Open Access
RAIRO-Oper. Res.
Volume 56, Number 2, March-April 2022
Page(s) 649 - 687
Published online 14 April 2022
  • A.I. Ali and L.M. Seiford, Translation invariance in data envelopment analysis. Oper. Res. Lett. 9 (1990) 403–405. [CrossRef] [Google Scholar]
  • J. Aparicio, J.T. Pastor and J.L. Zofio, On the inconsistency of the Malmquist-Luenberger index. Eur. J. Oper. Res. 229 (2013) 738–742. [CrossRef] [Google Scholar]
  • J. Aparicio, J.T. Pastor and F. Vidal, The directional distance function and the translation invariance property. Omega 58 (2016) 1–3. [CrossRef] [Google Scholar]
  • J. Aparicio, J. Barbero, M. Kapelko, J.T. Pastor and J.L. Zofo, Testing the consistency and feasibility of the standard Malmquist-Luenberger index: environmental productivity in world air emissions. J. Environ. Manage. 196 (2017) 148–160. [CrossRef] [Google Scholar]
  • B. Arabi, S. Munisamy and A. Emrouznejad, A new slacks-based measure of Malmquist-Luenberger index in the presence of undesirable outputs. Omega 51 (2015) 29–37. [Google Scholar]
  • N.K. Avkiran, An illustration of dynamic network DEA in commercial banking including robustness tests. Omega 55 (2015) 141–150. [CrossRef] [Google Scholar]
  • W. Briec and K. Kerstens, Infeasibility and directional distance functions with application to the determinateness of the luenberger productivity indicator. J. Optim. Theory App. 141 (2009) 55. [CrossRef] [Google Scholar]
  • W. Briec and K. Kerstens, The luenberger productivity indicator: an economic specification leading to infeasibilities. Econ. Modell. 26 (2009) 597–600. [CrossRef] [Google Scholar]
  • D.W. Caves, L.R. Christensen and W.E. Diewert, The economic theory of index numbers and the measurement of input, output, and productivity. Econ. J. Econ. Soc. 50 (1982) 1393–1414. [Google Scholar]
  • Y.-T. Chang, H.K. Park, B. Zou and N. Kafle, Passenger facility charge vs. airport improvement program funds: a dynamic network DEA analysis for US airport financing. Transp. Res. Part E: Logistics Transp. Rev. 88 (2016) 76–93. [CrossRef] [Google Scholar]
  • A. Charnes, W.W. Cooper, B. Golany, R. Halek, G. Klopp, E. Schmitz and D. Thomas, Two phase data envelopment analysis approaches to policy evaluation and management of army recruiting activities: tradeoffs between joint services and army advertising. In: Center for Cybernetic Studies. University of Texas-Austin Austin, Texas, USA (1986). [Google Scholar]
  • Y. Chen, A non-radial Malmquist productivity index with an illustrative application to Chinese major industries. Int. J. Prod. Econ. 83 (2003) 27–35. [CrossRef] [Google Scholar]
  • K. Chen and J. Zhu, Scale efficiency in two-stage network DEA. J. Oper. Res. Soc. 70 (2019) 101–110. [CrossRef] [Google Scholar]
  • Y.H. Chung, R. Färe and S. Grosskopf, Productivity and undesirable outputs: a directional distance function approach. J. Environm. Manage. 51 (1997) 229–240. [CrossRef] [Google Scholar]
  • T.J. Coelli, D.S.P. Rao, C.J. O’Donnell and G.E. Battese, An Introduction to Efficiency and Productivity Analysis. Springer Science & Business Media (2005). [Google Scholar]
  • W.D. Cook, L. Liang and J. Zhu, Measuring performance of two-stage network structures by DEA: a review and future perspective. Omega 38 (2010) 423–430. [Google Scholar]
  • C.A. Denizer, M. Dinc and M. Tarimcilar, Financial liberalization and banking efficiency: evidence from Turkey. J. Prod. Anal. 27 (2007) 177–195. [CrossRef] [Google Scholar]
  • J. Du, Y. Chen and Y. Huang, A modified Malmquist-Luenberger productivity index: assessing environmental productivity performance in China. Eur. J. Oper. Res. 269 (2018) 171–187. [CrossRef] [Google Scholar]
  • J. Du, Y. Duan and J. Xu, The infeasible problem of Malmquist-Luenberger index and its application on China’s environmental total factor productivity. Ann. Oper. Res. 278 (2019) 235–253. [CrossRef] [MathSciNet] [Google Scholar]
  • A. Emrouznejad and E. Thanassoulis, A mathematical model for dynamic efficiency using data envelopment analysis. Appl. Math. Comput. 160 (2005) 363–378. [Google Scholar]
  • A. Emrouznejad and E. Thanassoulis, Measurement of productivity index with dynamic DEA. Int. J. Oper. Res. 8 (2010) 247. [CrossRef] [Google Scholar]
  • A. Emrouznejad and G.-L. Yang, A framework for measuring global Malmquist-Luenberger productivity index with CO2 emissions on Chinese manufacturing industries. Energy 115 (2016) 840–856. [CrossRef] [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. [CrossRef] [Google Scholar]
