IDEAS home Printed from https://ideas.repec.org/a/wly/mgtdec/v26y2005i8p535-538.html
   My bibliography  Save this article

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. 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.
    3. 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.
    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. Hampf, Benjamin, 2017. "Rational inefficiency, adjustment costs and sequential technologies," European Journal of Operational Research, Elsevier, vol. 263(3), pages 1095-1108.
    4. Ender Coskun & Abdulvahap Ozcan, 2016. "Finansal Sikinti Surecinde Sirketlerin Etkinlik Duzeylerinin Belirlenmesi," EconWorld Working Papers 16001, WERI-World Economic Research Institute, revised Apr 2016.
    5. Wang, Shouhong, 2006. "Comments on operational competitiveness rating analysis (OCRA)," European Journal of Operational Research, Elsevier, vol. 169(1), pages 329-331, February.
    6. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499, January.
    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. 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.
    9. 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.
    10. 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.
    11. Ruiz, Jose L. & Sirvent, Inmaculada, 2001. "Techniques for the assessment of influence in DEA," European Journal of Operational Research, Elsevier, vol. 132(2), pages 390-399, July.
    12. Peyrache, Antonio & Rose, Christiern & Sicilia, Gabriela, 2020. "Variable selection in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 282(2), pages 644-659.
    13. Zanakis, Stelios H. & Mandakovic, Tomislav & Gupta, Sushil K. & Sahay, Sundeep & Hong, Sungwan, 1995. "A review of program evaluation and fund allocation methods within the service and government sectors," Socio-Economic Planning Sciences, Elsevier, vol. 29(1), pages 59-79, March.
    14. Dios-Palomares, Rafaela & José-Diz, David Alcaide & Jurado, Manuel & Guijarro, Angel Prieto & Martinez-Paz, J. M. & Zúniga-González, Carlos Alberto, 2015. "Aspectos medioambientales en los análisis de eficiencia," Revista Iberoamericana de Bioeconomía y Cambio Climàtico, National Autonomous University of Nicaragua, Leon, vol. 1(1), pages 1-7, July.
    15. Miki Tsutsui & Kaoru Tone, 2007. "Separation of uncontrollable factors and time shift effects from DEA scores," GRIPS Discussion Papers 07-09, National Graduate Institute for Policy Studies.
    16. Abdelaati Daouia & Léopold Simar & Paul W. Wilson, 2017. "Measuring firm performance using nonparametric quantile-type distances," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 156-181, March.
    17. Joaquín Maudos Villarroya & José Manuel Pastor Monsálvez & Lorenzo Serrano Martínez, 1998. "- Eficiency And Productivity Specialization: The Spanish Regions," Working Papers. Serie EC 1998-26, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    18. Glover, Fred & Sueyoshi, Toshiyuki, 2009. "Contributions of Professor William W. Cooper in Operations Research and Management Science," European Journal of Operational Research, Elsevier, vol. 197(1), pages 1-16, August.
    19. Gong, Linguo & Sun, Bruce, 1995. "Efficiency measurement of production operations under uncertainty," International Journal of Production Economics, Elsevier, vol. 39(1-2), pages 55-66, April.
    20. Vittadini, Giorgio & Sturaro, Caterina & Folloni, Giuseppe, 2022. "Non-Cognitive Skills and Cognitive Skills to measure school efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).

    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.