Vol. 11 No. 3 (2025)
September

Articles

Analysis of the circular management model of information and communication technology resources of large enterprises in the European Union

Published 2025-09-30

  • Małgorzata Sztorc
    • ,
  • Konstantins Savenkovs

Małgorzata Sztorc
Kielce University of Technology

Konstantins Savenkovs
Riga Aeronautical Institute

Abstract

Objective: The aim of this article is to determine the level of differentiation of large enterprises operating in the European Union in terms of the sustainable use of information and communications technology tools and to reduce the dimension of variables describing the circular model of managing information and communications technology solutions.

Research Design & Methods: We used quantitative analysis, considering secondary data from the Eurostat database for 2022 from the information and communications technology (ICT) and the environment by the size class of the enterprise section. We conducted a pilot study using data from large enterprises from 27 European Union countries. We analysed data using the diagnostic-descriptive method, principal component analysis, MOORA method, and linearly ordered object grouping.

Findings: The research results indicate that large enterprises represent different levels of circularity in the use of green IT/ICT related to the selection, use, and disposal of devices. Most entities operating in 18 European Union countries achieve an average level of circularity of ICT devices. Thus far, they have not included the reuse of ICT devices in the procedure consistent with the 3R circularity principle. The process of selecting, recovering, and recycling ICT equipment is carried out unevenly and in stages. On the other hand, the indicator of pro-environmental involvement displays low intensity, and in such a situation, the surveyed entities did not achieve the strategic goals assumed by the European Union in the field of circular economy regarding the selection and use of ICT equipment.

Implications & Recommendations: The research results enable managers to develop circular business models by reducing the consumption of raw materials, waste, greenhouse gas emissions, and energy. They support strategic decisions on the transition from a linear model of ICT equipment management to a circular model. They also support European Union policymakers in developing legal regulations aimed at closing material and energy loops. Moreover, they provide guidance on the allocation of financial support to improve the level of circularity of large enterprises.

Contribution & Value Added: The article makes a significant contribution to the development of the circular economy theory by developing an original indicator of pro-environmental involvement in the process of selecting ICT equipment and conducting a comprehensive analysis of circularity in the management of ICT devices in large enterprises of the European Union. The conducted research reveals significant differences in the implementation of the principles of the circular economy in the countries studied, constituting a starting point for further actions to improve efficiency and transfer best practices in this area.

Keywords

circular economy, sustainable development, information and communication technologies, green IT/ICT, MOORA method

(PDF) Save

Author Biography

Małgorzata Sztorc

Ph.D. in Economics in the discipline of Management Sciences (2014, Krakow University of Economics, Poland). Assistant Professor at the Kielce University of Technology (Poland). Her research interests include modern methods and techniques of enterprise management, enterprise strategies, shaping the competitiveness of enterprises, and the functioning of global corporations and network organizations.

Konstantins Savenkovs

PhD in Economics (2019, Riga Aeronautical Institute, Latvia). Vice-Rector of Studies and Program Director of Transport Service of Riga Aeronautical Institute. Author of numerous scientific articles and participant in various international projects within the Horizon project. His research interests include coaching, leadership, business training, marketing, management, logistics, and regional economy.

