IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i6p3610-d774704.html
   My bibliography  Save this article

CI-DEA: A Way to Improve the Discriminatory Power of DEA—Using the Example of the Efficiency Assessment of the Digitalization in the Life of the Generation 50+

Author

Listed:
  • Anna Łozowicka

    (Department of Econometrics, Poznań University of Economics and Business, 61-875 Poznań, Poland)

  • Bartłomiej Lach

    (Analyx ® , 61-887 Poznań, Poland)

Abstract

Data envelopment analysis (DEA) is a popular and universal method for examining the efficiency with which decision-making units (DMUs) transform multiple inputs into multiple outputs. However, DEA has its limitations, one of them being its decreasing discriminatory power when the number of analyzed DMUs is insufficient or when there are too many variables (inputs/outputs) describing them. When resigning from any of the variables is impossible or undesired, or when the number of units cannot be increased, CI-DEA, a method proposed in this article, proves to be helpful. It consists of replacing the inputs and/or outputs of the studied DMUs with a smaller number of composite indicators. The aggregation of variables is not based on subjective decisions of the analyst, but depends solely on correlations that exist among variables. The construction of the CI-DEA model makes the interpretation of the results unambiguous and easy. The reliability of the results obtained with CI-DEA have been confirmed by extensive simulation studies performed under conditions of predetermined real-efficiency of DMUs. The usefulness of CI-DEA on real data has been demonstrated on the example of the efficiency assessment of the digitalization in the life of the Generation 50+ in 32 European countries.

