IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v254y2016i2p532-549.html
   My bibliography  Save this article

DEA models incorporating uncertain future performance

Author

Listed:
  • Chang, Tsung-Sheng
  • Tone, Kaoru
  • Wu, Chen-Hui

Abstract

Conventional data envelopment analysis (DEA) models are designed for measuring the productive efficiency of decision making units (DMUs) based merely on historical data. However, in many practical applications, such past results are not sufficient for evaluating a DMU's performance in highly volatile operating environments, such as those with highly volatile crude oil prices and currency exchange rates. That is, in such environments, a DMU's whole performance may be seriously distorted if its future performance, which is sensitive to crude oil price volatility and/or currency fluctuations, is ignored in the evaluation process. However, despite its importance, to our knowledge, there are no DEA models proposed in the literature that explicitly take future performance volatility into account. Hence, this research aims at developing a new system of DEA models that incorporate a DMU's uncertain future performance, and thus can be applied to fully measure their efficiency.

Suggested Citation

  • Chang, Tsung-Sheng & Tone, Kaoru & Wu, Chen-Hui, 2016. "DEA models incorporating uncertain future performance," European Journal of Operational Research, Elsevier, vol. 254(2), pages 532-549.
    • Handle: RePEc:eee:ejores:v:254:y:2016:i:2:p:532-549
    DOI: 10.1016/j.ejor.2016.04.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221716302247
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2016.04.005?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. Nada R. Sanders & Karl B. Manrodt, 1994. "Forecasting Practices in US Corporations: Survey Results," Interfaces, INFORMS, vol. 24(2), pages 92-100, April.
    2. Tone, Kaoru & Tsutsui, Miki, 2010. "Dynamic DEA: A slacks-based measure approach," Omega, Elsevier, vol. 38(3-4), pages 145-156, June.
    3. 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.
    4. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, January.
    5. Tsung-Sheng Chang & Kaoru Tone & Chen-Hui Wu, 2015. "Past-present-future Intertemporal DEA models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(1), pages 16-32, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chang, Tsung-Sheng & Tone, Kaoru & Wu, Chen-Hui, 2021. "Nested dynamic network data envelopment analysis models with infinitely many decision making units for portfolio evaluation," European Journal of Operational Research, Elsevier, vol. 291(2), pages 766-781.
    2. Xu Gong & Boqiang Lin, 2021. "Effects of structural changes on the prediction of downside volatility in futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(7), pages 1124-1153, July.
    3. Ruey-Chyn Tsaur & I-Fei Chen & Yu-Shan Chan, 2017. "TFT-LCD industry performance analysis and evaluation using GRA and DEA models," International Journal of Production Research, Taylor & Francis Journals, vol. 55(15), pages 4378-4391, August.
    4. Li, Yongjun & Wang, Lizheng & Li, Feng, 2021. "A data-driven prediction approach for sports team performance and its application to National Basketball Association," Omega, Elsevier, vol. 98(C).
    5. P. Beraldi & M. E. Bruni, 2020. "Efficiency evaluation under uncertainty: a stochastic DEA approach," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 43(2), pages 519-538, December.
    6. Lin, Winston T. & Chen, Yueh H. & Hung, TingShu, 2019. "A partial adjustment valuation approach with stochastic and dynamic speeds of partial adjustment to measuring and evaluating the business value of information technology," European Journal of Operational Research, Elsevier, vol. 272(2), pages 766-779.
    7. Bryant, Benjamin P. & Borsuk, Mark E. & Hamel, Perrine & Oleson, Kirsten L.L. & Schulp, C.J.E. & Willcock, Simon, 2018. "Transparent and feasible uncertainty assessment adds value to applied ecosystem services modeling," Ecosystem Services, Elsevier, vol. 33(PB), pages 103-109.