References

  1. Agyapong, D., Agyapong, G., & Frimpong, S.E. (2024). Precursors of circular economy practices. Cleaner Environmental Systems, 12, 100163. https://doi.org/10.1016/j.cesys.2024.100163
  2. Akkaya, G., Turanoğlu, B., & Öztaş, S. (2015). An integrated fuzzy AHP and fuzzy MOORA approach to the problem of industrial engineering sector choosing. Expert Systems with Applications, 42(24), 9565-9573. https://doi.org/10.1016/j.eswa.2015.07.061
  3. Akmaludin, A., Sihombing, E.G., Dewi, L.S., Rinawati, R., & Arisawati, E. (2021). The MOORA method for selecting software App: Price-quality ratio approach. Sinkron: Jurnal Dan Penelitian Teknik Informatika, 5(2), 135-148. https://doi.org/10.33395/sinkron.v5i2.10789
  4. Anthony, B., Majid, M.A., & Romli, A. (2020). A generic study on Green IT/IS practice development in collaborative enterprise: Insights from a developing country. Journal of Engineering and Technology Management, 55(5), 101555. https://doi.org/10.1016/j.jengtecman.2020.101555
  5. Anthony Jr, B. (2019). Green information system integration for environmental performance in organizations: An extension of belief–action–outcome framework and natural resource-based view theory. Benchmarking: An International Journal, 26(3), 1033-1062. https://doi.org/10.1108/BIJ-05-2018-0142
  6. Appiah-Otoo, I., Atstaja, D., Volkova, T., Grasis, J., & Ozolina-Ozola, I. (2023). The typology of 60R circular economy principles and strategic orientation of their application in business. Journal of Cleaner Production, 409, 137189. https://doi.org/10.1016/j.jclepro.2023.137189
  7. Arabsheybani, A., Paydar, M.M., & Safaei, A.S. (2018). An integrated fuzzy MOORA method and FMEA technique for sustainable supplier selection considering quantity discounts and supplier’s risk. Journal of Cleaner Production, 190, 577-591. https://doi.org/10.1016/j.jclepro.2018.04.167
  8. Arbeláez-Rendón, M., Giraldo, D.P., & Lotero, L. (2023). Influence of digital divide in the entrepreneurial motor of a digital economy: A system dynamics approach. Journal of Open Innovation: Technology, Market, and Complexity, 9(2), 1-12. https://doi.org/10.1016/j.joitmc.2023.100046
  9. Arshad, M.W., Sintaro, S., Rahmanto, Y., Wantoro, A., & Setiawansyah, A. (2024). Optimization of Alternative Assessment with Modified MOORA Method: Case Study of Contract Employee Selection. KLIK: Kajian Ilmiah Informatika Dan Komputer, 4(6). https://doi.org/10.30865/klik.v4i6.1891
  10. Asadi, S., Hussin, A.R.Ch., & Dahlan, H.M. (2017). Organizational research in the field of Green IT: A systematic literature review from 2007 to 2016. Telematics and Informatics, 34(7), 1191-1249. https://doi.org/10.1016/j.tele.2017.05.009
  11. Awewomom, J., Dzeble, F., Takyi, Y.D., Ashie, W.B., Ettey, E.N.Y.O., Afua, P.E., Sackey, L.N.A., Opoku, F., & Akoto, O. (2024). Addressing global environmental pollution using environmental control techniques: A focus on environmental policy and preventive environmental management. Discover Environment, 2(1), 8. https://doi.org/10.1007/s44274-024-00033-5
  12. Baran, B. (2019). The Circular Economy in EU Policy as a Response to Contemporary Ecological Challenges. Gospodarka Narodowa. The Polish Journal of Economics, 300(4), 31-51. https://doi.org/10.33119/GN/113064
  13. Behzadian, M., Kazemzadeh, R.B., Albadvi, A., & Aghdasi, M. (2010). PROMETHEE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 200(1), 198-215. https://doi.org/10.1016/j.ejor.2009.01.021
  14. Bewick, V., Cheek, L., & Ball, J. (2003). Statistics review 7: Correlation and regression. Critical Care, 7(6), 451-459. https://doi.org/10.1186/cc2401
  15. Blesch, K., Hauser, O.P., & Jachimowicz, J.M. (2022). Measuring Inequality Beyond the Gini Coefficient May Clarify Conflicting Findings. Nature Human Behaviour, 6(11), 1525-1536. https://doi.org/10.1038/s41562-022-01430-7