Suggested Citation

  • Anna Łozowicka & Bartłomiej Lach, 2022. "CI-DEA: A Way to Improve the Discriminatory Power of DEA—Using the Example of the Efficiency Assessment of the Digitalization in the Life of the Generation 50+," Sustainability, MDPI, vol. 14(6), pages 1-22, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3610-:d:774704
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/6/3610/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/6/3610/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pilar Murias & Fidel Martinez & Carlos Miguel, 2006. "An Economic Wellbeing Index for the Spanish Provinces: A Data Envelopment Analysis Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 77(3), pages 395-417, July.
    2. Van Puyenbroeck, Tom & Rogge, Nicky, 2020. "Comparing regional human development using global frontier difference indices," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    3. N Adler & B Golany, 2002. "Including principal component weights to improve discrimination in data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(9), pages 985-991, September.
    4. Zhou, Peng & Poh, Kim Leng & Ang, Beng Wah, 2007. "A non-radial DEA approach to measuring environmental performance," European Journal of Operational Research, Elsevier, vol. 178(1), pages 1-9, April.
    5. P. Zhou & B. Ang & D. Zhou, 2010. "Weighting and Aggregation in Composite Indicator Construction: a Multiplicative Optimization Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 96(1), pages 169-181, March.
    6. Adler, Nicole & Golany, Boaz, 2001. "Evaluation of deregulated airline networks using data envelopment analysis combined with principal component analysis with an application to Western Europe," European Journal of Operational Research, Elsevier, vol. 132(2), pages 260-273, July.
    7. Lee, Chia-Yen & Cai, Jia-Ying, 2020. "LASSO variable selection in data envelopment analysis with small datasets," Omega, Elsevier, vol. 91(C).
    8. Victor Podinovski & Emmanuel Thanassoulis, 2007. "Improving discrimination in data envelopment analysis: some practical suggestions," Journal of Productivity Analysis, Springer, vol. 28(1), pages 117-126, October.
    9. Wagner, Janet M. & Shimshak, Daniel G., 2007. "Stepwise selection of variables in data envelopment analysis: Procedures and managerial perspectives," European Journal of Operational Research, Elsevier, vol. 180(1), pages 57-67, July.
    10. 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.
    11. Peyrache, Antonio & Rose, Christiern & Sicilia, Gabriela, 2020. "Variable selection in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 282(2), pages 644-659.
    12. Jenkins, Larry & Anderson, Murray, 2003. "A multivariate statistical approach to reducing the number of variables in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 147(1), pages 51-61, May.
    13. Adler, Nicole & Yazhemsky, Ekaterina, 2010. "Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction," European Journal of Operational Research, Elsevier, vol. 202(1), pages 273-284, April.
    14. United Nations UN, 2015. "Transforming our World: the 2030 Agenda for Sustainable Development," Working Papers id:7559, eSocialSciences.
    15. Noelia Somarriba & Bernardo Pena, 2009. "Synthetic Indicators of Quality of Life in Europe," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 94(1), pages 115-133, October.
    16. Jad Chaaban, 2009. "Measuring Youth Development: A Nonparametric Cross-Country ‘Youth Welfare Index’," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 93(2), pages 351-358, September.
    17. Dyson, R. G. & Allen, R. & Camanho, A. S. & Podinovski, V. V. & Sarrico, C. S. & Shale, E. A., 2001. "Pitfalls and protocols in DEA," European Journal of Operational Research, Elsevier, vol. 132(2), pages 245-259, July.
    18. 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.
    19. Jesús T. Pastor & JosÉ L. Ruiz & Inmaculada Sirvent, 2002. "A Statistical Test for Nested Radial Dea Models," Operations Research, INFORMS, vol. 50(4), pages 728-735, August.
    20. Yongjun Shen & Elke Hermans & Tom Brijs & Geert Wets, 2013. "Data Envelopment Analysis for Composite Indicators: A Multiple Layer Model," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 114(2), pages 739-756, November.
    21. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    22. Põldaru, Reet & Roots, Jüri, 2014. "A PCA–DEA approach to measure the quality of life in Estonian counties," Socio-Economic Planning Sciences, Elsevier, vol. 48(1), pages 65-73.
    23. Enzo Barberio Mariano & Diogo Ferraz & Simone Cristina Oliveira Gobbo, 2021. "The Human Development Index with Multiple Data Envelopment Analysis Approaches: A Comparative Evaluation Using Social Network Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 157(2), pages 443-500, September.
    24. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, June.
    25. Nataraja, Niranjan R. & Johnson, Andrew L., 2011. "Guidelines for using variable selection techniques in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 215(3), pages 662-669, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Toloo, Mehdi & Tone, Kaoru & Izadikhah, Mohammad, 2023. "Selecting slacks-based data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1302-1318.
    2. Jamal Ouenniche & Skarleth Carrales, 2018. "Assessing efficiency profiles of UK commercial banks: a DEA analysis with regression-based feedback," Annals of Operations Research, Springer, vol. 266(1), pages 551-587, July.
    3. Vincenzo Patrizii & Anna Pettini & Giuliano Resce, 2017. "The Cost of Well-Being," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(3), pages 985-1010, September.
    4. Imad Bou-Hamad & Abdel Latef Anouze & Ibrahim H. Osman, 2022. "A cognitive analytics management framework to select input and output variables for data envelopment analysis modeling of performance efficiency of banks using random forest and entropy of information," Annals of Operations Research, Springer, vol. 308(1), pages 63-92, January.
    5. Nataraja, Niranjan R. & Johnson, Andrew L., 2011. "Guidelines for using variable selection techniques in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 215(3), pages 662-669, December.
    6. Eskelinen, Juha, 2017. "Comparison of variable selection techniques for data envelopment analysis in a retail bank," European Journal of Operational Research, Elsevier, vol. 259(2), pages 778-788.
    7. Qiwei Xie & Yuanyuan Li & Lizheng Wang & Chao Liu, 2018. "Improving discrimination in data envelopment analysis without losing information based on Renyi’s entropy," 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. 26(4), pages 1053-1068, December.
    8. Villanueva-Cantillo, Jeyms & Munoz-Marquez, Manuel, 2021. "Methodology for calculating critical values of relevance measures in variable selection methods in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 290(2), pages 657-670.
    9. Charles, Vincent & Aparicio, Juan & Zhu, Joe, 2019. "The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 279(3), pages 929-940.
    10. Peyrache, Antonio & Rose, Christiern & Sicilia, Gabriela, 2020. "Variable selection in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 282(2), pages 644-659.
    11. Bojiang Yang & Youliang Zhang & Hongjun Zhang & Rui Zhang & Baoyu Xu, 2016. "Factor-specific Malmquist productivity index based on common weights DEA," Operational Research, Springer, vol. 16(1), pages 51-70, April.
    12. Esteve, Miriam & Aparicio, Juan & Rodriguez-Sala, Jesus J. & Zhu, Joe, 2023. "Random Forests and the measurement of super-efficiency in the context of Free Disposal Hull," European Journal of Operational Research, Elsevier, vol. 304(2), pages 729-744.
    13. Shih-Heng Yu, 2019. "Benchmarking and Performance Evaluation Towards the Sustainable Development of Regions in Taiwan: A Minimum Distance-Based Measure with Undesirable Outputs in Additive DEA," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(3), pages 1323-1348, August.
    14. Raul Moragues & Juan Aparicio & Miriam Esteve, 2023. "Ranking the Importance of Variables in a Nonparametric Frontier Analysis Using Unsupervised Machine Learning Techniques," Mathematics, MDPI, vol. 11(11), pages 1-24, June.
    15. Toloo, Mehdi & Hančlová, Jana, 2020. "Multi-valued measures in DEA in the presence of undesirable outputs," Omega, Elsevier, vol. 94(C).
    16. Yongjun Li & Xiao Shi & Min Yang & Liang Liang, 2017. "Variable selection in data envelopment analysis via Akaike’s information criteria," Annals of Operations Research, Springer, vol. 253(1), pages 453-476, June.
    17. Juan Aparicio & Magdalena Kapelko, 2019. "Enhancing the Measurement of Composite Indicators of Corporate Social Performance," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(2), pages 807-826, July.
    18. Adler, Nicole & Yazhemsky, Ekaterina, 2010. "Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction," European Journal of Operational Research, Elsevier, vol. 202(1), pages 273-284, April.
    19. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    20. Põldaru, Reet & Roots, Jüri, 2014. "A PCA–DEA approach to measure the quality of life in Estonian counties," Socio-Economic Planning Sciences, Elsevier, vol. 48(1), pages 65-73.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3610-:d:774704. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.