EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 58 / No 3 (May-June 2024) RAIRO-Oper. Res., 58 3 (2024) 2525-2541 References
Open Access
Issue
RAIRO-Oper. Res.
Volume 58, Number 3, May-June 2024
Page(s) 2525 - 2541
DOI https://doi.org/10.1051/ro/2024089
Published online 25 June 2024
  • H. Abbasiyan, Upgrading inefficient decision making units (with negative data) towards common weights (using DEA). Iran. J. Optim. 13 (2021) 161–167. [Google Scholar]
  • P.A. Aghimien, F. Kamarudin, M. Amid and B. Noordin, Efficiency of gulf cooperation council banks: empirical evidence using data envelopment analysis. Rev. Int. Bus. Strategy 26 (2016) 118–136. [CrossRef] [Google Scholar]
  • M. Afsharian and V.V. Podinovski, A linear programming approach to efficiency evaluation in nonconvex metatechnologies. Eur. J. Oper. Res. 268 (2018) 268–280. [CrossRef] [Google Scholar]
  • M. Afsharian, H. Ahn and S.G. Harms, Performance comparison of management groups under centralised management. Eur. J. Oper. Res.. 278 (2019) 845–854. [CrossRef] [Google Scholar]
  • M. Alsharif, Banks efficiency, ownership type and listing status in Gulf cooperation council countries: a cross-countries analysis. J. Crit. Rev. 7 (2020) 309–319. [Google Scholar]
  • S. Ang, M. Chen and F. Yang, Group cross-efficiency evaluation in data envelopment analysis: an application to Taiwan hotels. Comput. Ind. Eng. 125 (2018) 190–199. [CrossRef] [Google Scholar]
  • H. Babaie Asil, R.K. Matin, M. Khounsiavash and Z. Moghadas, A modified semi-oriented radial measure to deal with negative and stochastic data: an application in banking industry. Math. Sci. 16 (2021) 1–13. [Google Scholar]
  • R.D. Banker and R.C. Morey, The use of categorical variables in data envelopment analysis. Manage. Sci. 32 (1986) 1613–1627. [CrossRef] [Google Scholar]
  • R.D. Banker, A. Charnes and W.W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage. Sci. 30 (1984) 1078–1092. [Google Scholar]
  • A. Charnes, W.W. Cooper and E. Rhodes, Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2 (1978) 429–444. [Google Scholar]
  • A. Charnes, W.W. Cooper, B. Golany, L. Seiford and J. Stutz, Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. J. Econ. 30 (1985) 91–107. [CrossRef] [Google Scholar]
  • G. Cheng, P. Zervopoulos and Z. Qian, A variant of radial measure capable of dealing with negative inputs and outputs in data envelopment analysis. Eur. J. Oper. Res.. 225 (2013) 100–105. [CrossRef] [Google Scholar]
  • W.D. Cook and R.H. Green, Evaluating power plant efficiency: a hierarchical model. Comput. Oper. Res. 32 (2005) 813–823. [CrossRef] [Google Scholar]
  • W.D. Cook and J. Zhu, Within-group common weights in DEA: an analysis of power plant efficiency. Eur. J. Oper. Res. 178 (2007) 207–216. [CrossRef] [Google Scholar]
  • W.D. Cook, D. Chai, J. Doyle and R. Green, Hierarchies and groups in DEA. J. Prod. Anal. 10 (1998) 177–198. [CrossRef] [Google Scholar]
  • W.D. Cook, J. Ruiz, I. Sirvent and J. Zhu, Within-group common benchmarking using DEA. Eur. J. Oper. Res. 256 (2017) 901–910. [CrossRef] [Google Scholar]
  • G. Debreu, The coefficient of resource utilization. Econ. J. Econ. Soc. (1951) 273–292. [Google Scholar]
  • C.J. Donnell, D.P. Rao and G.E. Battese, Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical Econ. 34 (2008) 231–255. [CrossRef] [Google Scholar]