  16. Bocken, N.M.P., de Pauw, I., Bakker, C., & van der Grinten, B. (2016). Product design and business model strategies for a circular economy. Journal of Industrial and Production Engineering, 33(5), 308-320. https://doi.org/10.1080/21681015.2016.1172124
  17. Börjesson Rivera, M., Håkansson, C., Svenfelt, Å., & Finnveden, G. (2014). Including second order effects in environmental assessments of ICT. Environmental Modelling & Software, 56. https://doi.org/10.1016/j.envsoft.2014.02.005
  18. Boudt, K., d’Errico, M., Luu, H.A., & Pietrelli, R. (2022). Interpretability of Composite Indicators Based on Principal Components. Journal of Probability and Statistics, 2022(1), 4155384. https://doi.org/10.1155/2022/4155384
  19. Brauers, W.K.M., & Zavadskas, E.K. (2009). Robustness of the multi-objective MOORA method with a test for the facilities sector. http://Www.Tandfonline.Com/Doi/Pdf/10.3846/1392-8619.2009.15.352-375. Retrieved from https://etalpykla.vilniustech.lt/handle/123456789/123807 on June 2, 2024.
  20. Butler, T., & Hackney, R. (2021). The role of informational mechanisms in the adoption of Green IS to achieve eco-sustainability in municipalities. Information & Management, 58(3), 103320. https://doi.org/10.1016/j.im.2020.103320
  21. Chakraborty, S., Datta, H.N., Kalita, K., & Chakraborty, S. (2023). A narrative review of multi-objective optimization on the basis of ratio analysis (MOORA) method in decision making. OPSEARCH, 60(4), 1844-1887. https://doi.org/10.1007/s12597-023-00676-7
  22. Chang, A.C., Li, P., & Martin, S.M. (2018). Comparing cross-country estimates of Lorenz curves using a Dirichlet distribution across estimators and datasets. Journal of Applied Econometrics, 33(3), 473-478. https://doi.org/10.1002/jae.2595
  23. Charfeddine, L., & Umlai, M. (2023). ICT sector, digitization and environmental sustainability: A systematic review of the literature from 2000 to 2022. Renewable and Sustainable Energy Reviews, 184, 113482. https://doi.org/10.1016/j.rser.2023.113482
  24. Chattamvelli, R., & Shanmugam, R. (2023). Descriptive Statistics for Scientists and Engineers: Applications in R. Germany: Springer.
  25. Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1), 6. https://doi.org/10.1186/s12864-019-6413-7
  26. COM/2014/0398. (2014). Communication from the Commission to the European Parliament, the Council, the European economic and social committee and the committee of the regions towards a circular economy: A zero waste programme for Europe. Commission to the European Parliament. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:52014DC0398 on June 5, 2024.
  27. COM/2020/98. (2020). Communication from the Commission to the European Parliament, the Council, the European Economic and social committee and the committee of the regions a new Circular Economy Action Plan For a cleaner and more competitive Europe. European Commission. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:52020DC0098 on June 20, 2024.
  28. COM/2020/102. (2020). Communication from the Commission to the European Parliament, the CounCil, the European Economic and Social Committee and the Committee of the Regions A New Industrial Strategy for Europe. European Commission. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:52020DC0102 on June 3, 2024.
  29. Costa, J., Alscher, P., & Thums, K. (2024). Global competences and education for sustainable development. A bibliometric analysis to situate the OECD global competences in the scientific discourse. Zeitschrift Für Erziehungswissenschaft, 27(4), 889-919. https://doi.org/10.1007/s11618-024-01220-z
  30. Cramer, J. (2023). How circular economy and digital technologies can support the building sector to cope with its worldwide environmental challenge?. Npj Urban Sustainability, 3(1), 1-3. https://doi.org/10.1038/s42949-023-00109-w
  31. Desruelle, P., & Stančík, (2014). Characterizing and comparing the evolution of the major global economies in information and communication technologies. Telecommunications Policy, 38(8), 812-826. https://doi.org/10.1016/j.telpol.2014.04.010
