Acta Hortic., 2000, 536, P. 81–92. Doi: 10.17660/ActaHortic.2000.536.8
[102] Kotsiopoulos, I., and Cassaigne, N., A project resource management method for a single project with multiple participating organisations, in Proc. IEEE Int. Conf. Syst. Man Cybern., 2002, P. 87–92.
 Doi: 10.1109/ICSMC.2002.1175565
[103] Kresta, A., and Tichý, T., Selection of efficient market risk models: Backtesting results evaluation with DEA approach, Comput. Ind. Eng., 2016, 102, P. 331–339. Doi: 10.1016/j.cie.2016.07.017
[104] Labijak-Kowalska, A., and Kadziński, M., Experimental comparison of results provided by ranking methods in Data Envelopment Analysis, Expert Syst. Appl., 2021, 173 (1). Doi: 10.1016/j.eswa.2021.114739
[105] Lee, C.-F., Wang, K., and Peng, Y.-H., Cost structure and efficiency of the credit departments of the farmers’ associations in Taiwan, Rev. Pacific Basin Financ. Mark. Policies, 2006, 9 (3) P. 385–403.
 Doi: 10.1142/S021909150600077X
[106] Lee, H.-S., Efficiency decomposition of the network DEA in variable returns to scale: An additive dissection in losses, Omega, 2021, 100. Doi: 10.1016/
[107] Lewis, H. F., and Sexton, T. R., Network DEA: efficiency analysis of organizations with complex internal structure, Comput. Oper. Res., 2004, 31 (9), P. 1365–1410. Doi: 10.1016/S0305-0548(03)00095-9
[108] Li, Y., Chiu, Y.-H., Lin, T.-Y., and Huang, Y. Y., Market share and performance in Taiwanese banks: min/max SBM DEA, TOP, 2019, 27, P. 233–252. Doi: 10.1007/s11750-019-00504-6
[109] Li, Z., Rural finance, farmland transfer and agricultural production technical efficiency: Evidence from China, in Proc. - 2010 2nd IEEE Int. Conf. Inf. Financ. Eng. ICIFE 2010, 2010, P. 364–368.
Doi: 10.1109/ICIFE.2010.5609377
[110] Li, Z., Analysis of the validity of Wuhan sustainable development based on DEA, in Proc. - 2010 Int. Forum Inf. Technol. Appl. IFITA 2010, 2010, P. 241–244. Doi: 10.1109/IFITA.2010.100
[111] Li, Z., Crook, J., and Andreeva, G., Dynamic prediction of financial distress using Malmquist DEA, Expert Syst. Appl., 2017, 80, P. 94–106. Doi: 10.1016/j.eswa.2017.03.017
[112] Liang, C.-J., Yao, M.-J., Hwang, D.-Y., and Wu, W.-H., The impact of non-performing loans on bank’s operating efficiency for Taiwan banking industry, Rev. Pacific Basin Financ. Mark. Policies, 2008, 11 (2), P. 287–304, Doi: 10.1142/S0219091508001350.
[113] Lim, S., Oh, K. W., and Zhu, J., Use of DEA cross-efficiency evaluation in portfolio selection: An application to Korean stock market, Eur. J. Oper. Res., 2014, 236 (1), P. 361–368.
 Doi: 10.1016/j.ejor.2013.12.002.
[114] Liu, B., and Tripe, D., New Zealand bank mergers and efficiency gains, J. Asia-Pacific Bus., 2003, 4 (3), P. 61–81, Doi: 10.1300/J098v04n04_05.
[115] Liu, C.-C., A DEA study to evaluate the relative efficiency and investigate the reorganization of the credit department of farmers’ associations in Taiwan, Appl. Econ., 2007, 39 (20), P. 2663–2671.
 Doi: 10.1080/00036840600722273.
[116] Liu, S.-T., Slacks-based efficiency measures for predicting bank performance, Expert Syst. Appl., 2009, 36 (2), P. 2813–2818, Doi: 10.1016/j.eswa.2008.01.032.
