266
Views
1
CrossRef citations to date
0
Altmetric
Articles

Modelling efficiency in the presence of shared inputs within groups of DMUs

ORCID Icon, ORCID Icon, & ORCID Icon
Pages 1961-1977 | Received 22 Sep 2020, Accepted 25 Jul 2021, Published online: 24 Aug 2021

References

  • Abbott, M., & Doucouliagos, C. (2003). The efficiency of Australian universities: A data envelopment analysis. Economics of Education Review, 22(1), 89–97. https://doi.org/10.1016/S0272-7757(01)00068-1
  • Agasisti, T., Catalano, G., Landoni, P., & Verganti, R. (2012). Evaluating the performance of academic departments: An analysis of research-related output efficiency. Research Evaluation, 21(1), 2–14. https://doi.org/10.1093/reseval/rvr001
  • Agha, S. R., Kuhail, I., Abdul Nabi, N., Salem, M., & Ghanim, A. (2011). Assessment of academic departments efficiency using data envelopment analysis. Journal of Industrial Engineering and Management, 4(2), 301–325. https://doi.org/10.3926/jiem.2011.v4n2.p301-325
  • Amin, G. R., Emrouznejad, A., & Rezaei, S. (2011). Some clarifications on the DEA clustering approach. European Journal of Operational Research, 215(2), 498–501. https://doi.org/10.1016/j.ejor.2011.06.043
  • Amirteimoori, A., & Nashtaei, R. A. (2006). The role of time in multi-component efficiency analysis: An application. Applied Mathematics and Computation, 177(1), 11–17. https://doi.org/10.1016/j.amc.2005.10.029
  • Avilés-Sacoto, S. V., Cook, W. D., Güemes-Castorena, D., & Zhu, J. (2020). Measuring efficiency in DEA in the presence of common inputs. Journal of the Operational Research Society, 71(11), 1710–1722. https://doi.org/10.1080/01605682.2019.1630329
  • Aziz, N. A. A., Janor, R. M., & Mahadi, R. (2013). Comparative departmental efficiency analysis within a university: A DEA approach. Procedia - Social and Behavioral Sciences, 90(InCULT 2012), 540–548. https://doi.org/10.1016/j.sbspro.2013.07.124
  • Beasley, J. E. (1995). Determining teaching and research efficiencies. Journal of the Operational Research Society, 46(4), 441–452. https://doi.org/10.1057/jors.1995.63
  • Bi, G. B., Song, W., & Wu, J. (2014). A clustering method for evaluating the environmental performance based on slacks-based measure. Computers & Industrial Engineering, 72(1), 169–177. https://doi.org/10.1016/j.cie.2014.03.016
  • Bian, Y., Hu, M., & Xu, H. (2015). Measuring efficiencies of parallel systems with shared inputs/outputs using data envelopment analysis. Kybernetes, 44(3), 336–352. https://doi.org/10.1108/K-04-2014-0067
  • Cáceres V, H., Kristjanpoller R, W., & Tabilo A, J. (2014). Análisis de la eficiencia técnica y su relación con los resultados de la evaluación de desempeño en una Universidad chilena. Innovar, 24(54), 199–217. https://doi.org/10.15446/innovar.v24n54.46720
  • Chalos, P. (1997). An examination of budgetary inefficiency in education using data envelopment analysis. Financial Accountability and Management, 13(1), 55–69. https://doi.org/10.1111/1468-0408.00026
  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficienccy of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
  • Chen, Y., Du, J., Sherman, D. H., & Zhu, J. (2010). DEA model with shared resources and efficiency decomposition. European Journal of Operational Research, 207(1), 339–349. https://doi.org/10.1016/j.ejor.2010.03.031
  • Chen, P. C., Hsu, S. H., Chang, C. C., & Yu, M. M. (2013). Efficiency measurements in multi-activity data envelopment analysis with shared inputs: An application to farmers’ cooperatives in Taiwan. China Agricultural Economic Review, 5(1), 24–42. https://doi.org/10.1108/17561371311294748
  • Chen, H., Lin, H., & Zou, W. (2020). Research on the regional differences and influencing factors of the innovation efficiency of china’s high-tech industries: Based on a shared inputs two-stage network DEA. Sustainability, 12(8), 3284. https://doi.org/10.3390/su12083284
  • Cook, W. D., & Hababou, M. (2001). Sales performance measurement in bank branches. Omega, 29(4), 299–307. https://doi.org/10.1016/S0305-0483(01)00025-1
  • Cook, W. D., Hababou, M., & Tuenter, H. J. H. (2000). Multicomponent efficiency measurement and shared inputs in data envelopment analysis: An application to sales and service performance in bank branches. Journal of Productivity Analysis, 14(3), 209–224. https://doi.org/10.1023/A:1026598803764
  • Cook, W. D., Imanirad, R., Harrison, J., Rouse, P., & Zhu, J. (2013). Data envelopment analysis with non-homogeneous decision making units. Operations Research, 61(3), 666–676. https://doi.org/10.1287/opre.2013.1173
  • Cook, W. D., & Zhu, J. (2011). Multiple variable proportionality in data envelopment analysis. Operations Research, 59(4), 1024–1032. https://doi.org/10.1287/opre.1110.0937
