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2025 | 35 | 1 | 108-139

Article title

A generalized composition approach in Network Data Envelopment Analysis for complex structures: An application of higher education institutions in Poland

Content

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EN

Abstracts

EN
We present in this paper the pitfalls of the most established approach in network data envelopment analysis for units with a parallel internal structure. We show that these pitfalls are the cause of deficiencies of prevalent models employed for general series structures. To overcome these issues, we build a general composition approach that can be applied to units with any type of structure. Our approach relies on multi-objective programming and, unlike existing methods in the literature, we identify the divisional efficiency scores in a min-max and max-min sense simultaneously. This allows us to identify unique and unbiased efficiency scores that are not affected by the different magnitude of scores that the divisions can attain. Comparisons with other approaches, under various structures and assumptions, highlight the advantages of the proposed approach. We further employ this new approach to evaluate the teaching and research efficiency of the top 19 public higher education institutions in Poland with data drawn from the period 2020–2021. The proposed assessment framework departs from the employment of standard metrics such as number of publications and journal rankings, commonly used to evaluate the quantity and quality of research outcomes, and relies on other proxies, such as field-weighted citation impact factor and volume of research grants, that may provide more reliable results.

Year

Volume

35

Issue

1

Pages

108-139

Physical description

Contributors

  • Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, Wrocław, Poland
  • Department of Informatics, University of Piraeus, Piraeus, Greece
  • Big-xyt Sp. z o.o., Wrocław, Poland