  32. Diaz, A., & Baumgartner, R.J. (2024). A managerial approach to product planning for a circular economy: Strategy implementation and evaluation support. Journal of Cleaner Production, 442, 140829. https://doi.org/10.1016/j.jclepro.2024.140829
  33. Dinçer, H., Yüksel, S., & Applegate, LM. (2019). Interval type 2-based hybrid fuzzy evaluation of financial services in E7 economies with DEMATEL-ANP and MOORA methods. Applied Soft Computing, 79, 186-202. https://doi.org/10.1016/j.asoc.2019.03.018
  34. Directive 2012/19/EU. (2012). Directive 2012/19/EU of the European Parliament and of the Council of 4 July 2012 on Waste Electrical and Electronic Equipment (WEEE). European Commission. Retrieved from https://eur-lex.europa.eu/eli/dir/2012/19/oj/eng on August 3, 2024.
  35. Drees, H., & Sabourin, A. (2021). Principal component analysis for multivariate extremes. Electronic Journal of Statistics, 15(1), 908-943. https://doi.org/10.1214/21-EJS1803
  36. Drofenik, J., Seljak, T., & Pintarič, Z.N. (2025). A Multi-Level Approach to Circular Economy Progress: Linking National Targets with Corporate Implementation. Journal of Cleaner Production, 144902. https://doi.org/10.1016/j.jclepro.2025.144902
  37. Fawole, A.A., Orikpete, O.F., Ehiobu, N.N., & Ewim, D.R.E. (2023). Climate change implications of electronic waste: Strategies for sustainable management. Bulletin of the National Research Centre, 47(1), 147. https://doi.org/10.1186/s42269-023-01124-8
  38. Geissdoerfer, M., Savaget, P., Bocken, N.M.P., & Hultink, E.J. (2017). The Circular Economy – A new sustainability paradigm?. Journal of Cleaner Production, 143, 757-768. https://doi.org/10.1016/j.jclepro.2016.12.048
  39. Glen, L., & Zazkis, R. (2021). On Linear Functions and Their Graphs: Refining the Cartesian Connection. International Journal of Science and Mathematics Education, 19(7), 1485-1504. https://doi.org/10.1007/s10763-020-10113-6
  40. Göktaş, A., & Akkuş, Ö. (2021). Recent statistical methods for data analysis, applied economics, business & finance. Journal of Applied Statistics, 48(13-15), 2231-2238. https://doi.org/10.1080/02664763.2021.1991180
  41. Govindan, K. (2023). How digitalization transforms the traditional circular economy to a smart circular economy for achieving SDGs and net zero. Transportation Research Part E: Logistics and Transportation Review, 177, 103147. https://doi.org/10.1016/j.tre.2023.103147
  42. Hernandez, A.A. (2018). Understanding Motivation Factors in Green IT Adoption: An Empirical Evidence from Philippine SMEs. International Journal of Asian Business and Information Management (IJABIM), 9(4), 21-35. https://doi.org/10.4018/IJABIM.2018100102
  43. Hilbert, S., & Bühner, M. (2020). Principal Components Analysis. In V. Zeigler-Hill & T. K. Shackelford (Eds.), Encyclopedia of Personality and Individual Differences (pp. 4030-4034). Springer International Publishing. https://doi.org/10.1007/978-3-319-24612-3_1340
  44. Hilty, L.M., & Aebischer, B. (Eds.). (2014). ICT Innovations for Sustainability. Zurich, Switzerland: Springer.
  45. Karande, P., & Chakraborty, S. (2012). Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for materials selection. Materials & Design, 37, 317-324. https://doi.org/10.1016/j.matdes.2012.01.013
  46. Keeble, B.R. (1988). The Brundtland Report: “Our Common Future.” Medicine and War, 4(1), 17-25. Retrieved from https://www.jstor.org/stable/45353161 on August 5, 2024.
  47. Khadivar, A., Mobini Kashe, M., & Basraei, R. (2024). A Fuzzy Inference System to Evaluate Maturity of Green Information Technology. Journal of Information Technology Management, 16(2), 1-15. https://doi.org/10.22059/jitm.2023.306213.2567
  48. Kok, J. (2018). Leading in a VUCA World. Cham: Springer.
  49. Korhonen, J., Nuur, C., Feldmann, A., & Birkie, S.E. (2018). Circular economy as an essentially contested concept. Journal of Cleaner Production, 175, 544-552. https://doi.org/10.1016/j.jclepro.2017.12.111