[117]      Liu, X., Yu, X., and Gao, S., A quantitative study of financing efficiency of low-carbon companies: A three-stage data envelopment analysis, Bus. Strateg. Environ., 2019, 28, P. 858–871, Doi: 10.1002/bse.2288.
[118] Lu, W.-M., and Chen, M.-H., A benchmark-learning roadmap for the Military Finance Center, Math. Comput. Model., 2011, 53 (9-10), P. 1833–1843, Doi: 10.1016/j.mcm.2011.01.003.
[119] Lv, C., Shao, C., and Lee, C.-C., Green technology innovation and financial development: Do environmental regulation and innovation output matter?, Energy Econ., 2021, 98, 105237.
 Doi: 10.1016/j.eneco.2021.105237.
[120] Lynch, J. R., and Ozcan, Y. A., Hospital closure: An efficiency analysis, Hosp. Heal. Serv. Adm., 1994, 39 (2), P. 205–220.
[121] Lyu, X., and Shi, A., Research on the renewable energy industry financing efficiency assessment and mode selection, Sustain., 2018, 10 (1), 222, Doi: 10.3390/su10010222.
[122] Ma, H., and Zhang, R., The study of financial efficiency of new material industry in China based on DEA method, in Proc. IEEE Int. Conf. Grey Syst. Intell. Serv. GSIS, 2015, P. 628–634.
 Doi: 10.1109/GSIS.2015.7301818.
[123] Malhotra, D. K., Poteau, R., and Fritz, J. J., Benchmarking thrift and mortgage finance companies, Int. J. Data Anal. Tech. Strateg., 2015, 7 (1), P. 21–39, Doi: 10.1504/IJDATS.2015.067699.
[124] Manandhar, R., and Tang, J. C. S., An empirical study on the evaluation of bank branch performance using data envelopment analysis, Int. J. Serv. Technol. Manag., 2004, 5 (2), P. 111–139.
 Doi: 10.1504/IJSTM.2004.004054.
[125] Merkert, R., and Morrell, P. S., Mergers and acquisitions in aviation - Management and economic perspectives on the size of airlines, Transp. Res. Part E Logist. Transp. Rev., 2012, 48 (4), P. 853–862.
Doi: 10.1016/j.tre.2012.02.002.
[126] Min, D., Wang, F., and Zhan, S., Impact analysis of the global financial crisis on global container fleet, in Proc. 2009 6th Int. Conf. Serv. Syst. Serv. Manag. ICSSSM ’09, 2009, P. 161–166.
 Doi: 10.1109/ICSSSM.2009.5174875.
[127] Min, H., Min, H., Joo, S. J., and Kim, J., Evaluating the financial performances of Korean luxury hotels using data envelopment analysis, Serv. Ind. J., 2009, 29 (6), P. 835–845, Doi: 10.1080/02642060902749393.
[128] Mirhedayatian, S. M., Azadi, M., and Farzipoor Saen, R., A novel network data envelopment analysis model for evaluating green supply chain management, Int. J. Prod. Econ., 2014, 147 (B), P. 544–554.
 Doi: 10.1016/j.ijpe.2013.02.009.
[129] Młynarski, W., Prędki, A., and Kaliszewski, A., Efficiency and factors influencing it in forest districts in southern Poland: Application of Data Envelopment Analysis, For. Policy Econ., 2021, 130.
 Doi: 10.1016/j.forpol.2021.102530.
[130] Mohapatra, S., Jena, S. K., Mitra, A., and Tiwari, A. K., Intellectual capital and firm performance: evidence from Indian banking sector, Appl. Econ., 2019, 51 (1), P. 6054–6067, Doi: 10.1080/00036846.2019.1645283.
[131] Moheb-Alizadeh, H., and Handfield, R., Sustainable supplier selection and order allocation: A novel multi-objective programming model with a hybrid solution approach, Comput. Ind. Eng., 2019, 129, P. 192–209.
