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Using Data Envelopment Analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations: Design and implementation of a decision support system

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  • Samoilenko, Sergey
  • Osei-Bryson, Kweku-Muata

Abstract

The competitive nature of the business environment requires the productivity-driven organization to be aware of its relative level of effectiveness and efficiency vis-à-vis its competitors. This suggests the need, first, for an effective mechanism that allows for discovering appropriate productivity models for improving overall organizational performance, and, second for a feedback-type mechanism that allows for evaluating multiple productivity models in order to select the most suitable one. In this paper our focus is on organizations that consider the states of their internal (e.g., possibly exemplified by resource-based view) and external (e.g., possibly exemplified by positioning) organizational environment in the formulation of their strategies. We propose and test a DEA-centric Decision Support System (DSS) that aims to assess and manage the relative performance of such organizations.

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  • Samoilenko, Sergey & Osei-Bryson, Kweku-Muata, 2013. "Using Data Envelopment Analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations: Design and implementation of a decision support system," Omega, Elsevier, vol. 41(1), pages 131-142.
    • Handle: RePEc:eee:jomega:v:41:y:2013:i:1:p:131-142
    DOI: 10.1016/j.omega.2011.02.010
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    Cited by:

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    2. Hyeri Choi & Min Jae Park, 2019. "Evaluating the Efficiency of Governmental Excellence for Social Progress: Focusing on Low- and Lower-Middle-Income Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 111-130, January.
    3. Xiaoqing Chen & Xinwang Liu, 2023. "Comparing Malmquist and Hicks–Moorsteen productivity changes in China’s high-tech industries: exploring convexity implications," 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. 31(4), pages 1209-1237, December.
    4. Veiga, Gabriela Lobo & Pinheiro de Lima, Edson & Frega, José Roberto & Gouvea da Costa, Sérgio Eduardo, 2021. "A DEA-based approach to assess manufacturing performance through operations strategy lenses," International Journal of Production Economics, Elsevier, vol. 235(C).
    5. Tsionas, Mike G., 2023. "Clustering and meta-envelopment in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 304(2), pages 763-778.
    6. Kwon, He-Boong, 2017. "Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 159-170.
    7. Misiunas, Nicholas & Oztekin, Asil & Chen, Yao & Chandra, Kavitha, 2016. "DEANN: A healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status," Omega, Elsevier, vol. 58(C), pages 46-54.
    8. Hang Yin & Qing He & Tao Guo & Jianxin Zhu & Botao Yu, 2014. "Measurement Method and Empirical Research on the Sustainable Development Capability of a Regional Industrial System Based on Ecological Niche Theory in China," Sustainability, MDPI, vol. 6(12), pages 1-25, November.
    9. Saleem Shaik & Joseph Atwood, 2020. "A Comparative Study of Alternative Approaches to Estimate Productivity," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(4), pages 747-766, December.

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