  50. Kristensen, J.P. (2022). The Gini coefficient and discontinuity. Cogent Economics & Finance, 10(1), 2072451. https://doi.org/10.1080/23322039.2022.2072451
  51. Kumar, D., & Daman, K. (2023). Emerging Challenges of E-Waste Management: A Growing Socio Environmental Concern. Research, 8(1). https://doi.org/10.1234/re.v8.i1.2
  52. Laranja Ribeiro, M.P., Tommasetti, R., Gomes, M.Z., Castro, A., & Ismail, A. (2021). Adoption phases of Green Information Technology in enhanced sustainability: A bibliometric study. Cleaner Engineering and Technology, 3, 100095. https://doi.org/10.1016/j.clet.2021.100095
  53. Lautenschutz, D., Espana, S., Hankel, A., Overbeek, S., & Lago, P. (2018). A Comparative Analysis of Green ICT Maturity Models. ICT4S2018. 5th International Conference on Information and Communication Technology for Sustainability, 52(3), 153-167. https://doi.org/10.29007/5hgz
  54. Marchetti, S., & Tzavidis, N. (2021). Robust Estimation of the Theil Index and the Gini Coeffient for Small Areas. Journal of Official Statistics, 37(4), 955-979. https://doi.org/10.2478/jos-2021-0041
  55. McMahon, K., Mugge, R., & Hultink, E.J. (2024). Overcoming barriers to circularity for internal ICT management in organizations: A change management approach. Resources, Conservation and Recycling, 205, 107568. https://doi.org/10.1016/j.resconrec.2024.107568
  56. McNamee, R., Schoch, N., Oelschlaeger, P., & Huskey, L. (2010). Collaboration continuum: Cultural and technological enablers of knowledge exchange. Research Technology Management, 53, 54-57. Retrieved from https://www.proquest.com/openview/1a0c0e8439f26cbc3a2c3b1b2f27afc0/1?pqorigsite=gscholar&cbl=37905 on July 25, 2024.
  57. Morrison, D.F. (2004). Multivariate Statistical Methods (4th edition). New York: McGraw Hill.
  58. Murugesan, S., & Gangadharan, G.R. (Eds.). (2012). Harnessing Green IT: Principles and Practices (1st edition). New York: Wiley-IEEE Press.
  59. Nath, V., & Agrawal, R. (2020). Agility and lean practices as antecedents of supply chain social sustainability. International Journal of Operations & Production Management, 40(10), 1589-1611. https://doi.org/10.1108/IJOPM-09-2019-0642
  60. Ngemba, H.R., Richardo, R.R., Nur, R., Rusydi, M., Lopo, Ch., Nu, M., & Febrina, A.P. (2021). Implementation of the Multi-Objective Optimization Method based on Ratio Analysis (MOORA) in the Decision Support System for Determining the Beneficiary of BPJS Health Contribution Assistance (Case Study: Loru Village, Sigi Regency). Tadulako Science and Technology Journal, 2(1), 26-31. https://doi.org/10.22487/sciencetech.v2i1.15577
  61. Ojo, A.O., & Fauzi, M.A. (2020). Environmental awareness and leadership commitment as determinants of IT professionals engagement in Green IT practices for environmental performance. Sustainable Production and Consumption, 24(5), 298-307. https://doi.org/10.1016/j.spc.2020.07.017
  62. Panek, T., & Zwierzchowski, J. (2013). Statystyczne metody wielowymiarowej analizy porównawczej teoria i zastosowania. Warszawa: Oficyna Wydawnicza SGH.
  63. Pérez-Domínguez, L., Rodríguez-Picón, L.A., Alvarado-Iniesta, A., Luviano Cruz, D., & Xu, Z. (2018). MOORA under Pythagorean Fuzzy Set for Multiple Criteria Decision Making. Complexity, 2018(1), 2602376. https://doi.org/10.1155/2018/2602376
  64. Queirós, R.C.C., Méxas, M.P., & Drumond, G.M. (2020). Green Information Technology in organizations: A strategic vision. Sistemas & Gestão, 15(2). https://doi.org/10.20985/1980-5160.2020.v15n2.1629
  65. Ramakrishnan, R. (2021). Leading in a VUCA World. Ushus Journal of Business Management, 20(1). https://doi.org/10.12725/ujbm.54.5
  66. Rani, P., Chen, S.M., & Mishra, A.R. (2023). Multiple attribute decision making based on MAIRCA, standard deviation-based method, and Pythagorean fuzzy sets. Information Sciences, 644, 119274. https://doi.org/10.1016/j.ins.2023.119274
