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A multicriteria blockmodel for performance assessment

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  • Jessop, Alan

Abstract

This paper considers a network-based view of performance differences between organisations. Each organisation is considered in terms both of its size--the level of performance--and its shape--the means whereby the performance is achieved. Since many organisations prefer to monitor their performance via a number of performance measures rather than a single efficiency this is the approach adopted. A weighted additive multicriteria model is used to give an overall measure. The weights, inevitably imprecise, are modelled probabilistically resulting in correspondingly probabilistic estimates of the difference between pairs of organisations. Significant differences are identified and a binary network of relations built showing pairs for which performances are not significantly different from each other. Similarly, correlations between sets of measures leads to a second network showing pairs with similar shape. Blockmodels are built to show the extent to which differentiation between organisations can be found. This structural description is used to examine the changes in performance differences over time. The data used for illustration describe the performance of some airports over a nine year period.

Suggested Citation

  • Jessop, Alan, 2009. "A multicriteria blockmodel for performance assessment," Omega, Elsevier, vol. 37(1), pages 204-214, February.
  • Handle: RePEc:eee:jomega:v:37:y:2009:i:1:p:204-214
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    References listed on IDEAS

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    1. Jessop, Alan, 2003. "Blockmodels with maximum concentration," European Journal of Operational Research, Elsevier, vol. 148(1), pages 56-64, July.
    2. Thanassoulis, Emmanuel, 2000. "DEA and its use in the regulation of water companies," European Journal of Operational Research, Elsevier, vol. 127(1), pages 1-13, November.
    3. Oum, Tae Hoon & Yu, Chunyan, 2004. "Measuring airports' operating efficiency: a summary of the 2003 ATRS global airport benchmarking report," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 40(6), pages 515-532, November.
    4. Yoshida, Yuichiro & Fujimoto, Hiroyoshi, 2004. "Japanese-airport benchmarking with the DEA and endogenous-weight TFP methods: testing the criticism of overinvestment in Japanese regional airports," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 40(6), pages 533-546, November.
    5. Thanassoulis, E. & Boussofiane, A. & Dyson, R. G., 1996. "A comparison of data envelopment analysis and ratio analysis as tools for performance assessment," Omega, Elsevier, vol. 24(3), pages 229-244, June.
    6. Oum, Tae Hoon & Yu, Chunyan, 2004. "Airport Performance: A Summary of the 2003 ATRS Global Airport Benchmarking Report," 45th Annual Transportation Research Forum, Evanston, Illinois, March 21-23, 2004 208226, Transportation Research Forum.
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    Cited by:

    1. Sakouvogui Kekoura & Shaik Saleem & Addey Kwame Asiam, 2020. "Cluster-Adjusted DEA Efficiency in the presence of Heterogeneity: An Application to Banking Sector," Open Economics, De Gruyter, vol. 3(1), pages 50-69, January.
    2. Alan Jessop, 2010. "An optimising approach to alternative clustering schemes," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 18(3), pages 293-309, September.
    3. Bezerra, George C.L. & Gomes, Carlos F., 2018. "Performance measurement practices in airports: Multidimensionality and utilization patterns," Journal of Air Transport Management, Elsevier, vol. 70(C), pages 113-125.

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