 Doi: 10.1016/j.cie.2019.01.011.
[132] Mohsin, M., Taghizadeh-Hesary, F., Panthamit, N., Anwar, S., Abbas, Q., and Vo, X. V, Developing Low Carbon Finance Index: Evidence From Developed and Developing Economies, Financ. Res. Lett.,  2020, 43, 101520. Doi: 10.1016/
[133] Mohtashami, A., and Ghiasvand, B. M., Z-ERM DEA integrated approach for evaluation of banks & financial institutes in stock exchange, Expert Syst. Appl., 2020, 147, 113218. Doi: 10.1016/j.eswa.2020.113218.
[134] Moreno, P., and Lozano, S., Super SBI Dynamic Network DEA approach to measuring efficiency in the provision of public services, Int. Trans. Oper. Res., 2018, 25 (2), P. 715–735. Doi: 10.1111/itor.12257.
[135] Mugambi, P., Blanco, M., Ogachi, D., Ferasso, M., and Bares, L., Analysis of the regional efficiency of european funds in Spain from the perspective of renewable energy production: The regional dimension, Int. J. Environ. Res. Public Health, 2021, 18 (9). Doi: 10.3390/ijerph18094553.
[136] Murthi, B. P. S., Choi, Y. K., and Desai, P., Efficiency of mutual funds and portfolio performance measurement: A non-parametric approach, Eur. J. Oper. Res., 1997, 98 (2), P. 408–418.
 Doi: 10.1016/S0377-2217(96)00356-6.
[137] Nijkamp, P., and Suzuki, S., A generalized Goals-achievement model in data envelopment analysis: An application to efficiency improvement in local government finance in Japan, Spat. Econ. Anal., 2009, 4 (3), P. 249–274. Doi: 10.1080/17421770903114687.
[138] Ouellette, P., and Vierstraete, V., An evaluation of the efficiency of Québec’s school boards using the Data Envelopment Analysis method, Appl. Econ., 2005, 37 (14), P. 1643–1653. Doi: 10.1080/00036840500173247.
[139] Ozcan, Y. A., and McCue, M. J., Development of a financial performance index for hospitals: Dea approach, J. Oper. Res. Soc., 1996, 47 (1), P. 18–26. Doi: 10.1057/jors.1996.2.
[140] Pacheco, R. R., and Fernandes, E., Managerial efficiency of Brazilian airports, Transp. Res. Part A Policy Pract., 2003, 37 (8), P. 667–680. Doi: 10.1016/S0965-8564(03)00013-2.
[141] Panigrahi, S. S., Mantri, J. K., and Gahan, P., A DEA-based evolutionary computation model for stock market forecasting, Lect. Notes Electr. Eng., 2018, 453, P. 139–148. Doi: 10.1007/978-981-10-5565-2_12.
[142] Pantouvakis, A., and Dimas, A., Does ISO 9000 series certification matter for the financial performance of ports? Some preliminary findings from Europe, Marit. Policy Manag., 2010, 37 (5), P. 505–522.
 Doi: 10.1080/03088839.2010.503714.
[143] Petridis, K., Petridis, N. E., Emrouznejad, A., and Ben Abdelaziz, F., Prioritizing of volatility models: a computational analysis using data envelopment analysis, Int. Trans. Oper. Res., 2021. Doi: 10.1111/itor.13028.
[144] Piot-Lepetit, I., and Tchakoute Tchuigoua, H., Ownership and performance of microfinance institutions in Latin America: A pseudo-panel malmquist index approach, J. Oper. Res. Soc., 2021.
 Doi: 10.1080/01605682.2021.1895683.
[145] Premachandra, I. M., Bhabra, G. S., and Sueyoshi, T., DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique, Eur. J. Oper. Res., 2009, 193 (2), P. 412–424.
 Doi: 10.1016/j.ejor.2007.11.036.