  • G. Falavigna, R. Ippoliti and G.B. Ramello, DEA-based Malmquist productivity indexes for understanding courts reform. Soc.-Econ. Planning Sci. 62 (2018) 31–43. [CrossRef] [Google Scholar]
  • R. Färe and S. Grosskopf, Intertemporal production frontiers with dynamic DEA. In: Kluwer Academic, collaboration with R. Briinnlund et al. Boston (1996). [CrossRef] [Google Scholar]
  • R. Färe, S. Grosskopf and C.A.K. Lovell, Production Frontiers. Cambridge University Press (1994). [Google Scholar]
  • R. Färe, S. Grosskopf, M. Norris and Z. Zhang, Productivity growth, technical progress, and efficiency change in industrialized countries. Am. Econ. Rev. 84 (1994) 66–83. [Google Scholar]
  • D. Fernández, C. Pozo, R. Folgado, L. Jiménez and G. Guillén-Gosálbez, Productivity and energy efficiency assessment of existing industrial gases facilities via data envelopment analysis and the Malmquist index. Appl. Energy 212 (2018) 1563–1577. [CrossRef] [Google Scholar]
  • H. Fukuyama and W.L. Weber, Measuring Japanese bank performance: a dynamic network DEA approach. J. Prod. Anal. 44 (2015) 249–264. [CrossRef] [Google Scholar]
  • H. Fukuyama and W.L. Weber, Japanese bank productivity, 2007–2012: a dynamic network approach. Pac. Econ. Rev. 22 (2017) 649–676. [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]
  • S. Ghobadi, A generalized DEA model for inputs (outputs) estimation under inter-temporal dependence. RAIRO-Oper. Res. 53 (2019) 1791–1805. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • E. Grifell-Tatjé and C.A.K. Lovell, A note on the Malmquist productivity index. Econ. Lett. 47 (1995) 169–175. [CrossRef] [Google Scholar]
  • I.C. Henriques, V.A. Sobreiro, H. Kimura and E.B. Mariano, Two-stage DEA in banks: terminological controversies and future directions. Expert Syst. App. 161 (2020) 113–632. [Google Scholar]
  • O. Herrera-Restrepo, K. Triantis, J. Trainor, P. Murray-Tuite and P. Edara, A multi-perspective dynamic network performance efficiency measurement of an evacuation: a dynamic network-DEA approach. Omega 60 (2016) 45–59. [CrossRef] [Google Scholar]
  • D. Holod and H.F. Lewis, Resolving the deposit dilemma: a new DEA bank efficiency model. J. Banking Finance 35 (2011) 2801–2810. [Google Scholar]
  • J. Jablonsky, Ranking of countries in sporting events using two-stage data envelopment analysis models: a case of Summer Olympic Games 2016. Central Eur. J. Oper. Res. 26 (2018) 951–966. [CrossRef] [MathSciNet] [Google Scholar]
  • G.R. Jahanshahloo and M. Piri, Data Envelopment Analysis (DEA) with integer and negative inputs and outputs. J. Data Envelopment Anal. Decis. Sci. 2013 (2013) 1–15. [Google Scholar]
  • A.R. Jayaraman and P. Bhuyan, Impact of NPA and loan write-offs on the profit efficiency of Indian banks. Decision 47 (2020) 35–48. [CrossRef] [Google Scholar]
  • C. Kao, Dynamic data envelopment analysis: a relational analysis. Eur. J. Oper. Res. 227 (2013) 325–330. [CrossRef] [Google Scholar]
  • C. Kao, Network data envelopment analysis: a review. Eur. J. Oper. Res. 239 (2014) 1–16. [Google Scholar]
  • C. Kao, Network Data Envelopment Analysis: Foundations and Extensions. Springer (2017). [CrossRef] [Google Scholar]
  • S. Kourtzidis, R. Matousek and N.G. Tzeremes, Modelling a multi-period production process: evidence from the Japanese regional banks. Eur. J. Oper. Res. 294 (2021) 327–339. [CrossRef] [Google Scholar]
  • S. Kumar, Environmentally sensitive productivity growth: a global analysis using Malmquist-Luenberger index. Ecol. Econ. 56 (2006) 280–293. [CrossRef] [Google Scholar]
  • R. Lin and Z. Chen, Super-efficiency measurement under variable return to scale: an approach based on a new directional distance function. J. Oper. Res. Soc. 66 (2015) 1506–1510. [CrossRef] [Google Scholar]
  • R. Lin and Z. Chen, A directional distance based super-efficiency DEA model handling negative data. J. Oper. Res. Soc. 68 (2017) 1312–1322. [CrossRef] [Google Scholar]
  • R. Lin and Y. Liu, Super-efficiency based on the directional distance function in the presence of negative data. Omega 85 (2019) 26–34. [CrossRef] [Google Scholar]
  • C.A.K. Lovell, The decomposition of malmquist productivity indexes. J. Prod. Anal. 20 (2003) 437–458. [CrossRef] [Google Scholar]
