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Developing a step-by-step effectiveness assessment model for customer-oriented service organizations


  • Brissimis, Sophocles N.
  • Zervopoulos, Panagiotis D.


Effectiveness involves more than simple efficiency, which is limited to the production process assessment of peer operational units. Effectiveness incorporates variables that are both controllable (i.e. efficiency) and non-controllable (i.e. perceived quality) by the operational units. It is a fundamental driver for the success of either a for-profit or a non-for-profit unit in a competitive environment that is customer/citizen- and goal-oriented. Additionally, with respect to the short-run production constraints, i.e. the resources available and controllable by the operational units, and the legal status, we go beyond the traditional effectiveness assessment techniques by developing a Modified or “rational” Quality-driven-Efficiency-adjusted Data Envelopment Analysis (MQE-DEA) model. This particular model provides in the short run a feasible effectiveness attainment path for every disqualified unit in order to meet high-perceived quality and high-efficiency standards. By applying the MQE-DEA model a new production equilibrium is determined, which is different from the equilibrium suggested by the mainstream microeconomic theory, in that it takes into account not only the need for operational efficiency but also the customer-driven market dynamics.

Suggested Citation

  • Brissimis, Sophocles N. & Zervopoulos, Panagiotis D., 2012. "Developing a step-by-step effectiveness assessment model for customer-oriented service organizations," European Journal of Operational Research, Elsevier, vol. 223(1), pages 226-233.
  • Handle: RePEc:eee:ejores:v:223:y:2012:i:1:p:226-233 DOI: 10.1016/j.ejor.2012.06.003

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    References listed on IDEAS

    1. Perelman, Sergio & Santín, Daniel, 2009. "How to generate regularly behaved production data? A Monte Carlo experimentation on DEA scale efficiency measurement," European Journal of Operational Research, Elsevier, vol. 199(1), pages 303-310, November.
    2. Seiford, Lawrence M. & Zhu, Joe, 2003. "Context-dependent data envelopment analysis--Measuring attractiveness and progress," Omega, Elsevier, vol. 31(5), pages 397-408, October.
    3. 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.
    4. 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.
    5. H. Sherman & Joe Zhu, 2006. "Benchmarking with quality-adjusted DEA (Q-DEA) to seek lower-cost high-quality service: Evidence from a application," Annals of Operations Research, Springer, vol. 145(1), pages 301-319, July.
    6. Athanassopoulos, Antreas D., 1997. "Service quality and operating efficiency synergies for management control in the provision of financial services: Evidence from Greek bank branches," European Journal of Operational Research, Elsevier, vol. 98(2), pages 300-313, April.
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    Cited by:

    1. Fang, Lei, 2015. "Centralized resource allocation based on efficiency analysis for step-by-step improvement paths," Omega, Elsevier, vol. 51(C), pages 24-28.

    More about this item


    OR in service industries; Efficiency; Perceived quality; Production equilibrium; Data Envelopment Analysis (DEA); Context-dependent DEA;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis


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