[146] Puig-Junoy, J., Partitioning input cost efficiency into its allocative and technical components: An empirical DEA application to hospitals, Socioecon. Plann. Sci., 2000, 34 (3), P. 199–218.
 Doi: 10.1016/S0038-0121(99)00024-5.
[147] Rahman, M., Lambkin, M., and Hussain, D., Value creation and appropriation following M&A: A data envelopment analysis, J. Bus. Res., 2016, 69 (12), P. 5628–5635. Doi: 10.1016/j.jbusres.2016.03.070.
[148] Ralston, D., Wright, A., and Garden, K., Can mergers ensure the survival of credit unions in the third millennium?, J. Bank. Financ., 2001, 25 (12), P. 2277–2304. Doi: 10.1016/S0378-4266(01)00193-5.
[149] Ramón, N., Ruiz, J. L., and Sirvent, I., Cross-benchmarking for performance evaluation: Looking across best practices of different peer groups using DEA, Omega, 2020, 92, 102169. Doi: 10.1016/
[150] Ray, S. C., and Mukherjee, K., A study of size efficiency in US banking: Identifying banks that are too large, Int. J. Syst. Sci., 1998, 29 (11), P. 1281–1294. Doi: 10.1080/00207729808929615
[151] Reiner, G., and Hofmann, P., Efficiency analysis of supply chain processes, Int. J. Prod. Res., 2006, 44 (23), P. 5065–5087. Doi: 10.1080/00207540500515123
[152] Resti, A., Efficiency measurement for multi-product industries: a comparison of classic and recent techniques based on simulated data, Eur. J. Oper. Res., 2000, 121 (3), P. 559–578.
Doi: 10.1016/S0377-2217(99)00054-5
[153] Rolle, A., Out with the old—in with the new: Thoughts on the future of educational productivity research, Peabody J. Educ., 2004, 79 (3), P. 31–56. Doi: 10.1207/s15327930pje7903_3
[154] Rosman, R., Wahab, N. A., and Zainol, Z., Efficiency of Islamic banks during the financial crisis: An analysis of Middle Eastern and Asian countries, Pacific Basin Financ. J. 28 (2014) 76–90.
[155] Ryan, A., Barchers, C., Christofa, E., and Knodler, M., Equitable resource allocation for municipal safety: A data envelopment analysis, Transp. Res. Part D Transp. Environ., 2021, 97. Doi: 10.1016/j.trd.2021.102926
[156] Saranga, H., The Indian auto component industry – Estimation of operational efficiency and its determinants using DEA, Eur. J. Oper. Res., 2009, 196 (2), P. 707–718. Doi: 10.1016/j.ejor.2008.03.045
[157] Sayar, T., Ghiyasi, M., and Fathali, J., New inverse DEA models for budgeting and planning, RAIRO - Oper. Res., 2021, 55 (3), P. 1933–1948. Doi: 10.1051/ro/2021069
[158] Scheraga, C. A., Operational efficiency versus financial mobility in the global airline industry: A data envelopment and Tobit analysis, Transp. Res. Part A Policy Pract., 2004, 38 (5), P. 383–404.
 Doi: 10.1016/j.tra.2003.12.003
[159] Serrano-Cinca, C., Fuertes-Callén, Y., and Mar-Molinero, C., Measuring DEA efficiency in Internet companies, Decis. Support Syst., 2005, 38 (4), P. 557–573. Doi: 10.1016/j.dss.2003.08.004
[160] Sexton, T. R., Comunale, C., Higuera, M. S., and Stickle, K., Performance benchmarking of school districts in New York state, Int. Ser. Oper. Res. Manag. Sci., 2016, 238, P. 439–462. Doi: 10.1007/978-1-4899-7684-0_13
[161] Shan, S., and Sun, Y., Allocation of Resources in Different Types of PPP Projects based on DEA-Game Model in A Competition Environment, in ACM Int. Conf. Proceeding Ser., 2020, P. 51–53.