  • A. Emrouznejad and A.L. Anouze, Data envelopment analysis with classification and regression tree – a case of banking efficiency. Expert Syst. 27 (2010) 231–246. [CrossRef] [Google Scholar]
  • A. Emrouznejad and G.-L. Yang, A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Soc.-Econ. Planning Sci. 61 (2018) 4–8. [CrossRef] [Google Scholar]
  • A. Emrouznejad, B.R. Parker and G. Tavares, Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA. Soc.-Econ. Sci. 42 (2008) 151–157. [Google Scholar]
  • A. Emrouznejad, A.L. Anouze and E. Thanassoulis, A semi-oriented radial measure for measuring the efficiency of decision making units with negative data, using DEA. Eur. J. Oper. Res. 200 (2010) 297–304. [Google Scholar]
  • M.J. Farrell, The measurement of productive efficiency. J. R. Stat. Soc. Ser. A: Stat. Soc. 120 (1957) 253–281. [CrossRef] [Google Scholar]
  • H. Fukuyama and W.L. Weber, A directional slacks-based measure of technical inefficiency. Soc.-Econ. Planning Sci. 43 (2009) 274–287. [CrossRef] [Google Scholar]
  • H. Fukuyama, R. Matousek and N.G. Tzeremes, A Nerlovian cost inefficiency two-stage DEA model for modeling banks’ production process: evidence from the Turkish banking system. Omega 95 (2020) 102198. [CrossRef] [Google Scholar]
  • H. Fukuyama, R. Matousek and N.G. Tzeremes, A unified framework for nonperforming loan modeling in bank production: an application of Data Envelopment Analysis. Omega 126 (2024) 103063. [CrossRef] [Google Scholar]
  • I.C. Henriques, V.A. Sobreiro, H. Kimura and E.B. Mariano, Efficiency in the Brazilian banking system using data envelopment analysis. Future Bus. J. 4 (2018) 157–178. [CrossRef] [Google Scholar]
  • S. Kaffash, R. Kazemi Matin and M. Tajik, A directional semi-oriented radial DEA measure: an application on financial stability and the efficiency of banks. Ann. Oper. Res. 264 (2018) 213–234. [CrossRef] [MathSciNet] [Google Scholar]
  • K. Kerstens and I. Van de Woestyne, Negative data in DEA: a simple proportional distance function approach. J. Oper. Res. Soc. 62 (2011) 1413–1419. [CrossRef] [Google Scholar]
  • J.S. Liu, L.Y.Y. Lu, W.M. Lu and B.J.U. Lin, Data envelopment analysis 1978–2010: a citation-based literature survey. Omega 41 (2013) 3–15. [CrossRef] [Google Scholar]
  • C.K. Lovel, Measuring the macroeconomic performance of the Taiwanese economy. Int. J. Prod. Econ. 39 (1995) 165–178. [CrossRef] [Google Scholar]
  • M.Z. Mahmoudabadi and A. Emrouznejad, Comprehensive performance evaluation of banking branches: a three-stage Slacks-Based Measure (SBM) data envelopment analysis. Int. Rev. Econ. Finan. 64 (2019) 359–376. [CrossRef] [Google Scholar]
  • R.K. Matin and M.I. Ghahfarokhi, A two-phase modified slack-based measure approach for efficiency measurement and target setting in data envelopment analysis with negative data. IMA J. Manage. Math. 26 (2015) 83–98. [Google Scholar]
  • R.K. Matin, G.R. Amin and A. Emrouznejad, A modified semi-oriented radial measure for target setting with negative data. Measurement 54 (2014) 152–158. [CrossRef] [Google Scholar]
  • T. Mohamed Shahwan and A. Kaba, Efficiency analysis of GCC academic libraries: an application of data envelopment analysis. Perform. Meas. Metrics 14 (2013) 197–210. [CrossRef] [Google Scholar]
