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On the Use of Value Efficiency Analysis and Some Further Developments

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The purpose of this paper is to discuss the use of Value Efficiency Analysis (VEA) in efficiency evaluation when preference information is taken into account. Value efficiency analysis is an approach, which applies the ideas developed for Multiple Objective Linear Programming (MOLP) to Data Envelopment Analysis (DEA). Preference information is given through the desirable structure of input- and output-values. The same values can be used for all units under evaluation or the values can be specific for each unit. A decision-maker can specify the input- and output-values subjectively without any support or (s)he can use a multiple criteria support system to assist him/her to find those values on the efficient frontier. The underlying assumption is that the most preferred values maximize the decision-maker's implicitly known value function in a production possibility set or a subset. The purpose of value efficiency analysis is to estimate a need to increase outputs and/or decrease inputs for reaching the indifference contour of the value function at the optimum. In this paper, we briefly review the main ideas in value efficiency analysis and discuss practical aspects related to the use of value efficiency analysis. We also consider some extensions. Copyright Kluwer Academic Publishers 2002

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  • Pekka Korhonen & Margareta Soismaa & Aapo Siljamäki, 2002. "On the Use of Value Efficiency Analysis and Some Further Developments," Journal of Productivity Analysis, Springer, vol. 17(1), pages 49-64, January.
    • Handle: RePEc:kap:jproda:v:17:y:2002:i:1:p:49-64
    DOI: 10.1023/A:1013532219759
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    1. Tavana, Madjid & Ebrahimnejad, Ali & Santos-Arteaga, Francisco J. & Mansourzadeh, Seyed Mehdi & Matin, Reza Kazemi, 2018. "A hybrid DEA-MOLP model for public school assessment and closure decision in the City of Philadelphia," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 70-89.
    2. Soushi Suzuki & Peter Nijkamp & Piet Rietveld, 2013. "Preference Elicitation in Generalized Data Envelopment Analysis - In Search of a New Energy Balance in Japan," Tinbergen Institute Discussion Papers 13-162/VIII, Tinbergen Institute.
    3. Soushi Suzuki & Peter Nijkamp, 2008. "A Generalized Goals-achievement Model in Data Envelopment Analysis," Tinbergen Institute Discussion Papers 08-110/3, Tinbergen Institute.
    4. Panagiotis Ravanos & Giannis Karagiannis, 2022. "In search for the most preferred solution in value efficiency analysis," Journal of Productivity Analysis, Springer, vol. 58(2), pages 203-220, December.
    5. Soushi Suzuki, 2014. "Improvement restriction data envelopment analysis for new energy in Japan," ERSA conference papers ersa14p223, European Regional Science Association.
    6. Peter Bogetoft & Kurt Nielsen, 2005. "Internet Based Benchmarking," Group Decision and Negotiation, Springer, vol. 14(3), pages 195-215, May.
    7. Yang, Jian-Bo & Wong, Brandon Y.H. & Xu, Dong-Ling & Stewart, Theodor J., 2009. "Integrating DEA-oriented performance assessment and target setting using interactive MOLP methods," European Journal of Operational Research, Elsevier, vol. 195(1), pages 205-222, May.
    8. Soushi Suzuki & Peter Nijkamp & Piet Rietveld, 2013. "A Target-Oriented Data Envelopment Analysis for Energy-Environment Efficiency Improvement in Japan," Tinbergen Institute Discussion Papers 13-137/VIII, Tinbergen Institute.
    9. Inha Oh & Jeong-Dong Lee & Seogwon Hwang & Almas Heshmati, 2010. "Analysis of product efficiency in the Korean automobile market from a consumer’s perspective," Empirical Economics, Springer, vol. 38(1), pages 119-137, February.
    10. Førsund, Finn R., 2017. "Measuring effectiveness of production in the public sector," Omega, Elsevier, vol. 73(C), pages 93-103.
    11. Suzuki, Soushi & Nijkamp, Peter, 2016. "An evaluation of energy-environment-economic efficiency for EU, APEC and ASEAN countries: Design of a Target-Oriented DFM model with fixed factors in Data Envelopment Analysis," Energy Policy, Elsevier, vol. 88(C), pages 100-112.
    12. Panagiotis Ravanos & Giannis Karagiannis, 2021. "Using VEA to assess effectiveness in the development of human capabilities," Economic Change and Restructuring, Springer, vol. 54(1), pages 75-99, February.
    13. Soushi Suzuki & Peter Nijkamp & Piet Rietveld, 2012. "A preference allocation-DFM model in Data Envelopment Analysis -An application to Energy-Environment-Economic efficiency in Japan-," ERSA conference papers ersa12p332, European Regional Science Association.
    14. Panagiotis Ravanos & Giannis Karagiannis, 2022. "In search for the Most Preferred Solution in Value Efficiency Analysis," Discussion Paper Series 2022_05, Department of Economics, University of Macedonia, revised Jul 2022.
    15. Peter Nijkamp & Soushi Suzuki, 2009. "A Generalized Goals-achievement Model in Data Envelopment Analysis: an Application to Efficiency Improvement in Local Government Finance in Japan," Spatial Economic Analysis, Taylor & Francis Journals, vol. 4(3), pages 249-274.
    16. Ahti Salo & Antti Punkka, 2011. "Ranking Intervals and Dominance Relations for Ratio-Based Efficiency Analysis," Management Science, INFORMS, vol. 57(1), pages 200-214, January.
    17. Giannis Karagiannis & Panagiotis Ravanos, 2023. "On Value Efficiency Analysis and Cone-Ratio Data Envelopment Analysis models," Discussion Paper Series 2023_03, Department of Economics, University of Macedonia, revised Mar 2023.
    18. Panagiotis Ravanos & Giannis Karagiannis, 2022. "On VEA, Production Trade-offs and Weight Restrictions," Discussion Paper Series 2022_04, Department of Economics, University of Macedonia, revised Jun 2022.
    19. Panagiotis Ravanos & Giannis Karagiannis, 2021. "A VEA Benefit-of-the-Doubt Model for the HDI," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(1), pages 27-46, May.
    20. Soushi Suzuki & Peter Nijkamp, 2021. "High urban population density as a facilitator of energy–environment–economy performance–development of an autoconfiguration target model in data envelopment analysis," Asia-Pacific Journal of Regional Science, Springer, vol. 5(1), pages 261-287, February.
    21. Karima Kourtit & Peter Nijkamp & Soushi Suzuki, 2023. "Quantitative performance assessment of Asian stellar cities by a DEA cascade system: a capability interpretation," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 70(1), pages 259-286, February.
    22. Suzuki, Soushi & Nijkamp, Peter & Rietveld, Piet & Pels, Eric, 2010. "A distance friction minimization approach in data envelopment analysis: A comparative study on airport efficiency," European Journal of Operational Research, Elsevier, vol. 207(2), pages 1104-1115, December.

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