[162] Shawtari, F. A., Abdelnabi Salem, M., and Bakhit, I., Decomposition of efficiency using DEA window analysis: A comparative evidence from Islamic and conventional banks, Benchmarking, 2018, 25 (6), P. 1681–1705, Doi: 10.1108/BIJ-12-2016-0183.
[163] Shi, D., Efficiency measurement of financial innovation system based on data envelopment analysis, in 2008 Int. Conf. Wirel. Commun. Netw. Mob. Comput. WiCOM, 2008, Doi: 10.1109/WiCom.2008.2258.
[164] Song, M., Xie, Q., and Shen, Z., Impact of green credit on high-efficiency utilization of energy in China considering environmental constraints, Energy Policy, 2021, 153 (22), Doi: 10.1016/j.enpol.2021.112267.
[165] Sueyoshi, T., and Goto, M., Can R&D expenditure avoid corporate bankruptcy? Comparison between Japanese machinery and electric equipment industries using DEA-discriminant analysis, Eur. J. Oper. Res., 2009, 196 (1), P. 289–311, Doi: 10.1016/j.ejor.2008.02.021.
[166] Sun, C., and Galagedera, D. U. A., Do superannuation funds manage disbursements and risk efficiently in generating returns? New evidence, Appl. Econ., 2021, 53 (34), P. 3931–3947.
 Doi: 10.1080/00036846.2021.1888863.
[167] Tavana, M., Izadikhah, M., Toloo, M., and Roostaee, R., A new non-radial directional distance model for data envelopment analysis problems with negative and flexible measures, Omega, 2021, 102 (2021),  102355, Doi: 10.1016/
[168] Terdpaopong, K., and Rickards, R. C., Thai non-life insurance companies’ resilience and the historic 2011 floods: Some recommendations for greater sustainability, Sustain., 2021, 13 (16), Doi: 10.3390/su13168890.
[169] Thompson, R. G., Dharmapala, P. S., Gatewood, E. J., Macy, S., and Thrall, R. M., DEA/assurance region sbdc efficiency and unique projections, Oper. Res., 1996, 44 (4), P. 533–542, Doi: 10.1287/opre.44.4.533.
[170] Toloo, M., The role of non-Archimedean epsilon in finding the most efficient unit: With an application of professional tennis players, Appl. Math. Model., 2014, 38 (21-22), P. 5334–5346.
 Doi: 10.1016/j.apm.2014.04.010.
[171] Toloo, M., and Ertay, T., The most cost efficient automotive vendor with price uncertainty: A new DEA approach, Measurement, 2014, 52 (1), P. 135–144, Doi: 10.1016/j.measurement.2014.03.002.
[172] Tone, K., Toloo, M., and Izadikhah, M., A modified slacks-based measure of efficiency in data envelopment analysis, Eur. J. Oper. Res., 2020, 287 (2), P. 560-571, Doi: 10.1016/j.ejor.2020.04.019.
[173] Tseng, Y., Kao, S., Lee, T., and Wu, C., A DEA/AHP approach to efficiency investigation for Taiwan’s retailing industry via financial data analysis, in Proc. - 8th Int. Conf. Intell. Syst. Des. Appl. ISDA, 2008, P. 235–239, Doi: 10.1109/ISDA.2008.106.
[174] Tsolas, I. E., Relative profitability and stock market performance of listed commercial banks on the Athens Exchange: A non-parametric approach, IMA J. Manag. Math., 2011, 22 (4), P. 323–342.
 Doi: 10.1093/imaman/dpq017.
[175] Tsolas, I. E., and Charles, V., Incorporating risk into bank efficiency: A satisficing DEA approach to assess the Greek banking crisis, Expert Syst. Appl., 2015, 42 (7), P. 3491–3500, Doi: 10.1016/j.eswa.2014.12.033.