  • H. Omrani, A. Emrouznejad. M. Shamsi and P. Fahimi, Evaluation of insurance companies considering uncertainty: a multi-objective network data envelopment analysis model with negative data and undesirable outputs. Soc.-Econ. Planning Sci. 82 (2022) 101306. [CrossRef] [Google Scholar]
  • A. Panwar, M. Olfati, M. Pant and V. Snasel, A review on the 40 years of existence of data envelopment analysis models: historic development and current trends. Arch. Comput. Methods Eng. 29 (2022) 5397–5426. [CrossRef] [PubMed] [Google Scholar]
  • J.C. Paradi and H. Zhu, A survey on bank branch efficiency and performance research with data envelopment analysis. Omega 41 (2013) 61–79. [Google Scholar]
  • J.T. Pastor and J.L. Ruiz, Variables with negative values in DEA, in Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, edited by J. Zhu and W.D. Cook. Springer, Boston, MA (2007) 63–84. [Google Scholar]
  • M.S. Portela, E. Thanassoulis and G. Simpson, Negative data in DEA: a directional distance approach applied to bank branches. J. Oper. Res. Soc. 55 (2004) 1111–1121. [Google Scholar]
  • M.J. Rezaee and A. Karimdadi, Do geographical locations affect in hospitals performance? A multi-group data envelopment analysis. J. Med. Syst. 39 (2015) 1–11. [CrossRef] [PubMed] [Google Scholar]
  • R. Rostamzadeh, O. Akbarian, A. Banaitis and Z. Soltani, Application of DEA in benchmarking: a systematic literature review from 2003 to 2020. Technol. Econ. Dev. Econ. 27 (2021) 175–222. [CrossRef] [Google Scholar]
  • A.S. Saleh, A. Moradi-Motlagh and R. Zeitun, What are the drivers of inefficiency in the Gulf cooperation council banking industry? A comparison between conventional and Islamic banks. Pac.-Basin Finan. J. 60 (2020) 101266. [CrossRef] [Google Scholar]
  • H. Scheel, Negative Data and Undesirable Outputs in DEA. Euro Summer Institute (1998). [Google Scholar]
  • H. Scheel, Undesirable outputs in efficiency valuations. Eur. J. Oper. Res. 132 (2001) 400–410. [Google Scholar]
  • L.M. Seiford, A bibliography for data envelopment analysis (1978–1996). Ann. Oper. Res. 73 (1997) 393–438. [CrossRef] [Google Scholar]
  • L.M. Seiford and J. Zhu, Modeling undesirable factors in efficiency evaluation. Eur. J. Oper. Res. 142 (2002) 16–20. [Google Scholar]
  • M.S. Shahbazifar, R. Kazemi Matin, M. Khounsiavash and F. Koushki, Group ranking of two-stage production units in network data envelopment analysis. RAIRO-Oper. Res. 55 (2021) 1825–1840. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • J.A. Sharp, W. Meng and W. Liu, A modified slacks-based measure model for data envelopment analysis with “natural” negative outputs and inputs. J. Oper. Res. Soc. 58 (2007) 1672–1677. [Google Scholar]
  • B.M. Sillah and N. Harrathi, Bank efficiency analysis: Islamic banks versus conventional banks in the Gulf Cooperation Council Countries 2006–2012. Int. J. Finan. Res. 6 (2015) 143–150. [Google Scholar]
  • K. Tone, A slacks-based measure of efficiency in data envelopment analysis. Eur. J. Oper. Res. 130 (2001) 498–509. [Google Scholar]
  • K. Tone, T.-S. Chang and C.-H. Wu, Handling negative data in slacks-based measure data envelopment analysis models. Eur. J. Oper. Res. 282 (2020) 926–935. [CrossRef] [Google Scholar]
  • M. Xia, J. Chen and X.J. Zeng, Data envelopment analysis based on team reasoning. Int. Trans. Oper. Res. 27 (2020) 1080–1100. [CrossRef] [MathSciNet] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.