IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v21y2021i4d10.1007_s12351-019-00532-x.html
   My bibliography  Save this article

Relative partial efficiency: network and black box SBM DEA interpretations in multiplier form

Author

Listed:
  • Fatemeh Boloori

    (University of Tabriz)

  • Rashed Khanjani-Shiraz

    (University of Tabriz)

  • Hirofumi Fukuyama

    (Fukuoka University)

Abstract

In traditional black-box DEA when the ratio-based multiplier DEA model is estimated to obtain a technical efficiency score, the estimated multipliers (shadow prices) serve as the weights that maximize the ratio of the aggregation of weighted sum of outputs (virtual output) to that of inputs (virtual input) of the assessed DMU in comparison with the other decision making units (DMUs). With respect to the ratio-based multiplier model of non-radial slack-based measure (SBM), however, there does not exist such a nice efficiency interpretation. For the purpose of providing a reasonable efficiency interpretation for both black-box and network SBM models, this paper introduces a concept called relative partial efficiency (RPE). In the black box structure, RPEs are defined for each input–output pair and a multi objective programming is formed in order to maximize RPEs. Then, it is proved that its equivalent single objective programming problem is the same SBM multiplier DEA model. The obtained explicit efficiency interpretation coming from this novel concept is then generalized for the multiplier network SBM DEA model represented by Boloori (Comput Ind Eng 95:83–96, 2016).