[176] Tu, C.-J., Chen, W.-L., and Lin, T. T., Applying DEA on operating performance analysis: Comparison between urban and rural operating areas of a case telecom company, Rev. Pacific Basin Financ. Mark. Policies, 2014, 17 (2), Doi: 10.1142/S0219091514500118.
[177] Varabyova, Y., and Schreyögg, J., International comparisons of the technical efficiency of the hospital sector: Panel data analysis of OECD countries using parametric and non-parametric approaches, Health Policy (New. York), 2013, 112 (1-2), P. 70–79, Doi: 10.1016/j.healthpol.2013.03.003.
[178] Wang, H., Pan, C., Wang, Q., and Zhou, P., Assessing sustainability performance of global supply chains: An input-output modeling approach, Eur. J. Oper. Res., 2020, 285 (1), P. 393–404.
 Doi: 10.1016/j.ejor.2020.01.057.
[179] Wang, P., Shao, Z., Wang, J., and Wu, Q., The impact of land finance on urban land use efficiency: A panel threshold model for Chinese provinces, Growth Change, 2021, 52 (10), P. 310–331, Doi: 10.1111/grow.12464.
[180] Wang, P., Yang, Y., and Zhang, L., Study on dynamic efficiency of agricultural finance projects under the new countryside background-taking henan province as example, Commun. Comput. Inf. Sci., 2011, 208 CCIS, P. 70–76. Doi: 10.1007/978-3-642-23023-3_10
[181] Wang, X., and Han, L., Research on listed firms’ financial distress prewarning based on a longitudinal data envelopment analysis, in 2008 Int. Conf. Wirel. Commun. Netw. Mob. Comput. WiCOM 2008, 2008.
[182] Wanke, P., Tan, Y., Antunes, J., and Hadi-Vencheh, A., Business environment drivers and technical efficiency in the Chinese energy industry: A robust Bayesian stochastic frontier analysis, Comput. Ind. Eng., 2020, 144, 106487. Doi: 10.1016/j.cie.2020.106487
[183] Wasiaturrahma, Sukmana, R., Ajija, S. R., Salama, S. C. U., and Hudaifah, A., Financial performance of rural banks in Indonesia: A two-stage DEA approach, Heliyon, 2020, 6 (7), e04390.
 Doi: 10.1016/j.heliyon.2020.e04390
[184] Watson, J., Wickramanayke, J., and Premachandra, I. M., The value of Morningstar ratings: evidence using stochastic data envelopment analysis, Manag. Financ., 2011, 37 (2), P. 94–116.
 Doi: 10.1108/03074351111103659
[185] West, J., Capital valuation and sustainability: a data programming approach, Rev. Quant. Financ. Account., 2015, 45, P. 591–608. Doi: 10.1007/s11156-014-0448-2
[186] Widiarto, I., Emrouznejad, A., and Anastasakis, L., Observing choice of loan methods in not-for-profit microfinance using data envelopment analysis, Expert Syst. Appl., 2017, 82, P. 278–290.
 Doi: 10.1016/j.eswa.2017.03.022
[187] Wu, S., Zhang, H., Tian, Y., and Shi, L., Financial Distress Warning: An Evaluation System including Ecological Efficiency, Discret. Dyn. Nat. Soc., 2021, 2021, 5605892. Doi: 10.1155/2021/5605892
[188] Wulandari, E., Meuwissen, M. P. M., Karmana, M. H., and Oude Lansink, A. G. J. M., Performance and access to finance in Indonesian horticulture, Br. Food J., 2017, 119 (3), P. 625–638.
 Doi: 10.1108/BFJ-06-2016-0236
[189] Xiong, B., Chen, H., An, Q., and Wu, J., A multi-objective distance friction minimization model for performance assessment through data envelopment analysis, Eur. J. Oper. Res., 2019, 279 (1), P. 132–142.