References

  • Amirteimoori, A., and Masrouri, S. DEA-based competition strategy in the presence of undesirable products: An application to paper mills. Operations Research and Decisions 31, 2 (2021), 5–21.
  • Athanassopoulos, A. D., and Shale, E. Assessing the comparative efficiency of higher education institutions in the UK by the means of data envelopment analysis. Education Economics 5, 2 (1997), 117–134.
  • Banker, R. D., Charnes, A., and Cooper, W. W. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science 30, 9 (1984), 1078–1092.
  • Beasley, J. E. Determining teaching and research efficiencies. Journal of the Operational Research Society 46, 4 (1995), 441–452.
  • Buchanan, J., and Gardiner, L. A comparison of two reference point methods in multiple objective mathematical programming. European Journal of Operational Research 149, 1 (2003), 17–34.
  • Charnes, A., and Cooper, W. W. Programming with linear fractional functionals. Naval Research Logistics Quarterly 9, 3–4 (1962), 181–186.
  • Charnes, A., Cooper, W. W., and Rhodes, E. Measuring the efficiency of decision making units. European Journal of Operational Research 2, 6 (1978), 429–444.
  • Chen, Y., Cook, W. D., Li, N., and Zhu, J. Additive efficiency decomposition in two-stage DEA. European Journal of Operational Research 196, 3 (2009), 1170–1176.
  • Cook, W. D., Zhu, J., Bi, G., and Yang, F. Network DEA: Additive efficiency decomposition. European Journal of Operational Research 207, 2 (2010), 1122–1129.
  • Despotis, D. K. fractional minmax goal programming: A unified approach to priority estimation and preference analysis in MCDM. Journal of the Operational Research Society 47, 8 (1996), 989–999.
  • Despotis, D. K., Koronakos, G., and Sotiros, D. A multi-objective programming approach to network DEA with an application to the assessment of the academic research activity. Procedia Computer Science 55 (2015), 370–379.
  • Despotis, D. K., Koronakos, G., and Sotiros, D. Composition versus decomposition in two-stage network DEA: a reverse approach. Journal of Productivity Analysis 45, 1 (2016), 71–87.
  • Despotis, D. K., Koronakos, G., and Sotiros, D. The “weak-link” approach to network DEA for two-stage processes. European Journal of Operational Research 254, 2 (2016), 481–492.
  • Despotis, D. K., Sotiros, D., and Koronakos, G. A network DEA approach for series multi-stage processes. Omega 61 (2016), 35–48.
  • Despotis, D. K., Sotiros, D., and Koronakos, G. Data envelopment analysis of two-stage processes: an alternative (non-conventional) approach. International Transactions in Operational Research 32, 1 (2025), 384–405.
  • Du, J., Zhu, J., Cook, W. D., and Huo, J. DEA models for parallel systems: Game-theoretic approaches. Asia-Pacific Journal of Operational Research 32, 2 (2015), 1550008.
  • Guo, C., Shureshjani, R. A., Foroughi, A. A., and Zhu, J. Decomposition weights and overall efficiency in two-stage additive network DEA. European Journal of Operational Research 257, 3 (2017), 896–906.
  • Halkos, G. E., Tzeremes, N. G., and Kourtzidis, S. A. A unified classification of two-stage DEA models. Surveys in Operations Research and Management Science 19, 1 (2014), 1–16.
  • Johnes, G., and Johnes, J. Measuring the research performance of UK economics departments: An application of data envelopment analysis. Oxford Economic Papers 45, 2 (1993), 332–347.
  • Johnes, J. Data envelopment analysis and its application to the measurement of efficiency in higher education. Economics of Education Review 25, 3 (2006), 273–288.
  • Kao, C. Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research 192, 3 (2009), 949–962.
  • Kao, C. Efficiency measurement for parallel production systems. European Journal of Operational Research 196, 3 (2009), 1107–1112.
  • Kao, C. Efficiency decomposition for parallel production systems. Journal of the Operational Research Society 63, 1 (2012), 64–71.
  • Kao, C. Efficiency decomposition for general multi-stage systems in data envelopment analysis. European Journal of Operational Research 232, 1 (2014), 117–124.
  • Kao, C. network data envelopment analysis: Foundations and extensions, vol. 240 of International Series in Operations Research & Management Science, Springer, 2017.
  • Kao, C., and Hwang, S.-N. Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research 185, 1 (2008), 418–429.
  • Karagiannis, G., and Kourtzidis, S. On modelling non-performing loans in bank efficiency analysis. International Journal of Finance & Economics (2024), 1–16 (advance online publication).
  • Koronakos, G. A taxonomy and review of the network data envelopment analysis literature. In Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems (Cham, 2019), G. A. Tsihrintzis, M. Virvou, E. Sakkopoulos and L. C. Jain, Eds., vol. 1 of Learning and Analytics in Intelligent Systems, Springer, 2019, pp. 255–311.
  • Koronakos, G., Chytilova, L., and Sotiros, D. Measuring the research performance of UK computer science departments via network DEA. In 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA) (Patras, Greece, 2019), IEEE, pp. 1–7.
  • Koronakos, G., Sotiros, D., and Despotis, D. K. Reformulation of network data envelopment analysis models using a common modelling framework. European Journal of Operational Research 278, 2 (2019), 472–480.
  • Koronakos, G., Sotiros, D., Despotis, D. K., and Kritikos, M. N. Fair efficiency decomposition in network DEA: A compromise programming approach. Socio-Economic Planning Sciences 79 (2022), 101100.
  • Kuchta, D., Despotis, D., Frączkowski, K., and Stanek, S. Application of data envelopment analysis to evaluation of IT project success. Operations Research and Decisions 29, 3 (2019), 17–36.
  • Lee, B. L., and Johnes, J. Using network DEA to inform policy: The case of the teaching quality of higher education in England. Higher Education Quarterly 76, 2 (2022), 399–421.
  • Lee, B. L., and Worthington, A. C. A network DEA quantity and quality-oriented production model: An application to Australian university research services. Omega 60 (2016), 26–33.
  • Liang, L., Cook, W. D., and Zhu, J. DEA models for two-stage processes: Game approach and efficiency decomposition. Naval Research Logistics 55, 7 (2008), 643–653.
  • Łukaszewska, K. (2015). Where does University Money Come from? Part. 1 - Algorithmic Grant (in Polish) (accessed on 17 February 2023).
  • Major, W. Data envelopment analysis as an instrument for measuring the efficiency of courts. Operations Research and Decisions 25, 4 (2015), 19–34.
  • Michali, M., Emrouznejad, A., Dehnokhalaji, A., and Clegg, B. Noise-pollution efficiency analysis of European railways: A network DEA model. Transportation Research Part D: Transport and Environment 98 (2021), 102980.
  • Nazarko, J., Komuda, M., Kuźmicz, K., Szubzda, E., and Urban, J. The DEA method in public sector institutions efficiency analysis on the basis of higher education institutions. Operations Research and Decisions 18, 4 (2008), 89–105.
  • Peyrache, A., and Silva, M. C. A. A Comment on Decomposition of Efficiency in Network Production Models. Working Paper Series No. WP07/2022, School of Economics, University of Queensland, 2022.
  • Sotiros, D., Koronakos, G., and Despotis, D. K. Dominance at the divisional efficiencies level in network DEA: The case of two-stage processes. Omega 85 (2019), 144–155.
  • Thanassoulis, E., Kortelainen, M., Johnes, G., and Johnes, J. Costs and efficiency of higher education institutions in England: A DEA analysis. Journal of the Operational Research Society 62, 7 (2011), 1282–1297.
  • Tone, K., and Tsutsui, M. Network DEA: A slacks-based measure approach. European Journal of Operational Research 197, 1 (2009), 243–252.
  • Tsolas, I. E. Firm credit risk evaluation: A series two-stage DEA modeling framework. Annals of Operations Research 233 (2015), 483–500.
  • Tsolas, I. E. Performance evaluation of electric trolley bus routes. A series two-stage DEA approach. Infrastructures 6, 3 (2021), 44.
  • Valizadeh, O., and Ghiyasi, M. Assessing telecommunication contractor firms using a hybrid DEA-BWM method. Operations Research and Decisions 33, 4 (2023), 189–200.
  • Wolters Kluver. Act of 20 July 2018. Law on higher education and science (in Polish) (accessed on 18 April 2023).
  • Yu, M.-M., and Lin, E. T. J. Efficiency and effectiveness in railway performance using a multi-activity network DEA model. Omega 36, 6 (2008), 1005–1017.

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.desklight-db02ba46-1faf-4fb7-94a5-eac75a99e7f8
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