    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. Chang, Tsung-Sheng & Tone, Kaoru & Wu, Chen-Hui, 2021. "Nested dynamic network data envelopment analysis models with infinitely many decision making units for portfolio evaluation," European Journal of Operational Research, Elsevier, vol. 291(2), pages 766-781.
    2. Mohammad Nourani & Qian Long Kweh & Evelyn Shyamala Devadason & V.G.R. Chandran, 2020. "A decomposition analysis of managerial efficiency for the insurance companies: A data envelopment analysis approach," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(6), pages 885-901, September.
    3. Gómez-Calvet, Roberto & Conesa, David & Gómez-Calvet, Ana Rosa & Tortosa-Ausina, Emili, 2014. "Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures?," Applied Energy, Elsevier, vol. 132(C), pages 137-154.
    4. Kaoru Tone, 2015. "SBM variations revisited," GRIPS Discussion Papers 15-05, National Graduate Institute for Policy Studies.
    5. Mohammad Nourani & VGR Chandran & Qian Long Kweh & Wen-Min Lu, 2018. "Measuring Human, Physical and Structural Capital Efficiency Performance of Insurance Companies," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 137(1), pages 281-315, May.
    6. Wang, Wei & Huang, Jun & Wang, Haibo & Alidaee, Bahram, 2022. "Internal and external analysis of community banks' performance," International Review of Financial Analysis, Elsevier, vol. 84(C).
    7. Necmi Kemal Avkiran, 2017. "An illustration of multiple-stakeholder perspective using a survey across Australia, China and Japan," Annals of Operations Research, Springer, vol. 248(1), pages 93-121, January.
    8. Patricija Bajec & Danijela Tuljak-Suban & Eva Zalokar, 2021. "A Distance-Based AHP-DEA Super-Efficiency Approach for Selecting an Electric Bike Sharing System Provider: One Step Closer to Sustainability and a Win–Win Effect for All Target Groups," Sustainability, MDPI, vol. 13(2), pages 1-24, January.
    9. Alperovych, Yan & Hübner, Georges & Lobet, Fabrice, 2015. "How does governmental versus private venture capital backing affect a firm's efficiency? Evidence from Belgium," Journal of Business Venturing, Elsevier, vol. 30(4), pages 508-525.
    10. Mario Martín-Gamboa & Diego Iribarren, 2016. "Dynamic Ecocentric Assessment Combining Emergy and Data Envelopment Analysis: Application to Wind Farms," Resources, MDPI, vol. 5(1), pages 1-11, January.
    11. Xiang Ji & Jiasen Sun & Qunwei Wang & Qianqian Yuan, 2019. "Revealing Energy Over-Consumption and Pollutant Over-Emission Behind GDP: A New Multi-criteria Sustainable Measure," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1391-1421, December.
    12. 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.
    13. Iveta Palečková, 2018. "Differences in Efficiency between Banks in Financial Conglomerates and other Banks in the Banking Sectors in Visegrad Countries," Working Papers 0052, Silesian University, School of Business Administration.
    14. Wanke, Peter & Barros, Carlos P. & Faria, João R., 2015. "Financial distress drivers in Brazilian banks: A dynamic slacks approach," European Journal of Operational Research, Elsevier, vol. 240(1), pages 258-268.
    15. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499.
    16. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2021. "Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    17. Yongqi Feng & Haolin Zhang & Yung-ho Chiu & Tzu-Han Chang, 2021. "Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3091-3129, April.
    18. Yu-Chuan Chen & Yung-Ho Chiu & Tzu-Han Chang & Tai-Yu Lin, 2023. "Sustainable Development, Government Efficiency, and People’s Happiness," Journal of Happiness Studies, Springer, vol. 24(4), pages 1549-1578, April.
    19. Danijela Tuljak-Suban & Patricija Bajec, 2022. "A Hybrid DEA Approach for the Upgrade of an Existing Bike-Sharing System with Electric Bikes," Energies, MDPI, vol. 15(21), pages 1-23, October.
    20. Chen, Kuan-Chen & Lin, Sun-Yuan & Yu, Ming-Miin, 2022. "Exploring the efficiency of hospital and pharmacy utilizations in Taiwan: An application of dynamic network data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).

    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:eee:ejores:v:254:y:2016:i:2:p:532-549. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.