  • De Witte, K., & López-Torres, L. (2017). Efficiency in education: A review of literature and a way forward. Journal of the Operational Research Society, 68(4), 339–363. https://doi.org/10.1057/jors.2015.92
  • Ding, J., Dong, W., Liang, L., & Zhu, J. (2017). Goal congruence analysis in multi-Division Organizations with shared resources based on data envelopment analysis. European Journal of Operational Research, 263(3), 961–973. https://doi.org/10.1016/j.ejor.2017.06.040
  • Imanirad, R., Cook, W. D., & Zhu, J. (2013). Partial input to output impacts in DEA: Production considerations and resource sharing among business sub-units. Naval Research Logistics (NRL), 60(3), 190–207. https://doi.org/10.1002/nav.21528
  • Jiang, H., Hua, M., Zhang, J., Cheng, P., Ye, Z., Huang, M., & Jin, Q. (2020). Sustainability efficiency assessment of wastewater treatment plants in China: A data envelopment analysis based on cluster benchmarking. Journal of Cleaner Production, 244, 118729. https://doi.org/10.1016/j.jclepro.2019.118729
  • Kao, C., & Hung, H. T. (2008). Efficiency analysis of university departments: An empirical study. Omega, 36(4), 653–664. https://doi.org/10.1016/j.omega.2006.02.003
  • Lee, J., Kim, C., & Choi, G. (2019). Exploring data envelopment analysis for measuring collaborated innovation efficiency of small and medium-sized enterprises in Korea. European Journal of Operational Research, 278(2), 533–545. https://doi.org/10.1016/j.ejor.2018.08.044
  • Ma, J. (2015). A two-stage DEA model considering shared inputs and free intermediate measures. Expert Systems with Applications, 42(9), 4339–4347. https://doi.org/10.1016/j.eswa.2015.01.040
  • Mahmoudi, R., Emrouznejad, A., Khosroshahi, H., Khashei, M., & Rajabi, P. (2019). Performance evaluation of thermal power plants considering CO2 emission: A multistage PCA, clustering, game theory and data envelopment analysis. Journal of Cleaner Production, 223, 641–650. https://doi.org/10.1016/j.jclepro.2019.03.047
  • Moreno, A. A., & Tadepalli, R. (2002). Assessing academic department efficiency at a public university. Managerial and Decision Economics, 23(7), 385–397. https://doi.org/10.1002/mde.1075
  • Paradi, J. C., Zhu, H., & Edelstein, B. (2012). Identifying managerial groups in a large Canadian bank branch network with a DEA approach. European Journal of Operational Research, 219(1), 178–187. https://doi.org/10.1016/j.ejor.2011.12.022
  • Po, R. W., Guh, Y. Y., & Yang, M. S. (2009). A new clustering approach using data envelopment analysis. European Journal of Operational Research, 199(1), 276–284. https://doi.org/10.1016/j.ejor.2008.10.022
  • Sarrico, C., Hogan, S., Dyson, R., & Athanassopoulos, A. (1997). Data envelopment analysis and university selection. Journal of the Operational Research Society, 48(12), 1163–1177. https://doi.org/10.1057/jors.2010.172
  • Sîrbu, A., Cimpoieş, D., & Racul, A. (2016). Use of data envelopment analysis to measure the performance efficiency of academic departments. Agriculture and Agricultural Science Procedia, 10, 578–585. https://doi.org/10.1016/j.aaspro.2016.09.037
  • Thanassoulis, E., Shiraz, R. K., & Maniadakis, N. (2015). A cost Malmquist productivity index capturing group performance. European Journal of Operational Research, 241(3), 796–805. https://doi.org/10.1016/j.ejor.2014.09.002
  • Tsai, P. F., & Molinero, C. M. (2002). A variable returns to scale data envelopment analysis model for the joint determination of efficiencies with an example of the UK health service. European Journal of Operational Research, 141(1), 21–38. https://doi.org/10.1016/S0377-2217(01)00223-5
  • Tyagi, P., Yadav, S. P., & Singh, S. P. (2009). Relative performance of academic departments using DEA with sensitivity analysis. Evaluation and Program Planning, 32(2), 168–177. https://doi.org/10.1016/j.evalprogplan.2008.10.002
  • Ulucan, A., & Bariş Atici, K. (2010). Efficiency evaluations with context-dependent and measure-specific data envelopment approaches: An application in a World Bank supported project. Omega, 38(1-2), 68–83. https://doi.org/10.1016/j.omega.2009.04.003
  • Wu, J., Zhu, Q., Ji, X., Chu, J., & Liang, L. (2016). Two-stage network processes with shared resources and resources recovered from undesirable outputs. European Journal of Operational Research, 251(1), 182–197. https://doi.org/10.1016/j.ejor.2015.10.049
  • Zelenyuk, V. (2011). Aggregation of economic growth rates and of its sources. European Journal of Operational Research, 212(1), 190–198. https://doi.org/10.1016/j.ejor.2011.01.008

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.