  67. Rizk-Allah, R.M., Hassanien, A.E., & Slowik, A. (2020). Multi-objective orthogonal opposition-based crow search algorithm for large-scale multi-objective optimization. Neural Computing and Applications, 32(17), 13715-13746. https://doi.org/10.1007/s00521-020-04779-w
  68. Roussilhe, G., Ligozat, A.L., & Quinton, S. (2023). A long road ahead: A review of the state of knowledge of the environmental effects of digitization. Current Opinion in Environmental Sustainability, 62, 101296. https://doi.org/10.1016/j.cosust.2023.101296
  69. Şahin, M. (2021). A comprehensive analysis of weighting and multicriteria methods in the context of sustainable energy. International Journal of Environmental Science and Technology, 18(6), 1591-1616. https://doi.org/10.1007/s13762-020-02922-7
  70. Santarius, T., Dencik, L., Diez, T., Ferreboeuf, H., Jankowski, P., Hankey, S., Hilbeck, A., Hilty, L.M., Höjer, M., Kleine, D., Lange, S., Pohl, J., Reisch, L., Ryghaug, M., Schwanen, T., & Staab, P. (2023). Digitalization and Sustainability: A Call for a Digital Green Deal. Environmental Science & Policy, 147, 11-14. https://doi.org/10.1016/j.envsci.2023.04.020
  71. Schneider, M. (2021). The Discovery of the Gini Coefficient: Was the Lorenz Curve the Catalyst?. History of Political Economy, 53(1), 115-141. https://doi.org/10.1215/00182702-8816637
  72. Singh, R., Pathak, V.K., Kumar, R., Dikshit, M., Aherwar, A., Singh, V., & Singh, T. (2024). A historical review and analysis on MOORA and its fuzzy extensions for different applications. Heliyon, 10(3), e25453. https://doi.org/10.1016/j.heliyon.2024.e25453
  73. Sitthiyot, T., & Holasut, K. (2021). A simple method for estimating the Lorenz curve. Humanities and Social Sciences Communications, 8(1), 1-9. https://doi.org/10.1057/s41599-021-00948-x
  74. Stoer, J., & Bulirsch, R. (2002). Systems of Linear Equations. In J. Stoer & R. Bulirsch (Eds.), Introduction to Numerical Analysis (pp. 190-288). Berlin: Springer.
  75. Sutarno, S., Mesran, M., Supriyanto, S., Yuliana, Y., & Dewi, A. (2019). Implementation of Multi-Objective Optimazation on the Base of Ratio Analysis (MOORA) in Improving Support for Decision on Sales Location Determination. Journal of Physics: Conference Series, 1424(1), 012019. https://doi.org/10.1088/1742-6596/1424/1/012019
  76. Taticchi, P., & Demartini, M. (Eds.). (2021). Corporate Sustainability in Practice: A Guide for Strategy Development and Implementation (1st ed. 2021 edition). Berlin: Springer.
  77. Thakkar, J.J. (2021). Multi-objective Optimization on the Basis of Ratio Analysis Method (MOORA). Springer. https://doi.org/10.1007/978-981-33-4745-8_11
  78. Theis, V., & Schreiber, D. (2020). Analysis of Green IT practices in technology-based organizations. Revista de Administração Da UFSM, 13(3), 1530-1550. https://doi.org/10.5902/1983465936408
  79. Tsoulfidis, L., & Athanasiadis, I. (2022). A new method of identifying key industries: A principal component analysis. Journal of Economic Structures, 11(1), 2. https://doi.org/10.1186/s40008-022-00261-z
  80. Unhelkar, B. (2011). Green IT Strategies and Applications: Using Environmental Intelligence (1st edition). London: Taylor & Francis, Routledge Group.
  81. Upadhyay, A., Laing, T., Kumar, V., & Dora, M. (2021). Exploring barriers and drivers to the implementation of circular economy practices in the mining industry. Resources Policy, 72, 102037. https://doi.org/10.1016/j.resourpol.2021.102037
  82. Wells, P.J. (2016). Understanding Linear Functions and Their Representations. Mathematics Teaching in the Middle School, 21(5), 308-316. https://doi.org/10.5951/mathteacmiddscho.21.5.0308
  83. Zahra, F. (2011). GREEN-IT: Why Developing Countries Should Care?. International Journal of Computer Science Issues, 8(4), 424-438.

Downloads

Download data is not yet available.

Similar Articles

11-20 of 224

You may also start an advanced similarity search for this article.