Suggested Citation

  • Fatemeh Boloori & Rashed Khanjani-Shiraz & Hirofumi Fukuyama, 2021. "Relative partial efficiency: network and black box SBM DEA interpretations in multiplier form," Operational Research, Springer, vol. 21(4), pages 2689-2718, December.
  • Handle: RePEc:spr:operea:v:21:y:2021:i:4:d:10.1007_s12351-019-00532-x
    DOI: 10.1007/s12351-019-00532-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-019-00532-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12351-019-00532-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    2. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    3. Cooper, W.W. & Pastor, Jesus T. & Aparicio, Juan & Borras, Fernando, 2011. "Decomposing profit inefficiency in DEA through the weighted additive model," European Journal of Operational Research, Elsevier, vol. 212(2), pages 411-416, July.
    4. Fatemeh Boloori & Jafar Pourmahmoud, 2016. "A modified SBM-NDEA approach for the efficiency measurement in bank branches," Operational Research, Springer, vol. 16(2), pages 301-326, July.
    5. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    6. Russell, R. Robert, 1985. "On the Axiomatic Approach to the Measurement of Technical Efficiency," Working Papers 85-33, C.V. Starr Center for Applied Economics, New York University.
    7. William Cooper & Kyung Park & Jesus Pastor, 1999. "RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA," Journal of Productivity Analysis, Springer, vol. 11(1), pages 5-42, February.
    8. Banker, Rajiv D. & Cooper, William W. & Seiford, Lawrence M. & Thrall, Robert M. & Zhu, Joe, 2004. "Returns to scale in different DEA models," European Journal of Operational Research, Elsevier, vol. 154(2), pages 345-362, April.
    9. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2006. "Introduction to Data Envelopment Analysis and Its Uses," Springer Books, Springer, number 978-0-387-29122-2, February.
    10. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    11. Robert Russell, R., 1985. "Measures of technical efficiency," Journal of Economic Theory, Elsevier, vol. 35(1), pages 109-126, February.
    12. Pastor, J. T. & Ruiz, J. L. & Sirvent, I., 1999. "An enhanced DEA Russell graph efficiency measure," European Journal of Operational Research, Elsevier, vol. 115(3), pages 596-607, June.
    13. Mirdehghan, S. Morteza & Fukuyama, Hirofumi, 2016. "Pareto–Koopmans efficiency and network DEA," Omega, Elsevier, vol. 61(C), pages 78-88.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gerami, Javad & Mozaffari, Mohammad Reza & Wanke, Peter F. & Correa, Henrique L., 2022. "Improving information reliability of non-radial value efficiency analysis: An additive slacks based measure approach," European Journal of Operational Research, Elsevier, vol. 298(3), pages 967-978.
    2. Meng, Fanyi & Su, Bin & Thomson, Elspeth & Zhou, Dequn & Zhou, P., 2016. "Measuring China’s regional energy and carbon emission efficiency with DEA models: A survey," Applied Energy, Elsevier, vol. 183(C), pages 1-21.
    3. Aparicio, Juan & Ortiz, Lidia & Pastor, Jesus T., 2017. "Measuring and decomposing profit inefficiency through the Slacks-Based Measure," European Journal of Operational Research, Elsevier, vol. 260(2), pages 650-654.
    4. Aparicio, Juan & Borras, Fernando & Pastor, Jesus T. & Vidal, Fernando, 2015. "Measuring and decomposing firm׳s revenue and cost efficiency: The Russell measures revisited," International Journal of Production Economics, Elsevier, vol. 165(C), pages 19-28.
    5. Sueyoshi, Toshiyuki & Sekitani, Kazuyuki, 2009. "An occurrence of multiple projections in DEA-based measurement of technical efficiency: Theoretical comparison among DEA models from desirable properties," European Journal of Operational Research, Elsevier, vol. 196(2), pages 764-794, July.
    6. Juan Aparicio & Jesus T. Pastor & Jose L. Sainz-Pardo & Fernando Vidal, 2020. "Estimating and decomposing overall inefficiency by determining the least distance to the strongly efficient frontier in data envelopment analysis," Operational Research, Springer, vol. 20(2), pages 747-770, June.
    7. Fukuyama, Hirofumi & Maeda, Yasunobu & Sekitani, Kazuyuki & Shi, Jianming, 2014. "Input–output substitutability and strongly monotonic p-norm least distance DEA measures," European Journal of Operational Research, Elsevier, vol. 237(3), pages 997-1007.
    8. Sueyoshi, Toshiyuki & Sekitani, Kazuyuki, 2007. "Computational strategy for Russell measure in DEA: Second-order cone programming," European Journal of Operational Research, Elsevier, vol. 180(1), pages 459-471, July.
    9. Ray, Subhash C. & Jeon, Yongil, 2008. "Reputation and efficiency: A non-parametric assessment of America's top-rated MBA programs," European Journal of Operational Research, Elsevier, vol. 189(1), pages 245-268, August.
    10. Barbero, Javier & Zofío, José L., 2023. "The measurement of profit, profitability, cost and revenue efficiency through data envelopment analysis: A comparison of models using BenchmarkingEconomicEfficiency.jl," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    11. Fukuyama, Hirofumi & Weber, William L., 2009. "A directional slacks-based measure of technical inefficiency," Socio-Economic Planning Sciences, Elsevier, vol. 43(4), pages 274-287, December.
    12. Rashidi, Kamran & Cullinane, Kevin, 2019. "Evaluating the sustainability of national logistics performance using Data Envelopment Analysis," Transport Policy, Elsevier, vol. 74(C), pages 35-46.
    13. Aparicio, Juan & Kapelko, Magdalena, 2019. "Accounting for slacks to measure dynamic inefficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 278(2), pages 463-471.
    14. Akther, Syed & Fukuyama, Hirofumi & Weber, William L., 2013. "Estimating two-stage network Slacks-based inefficiency: An application to Bangladesh banking," Omega, Elsevier, vol. 41(1), pages 88-96.
    15. Halická, Margaréta & Trnovská, Mária, 2018. "The Russell measure model: Computational aspects, duality, and profit efficiency," European Journal of Operational Research, Elsevier, vol. 268(1), pages 386-397.
    16. Hirofumi Fukuyama & Hiroya Masaki & Kazuyuki Sekitani & Jianming Shi, 2014. "Distance optimization approach to ratio-form efficiency measures in data envelopment analysis," Journal of Productivity Analysis, Springer, vol. 42(2), pages 175-186, October.
    17. Kao, Chiang, 2022. "A maximum slacks-based measure of efficiency for closed series production systems," Omega, Elsevier, vol. 106(C).
    18. Raul Moragues & Juan Aparicio & Miriam Esteve, 2023. "Measuring technical efficiency for multi-input multi-output production processes through OneClass Support Vector Machines: a finite-sample study," Operational Research, Springer, vol. 23(3), pages 1-33, September.
    19. Victor John M. Cantor & Kim Leng Poh, 2020. "Efficiency measurement for general network systems: a slacks-based measure model," Journal of Productivity Analysis, Springer, vol. 54(1), pages 43-57, August.
    20. Cook, Wade D. & Seiford, Larry M., 2009. "Data envelopment analysis (DEA) - Thirty years on," European Journal of Operational Research, Elsevier, vol. 192(1), pages 1-17, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:operea:v:21:y:2021:i:4:d:10.1007_s12351-019-00532-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.