 Doi: 10.1016/j.ejor.2019.05.007
[190] Xu, G., and Zhou, Z., Assessing the efficiency of financial supply chain for Chinese commercial banks: a two-stage AR-DEA model, Ind. Manag. Data Syst., 2021, 121 (4), P. 894–920. Doi: 10.1108/IMDS-01-2020-0022
[191] Yang, J., Brashear, T. G., and Asare, A., The value relevance of brand equity, intellectual capital and intellectual capital management capability, J. Strateg. Mark., 2015, 23, P. 543–559.
 Doi: 10.1080/0965254X.2014.1001863
[192] Yang, Z., A two-stage DEA model to evaluate the overall performance of Canadian life and health insurance companies, Math. Comput. Model., 2006, 43, P. 910–919. Doi: 10.1016/j.mcm.2005.12.011
[193] Yeh, C.-C., Chi, D.-J., and Hsu, M.-F., A hybrid approach of DEA, rough set and support vector machines for business failure prediction, Expert Syst. Appl., 2010, 37 (2), P. 1535–1541. Doi: 10.1016/j.eswa.2009.06.088
[194] Yeh, Q.-J., The application of data envelopment analysis in conjunction with financial ratios for bank performance evaluation, J. Oper. Res. Soc., 1996, 47, P. 980–988. Doi: 10.2307/3010406
[195] Yu, W., Liu, S., and Ding, L., Efficiency evaluation and selection strategies for green portfolios under different risk appetites, Sustain, 2021, 13, P. 1–16. Doi: 10.3390/su13041933
[196] Yu, Y., Evaluation of Environmental Efficiency in Beijing-Tianjin-Hebei Region Based on DEA, in J. Phys. Conf. Ser., 2020, Doi: 10.1088/1742-6596/1549/2/022142.
[197] Yunos, J. M., and Hawdon, D., The efficiency of the National Electricity Board in Malaysia: An intercountry comparison using DEA, Energy Econ., 1997, 19, P. 255–269, Doi: 10.1016/S0140-9883(96)01018-3.
[198] Zhang, A., Zhang, Y., and Zhao, R., Impact of Ownership and Competition on the Productivity of Chinese Enterprises, J. Comp. Econ., 2001, 29 (2), P. 327–346. Doi: 10.1006/jcec.2001.1714
[199] Zhang, C., Factors Influencing the Allocation of Regional Sci-Tech Financial Resources Based on the Multiple Regression Model, Math. Probl. Eng. 2021, 6688549. Doi: 10.1155/2021/6688549
[200] Zhang, S.-H., and Qiu, G., Research on Key Performance Evaluation Method Based on Fuzzy Analytic Hierarchy Process, Lect. Notes Inst. Comput. Sci. Soc. Telecommun. Eng. LNICST, 2021, 347, P. 472–481.
 Doi: 10.1109/ISDEA.2014.186
[201] Zhang, X.-S., and Cui, J.-C., A project evaluation system in the state economic information system of China An operations research practice in public sectors, Int. Trans. Oper. Res., 1999, 6, P. 441–452.
Doi: 10.1111/j.1475-3995.1999.tb00166.x
[202] Zhu J., Multi-factor performance measure model with an application to Fortune 500 companies, Eur. J. Oper. Res., 2000, 123 (1), P. 105–124. Doi: 10.1016/S0377-2217(99)00096-X
[203] Zhu, J., Use of DEA Cross-Efficiency Evaluation in Portfolio Selection: An application to Korean Stock Market, Eur. J. Oper. Res., 2013, 236 (1), P. 361-368, Doi: 10.1016/j.ejor.2013.12.002
[204] Zhu, W., Liu, B., Lu, Z., and Yu, Y., A DEALG methodology for prediction of effective customers of internet financial loan products, J. Oper. Res. Soc., 2021, 72, P. 1033–1041. Doi: 10.1080/01605682.2019.1700188.
Volume 7, Issue 1
January 2022
Pages 1-36
  • Receive Date: 07 May 2021
  • Revise Date: 16 August 2021
  • Accept Date: 16 September 2021
  • First Publish Date: 16 November 2021