IDEAS home Printed from https://ideas.repec.org/a/wly/mgtdec/v26y2005i8p535-538.html

The mystification of operational competitiveness rating analysis

Author

Listed:
  • Shouhong Wang

    (Department of Marketing|Business Information Systems, Charlton College of Business, University of Massachusetts Dartmouth, Dartmouth, MA 02747-2300, USA)

  • Hai Wang

    (Department of Finance and Management Science, Sobey School of Business, Saint Mary's University, Halifax, NS, Canada B3H 3C3)

Abstract

This note examines the fault of the operational competitiveness rating analysis (OCRA) method. The premise of the OCRA method requires that a single scalar measurement must be applied to inputs and outputs to calculate the performance ratings for production units. This property renders the OCRA method worthless, since simple comparisons of the aggregated inputs and outputs can generate accurate productive efficiency evaluation results for production units if the simple aggregation can be done. To avoid this problem, the OCRA method includes subjective weighting elements for input and output categories, so called calibration constants, into the performance rating computation. This approach of the OCRA method introduces much confusion for productive efficiency evaluation, and it violates the economics axiom of output|input maximization in its application context. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • Shouhong Wang & Hai Wang, 2005. "The mystification of operational competitiveness rating analysis," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 26(8), pages 535-538.
    • Handle: RePEc:wly:mgtdec:v:26:y:2005:i:8:p:535-538
    DOI: 10.1002/mde.1244
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1002/mde.1244
    File Function: Link to full text; subscription required
    Download Restriction: no
    File URL: https://libkey.io/10.1002/mde.1244?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
    ---><---

    References listed on IDEAS

    as
    1. Agrell, Per J. & Martin West, B., 2001. "A caveat on the measurement of productive efficiency," International Journal of Production Economics, Elsevier, vol. 69(1), pages 1-14, January.
    2. A. Charnes & W. W. Cooper & E. Rhodes, 1981. "Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through," Management Science, INFORMS, vol. 27(6), pages 668-697, June.
    3. Parkan, Celik & Wu, Ming-Lu, 1999. "Measurement of the performance of an investment bank using the operational competitiveness rating procedure," Omega, Elsevier, vol. 27(2), pages 201-217, April.
    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. Ömer Faruk Görçün & Sarfaraz Hashemkhani Zolfani & Mustafa Çanakçıoğlu, 2022. "Analysis of efficiency and performance of global retail supply chains using integrated fuzzy SWARA and fuzzy EATWOS methods," Operations Management Research, Springer, vol. 15(3), pages 1445-1469, December.
    2. Ender Coskun & Abdulvahap Ozcan, 2016. "Finansal Sikinti Surecinde Sirketlerin Etkinlik Duzeylerinin Belirlenmesi," EconWorld Working Papers 16001, WERI-World Economic Research Institute, revised Apr 2016.

    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. Fethi, Meryem Duygun & Pasiouras, Fotios, 2010. "Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey," European Journal of Operational Research, Elsevier, vol. 204(2), pages 189-198, July.
    2. Parkan, Celik, 2007. "Verifying OCRA's economic sense: Response to Agrell and West (2001)," International Journal of Production Economics, Elsevier, vol. 107(1), pages 274-278, May.
    3. Ender Coskun & Abdulvahap Ozcan, 2016. "Finansal Sikinti Surecinde Sirketlerin Etkinlik Duzeylerinin Belirlenmesi," EconWorld Working Papers 16001, WERI-World Economic Research Institute, revised Apr 2016.
    4. Wang, Shouhong, 2006. "Comments on operational competitiveness rating analysis (OCRA)," European Journal of Operational Research, Elsevier, vol. 169(1), pages 329-331, February.
    5. Hampf, Benjamin, 2017. "Rational inefficiency, adjustment costs and sequential technologies," European Journal of Operational Research, Elsevier, vol. 263(3), pages 1095-1108.
    6. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499.
    7. Büschken, Joachim, 2009. "When does data envelopment analysis outperform a naïve efficiency measurement model?," European Journal of Operational Research, Elsevier, vol. 192(2), pages 647-657, January.
    8. Ulucan, AydIn & BarIs AtIcI, KazIm, 2010. "Efficiency evaluations with context-dependent and measure-specific data envelopment approaches: An application in a World Bank supported project," Omega, Elsevier, vol. 38(1-2), pages 68-83, February.
    9. Andrew Worthington, 2001. "An Empirical Survey of Frontier Efficiency Measurement Techniques in Education," Education Economics, Taylor & Francis Journals, vol. 9(3), pages 245-268.
    10. Shuji Yao & Zhongwei Han & Dan Luo, 2010. "Performance of the Chinese Insurance Industry under Economic Reforms," Books, Edward Elgar Publishing, number 12788, June.
    11. Mergoni, Anna & Soncin, Mara & Agasisti, Tommaso, 2023. "The effect of ICT on schools’ efficiency: Empirical evidence on 23 European countries," Omega, Elsevier, vol. 119(C).
    12. Murinde, Victor & Zhao, Tianshu, 2009. "Bank competition, risk taking and productive efficiency: Evidence from Nigeria's banking reform experiments," Stirling Economics Discussion Papers 2009-23, University of Stirling, Division of Economics.
    13. Soteriou, Andreas C. & Zenios, Stavros A., 1999. "Using data envelopment analysis for costing bank products," European Journal of Operational Research, Elsevier, vol. 114(2), pages 234-248, April.
    14. Sergio Destefanis & Francesco Addesa & Giambattista Rossi, 2022. "The impact of COVID-19 on home advantage: a conditional order-m analysis of football clubs’ efficiency in the top-5 European leagues," Applied Economics, Taylor & Francis Journals, vol. 54(58), pages 6639-6655, December.
    15. Renata Machado de Andrade & Suhyung Lee & Paul Tae-Woo Lee & Oh Kyoung Kwon & Hye Min Chung, 2019. "Port Efficiency Incorporating Service Measurement Variables by the BiO-MCDEA: Brazilian Case," Sustainability, MDPI, vol. 11(16), pages 1-18, August.
    16. Avkiran, Necmi K. & Morita, Hiroshi, 2010. "Benchmarking firm performance from a multiple-stakeholder perspective with an application to Chinese banking," Omega, Elsevier, vol. 38(6), pages 501-508, December.
    17. Tengfei Huo & Hong Ren & Weiguang Cai & Wei Feng & Miaohan Tang & Nan Zhou, 2018. "The total-factor energy productivity growth of China’s construction industry: evidence from the regional level," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(3), pages 1593-1616, July.
    18. Hao Pan & Guo-liang Yang & Xiao-lei Chen & Yuan-yu Lou & Teng Wang & Zhong-cheng Guan, 2024. "Regret cross-efficiency evaluation using attitudinal entropy approach," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-17, December.
    19. Avkiran, Necmi K., 2001. "Investigating technical and scale efficiencies of Australian Universities through data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 35(1), pages 57-80, March.
    20. Verschelde, Marijn & Rogge, Nicky, 2012. "An environment-adjusted evaluation of citizen satisfaction with local police effectiveness: Evidence from a conditional Data Envelopment Analysis approach," European Journal of Operational Research, Elsevier, vol. 223(1), pages 214-225.

    More about this item

    Statistics

    Access and download statistics

    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:wly:mgtdec:v:26:y:2005:i:8:p:535-538. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/7976 .

    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.