  • C.A.K. Lovell, J.T. Pastor and J.A. Turner, Measuring macroeconomic performance in the OECD: a comparison of European and non-European countries. Eur. J. Oper. Res. 87 (1995) 507–518. [CrossRef] [Google Scholar]
  • S. Lozano and N. Soltani, Efficiency assessment using a multidirectional DDF approach. Int. Trans. Oper. Res. 27 (2020) 2064–2080. [Google Scholar]
  • S. Malmquist, Index numbers and indifference surfaces. Trabajos de Estadistica y de Investigacion Operativa 4 (1953) 209–242. [CrossRef] [Google Scholar]
  • F.B. Mariz, M.R. Almeida and D. Aloise, A review of dynamic data envelopment analysis: state of the art and applications. Int. Trans. Oper. Res. 25 (2018) 469–505. [CrossRef] [MathSciNet] [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]
  • P. Moreno and S. Lozano, Super SBI Dynamic Network DEA approach to measuring efficiency in the provision of public services. Int. Trans. Oper. Res. 25 (2018) 715–735. [CrossRef] [MathSciNet] [Google Scholar]
  • E. Njuki, B.E. Bravo-Ureta and C.J. O’Donnell, Decomposing agricultural productivity growth using a random-parameters stochastic production frontier. Empirical Econ. 57 (2019) 839–860. [CrossRef] [Google Scholar]
  • D.-H. Oh and A. Heshmati, A sequential Malmquist-Luenberger productivity index: environmentally sensitive productivity growth considering the progressive nature of technology. Energy Econ. 32 (2010) 1345–1355. [CrossRef] [Google Scholar]
  • K.S. Park and K. Park, Measurement of multiperiod aggregative efficiency. Eur. J. Oper. Res. 193 (2009) 567–580. [CrossRef] [Google Scholar]
  • M.C.A.S. Portela and E. Thanassoulis, Malmquist-type indices in the presence of negative data: an application to bank branches. J. Banking Finance 34 (2010) 1472–1483. [CrossRef] [Google Scholar]
  • M.C.A.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. [CrossRef] [Google Scholar]
  • S.C. Ray and E. Desli, Productivity growth, technical progress, and efficiency change in industrialized countries: comment. Am. Econ. Rev. 87 (1997) 1033–1039. [Google Scholar]
  • R.R. Russell and W. Schworm, Technological inefficiency indexes: a binary taxonomy and a generic theorem. J. Prod. Anal. 49 (2018) 17–23. [CrossRef] [Google Scholar]
  • H. Scheel, Undesirable outputs in efficiency valuations. Eur. J. Oper. Res. 132 (2001) 400–410. [Google Scholar]
  • L.M. Seiford and J. Zhu, Modeling undesirable factors in efficiency evaluation. Eur. J. Oper. Res. 142 (2002) 16–20. [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. [CrossRef] [Google Scholar]
  • V. Shestalova, Sequential Malmquist indices of productivity growth: an application to OECD industrial activities. J. Prod. Anal. 19 (2003) 211–226. [CrossRef] [Google Scholar]
  • R.M. Solow, Technical change and the aggregate production function. Rev. Econ. Stat. 39 (1957) 312–320. [CrossRef] [Google Scholar]
  • M. Tavana, M. Izadikhah, D. Di Caprio and R.F. Saen, A new dynamic range directional measure for two-stage data envelopment analysis models with negative data. Comput. Ind. Eng. 115 (2018) 427–448. [CrossRef] [Google Scholar]
  • K. Tone and M. Tsutsui, Dynamic DEA: a slacks-based measure approach. Omega 38 (2010) 145–156. [CrossRef] [Google Scholar]
  • K. Tone and M. Tsutsui, Dynamic DEA with network structure: a slacks-based measure approach. Omega 42 (2014) 124–131. [CrossRef] [Google Scholar]
  • H. Tulkens and P.V. Eeckaut, Non-parametric efficiency, progress and regress measures for panel data: methodological aspects. Eur. J. Oper. Res. 80 (1995) 474–499. [CrossRef] [Google Scholar]
  • B. Walheer, Malmquist productivity index for multi-output producers: an application to electricity generation plants. Soc.-Econ. Plan. Sci. 65 (2019) 76–88. [CrossRef] [Google Scholar]
  • D.C. Wheelock and P.W. Wilson, Technical progress, inefficiency, and productivity change in us banking, 1984–1993. J. Money Credit Bank. 31 (1999) 212–234. [CrossRef] [Google Scholar]
  • A. Zanella, A.S. Camanho and T.G. Dias, Undesirable outputs and weighting schemes in composite indicators based on data envelopment analysis. Eur. J. Oper. Res. 245 (2015) 517–530. [Google Scholar]
  • Y. Zha, N. Liang, M. Wu and Y. Bian, Efficiency evaluation of banks in China: a dynamic two-stage slacks-based measure approach. Omega 60 (2016) 60–72. [CrossRef] [Google Scholar]
  • J.L. Zofio, Malmquist productivity index decompositions: a unifying framework. Appl. Econ. 39 (2007) 2371–2387. [CrossRef] [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.