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

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  • Brissimis, Sophocles
  • Zervopoulos, Panagiotis

Abstract

Effectiveness involves more than simple efficiency, which is limited to the production process assessment of peer operational units. Effectiveness incorporates both endogenous and exogenous variables. It is a fundamental driver for the success of an operational unit within a competitive environment in which either the liquidity of money in the market and the customers are considered to be scarce sources, or the New Public Management (NPM) is citizen/customer and goal-oriented. Additionally, with respect to short-run production constraints, the resources available and controllable by the operational units, as well as the legal status, we go beyond the traditional effectiveness assessment techniques by developing a modified or “rational” Quality-driven – Efficiency-adjusted DEA (MQE-DEA) model. This particular model provides a feasible effectiveness attainment path for every disqualified unit in order to meet high-perceived quality and high-efficiency standards. The input-output mix restructuring targets estimated by the original QE-DEA model are provided on a step-by-step basis in order to have realistic managerial implications.

Suggested Citation

  • Brissimis, Sophocles & Zervopoulos, Panagiotis, 2011. "Developing a step-by-step effectiveness assessment model for customer-oriented service organizations," MPRA Paper 30765, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:30765
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    File URL: https://mpra.ub.uni-muenchen.de/30765/1/MPRA_paper_30765.pdf
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    References listed on IDEAS

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    1. Seiford, Lawrence M. & Zhu, Joe, 2003. "Context-dependent data envelopment analysis--Measuring attractiveness and progress," Omega, Elsevier, vol. 31(5), pages 397-408, October.
    2. 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.
    3. 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.
    4. 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.
    5. H. Sherman & Joe Zhu, 2006. "Benchmarking with quality-adjusted DEA (Q-DEA) to seek lower-cost high-quality service: Evidence from a U.S.bank 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

    Keywords

    Effectiveness; Efficiency; Perceived Quality; 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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