“Quo Vadis urban areas?”: (Re)thinking the future of urban areas using interpretive structural modeling
DOI: https://doi.org/10.3846/ijspm.2025.24051Abstract
The world’s population continues to grow at an unprecedented rate, with urban areas experiencing a more rapid rise in population density than rural regions. This demographic shift compels decision-makers to address pressing urban challenges and rethink future structures of cities. However, a vision for global sustainable urban growth remains elusive, as planners often lack comprehensive, credible and dynamic models to guide decision-making. The main purpose of this study is to propose a process-oriented methodology that integrates cognitive mapping, interpretive structural modeling (ISM) and a matrice d’impacts croisés multiplication appliquée à un classement (MICMAC) analysis to evaluate and prioritize key determinants of urban development. Group work sessions involving decision-makers from diverse fields were conducted to identify critical variables influencing urban development. Unlike traditional models, the proposed approach emphasizes participatory decision-making. By combining cognitive mapping and ISM-MICMAC, this study enables the identification of causal relationships among variables and allows decision-makers to anticipate trends and prioritize challenges effectively. The findings were further validated by an external expert to ensure neutrality and reliability. Overall, this study provides a theoretical contribution to decision-making methodologies while offering a practical framework for urban planners to influence cities toward a sustainable future.
Keywords:
cognitive mapping, decision making, Interpretive Structural Modeling (ISM), Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC), population density, urban areas, world populationHow to Cite
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Abdel-Basset, M., Mohamed, M., Hussien, A. N., & Sangaiah, A. (2018). A novel group decision-making model based on triangular neutrosophic numbers. Soft Computing, 22(20), 6629–6643. https://doi.org/10.1007/s00500-017-2758-5
Ackermann, F., & Eden, C. (2001). Strategic options development and analysis. In J. Rosenhead & J. Mingers (Eds.), Rational analysis for a problematic world revisited: Problem structuring methods for complexity, uncertainty and conflict (pp. 135–190). John Wiley & Sons.
Alonso, S., Herrera-Viedma, E., Chiclana, F., & Herrera, F. (2010). A web based consensus support system for group decision making problems and incomplete preferences. Information Sciences, 180(23), 4477–4495. https://doi.org/10.1016/j.ins.2010.08.005
Andrade, F., Ferreira, F., & Correia, R. (2022). Ranking residential neighborhoods based on their sustainability: A CM-BWM approach. International Journal of Strategic Property Management, 26(6), 410–423. https://doi.org/10.3846/ijspm.2022.18310
Attri, R., Dev, N., & Sharma, V. (2013). Interpretive Structural Modelling (ISM) approach: An overview. Research Journal of Management Sciences, 2(2), 3–8.
Bag, S., & Anand, N. (2015). Modeling barriers of sustainable supply chain network design using interpretive structural modelling: An insight from food processing sector in India. International Journal of Automation and Logistics, 1(3), 234–255. https://doi.org/10.1504/IJAL.2015.071722
Bell, S., & Morse, S. (2013). Groups and facilitators within problem structuring processes. Journal of the Operational Research Society, 64(7), 959–972. https://doi.org/10.1057/jors.2012.110
Bertinelli, L., & Black, D. (2004). Urbanization and growth. Journal of Urban Economics, 56, 80–96. https://doi.org/10.1016/j.jue.2004.03.003
Capuano, N., Chiclana, F., Fujita, H., Herrera-Viedma, E., & Loia, V. (2018). Fuzzy group decision making with incomplete information guided by social influence. IEEE Transactions on Fuzzy Systems, 26(3), 1704–1718. https://doi.org/10.1109/TFUZZ.2017.2744605
Chauvin, J., Glaeser, E., Ma, Y., & Tobio, K. (2017). What is different about urbanization in rich and poor countries? Cities in Brazil, China, India and the United States. Journal of Urban Economics, 98, 17–49. https://doi.org/10.1016/j.jue.2016.05.003
Çipi, A., Fernandes, A., Ferreira, F., Ferreira, N., & Meidutė-Kavaliauskienė, I. (2023). Detecting and developing new business opportunities in Society 5.0 contexts: A sociotechnical approach. Technology in Society, 73, Article 102243. https://doi.org/10.1016/j.techsoc.2023.102243
Çipi, A., Ferreira, A., Ferreira, F., & Ferreira, N. (2025). Using interpretive structural modeling (ISM) to detect and define initiatives that facilitate hemodynamic laboratory management. International Transactions in Operational Research, 32(4), 2117–2138. https://doi.org/10.1111/itor.13385
Cordeiro, T., Ferreira, F., Sphar, R., Sunderman, M., & Ferreira, N. (2024). Enhanced planning capacity in urban renewal: Addressing complex challenges using neutrosophic logic and DEMATEL. Cities, 150, Article 105006. https://doi.org/10.1016/j.cities.2024.105006
Correia, I., Ferreira, F., Zopounidis, C., & Ferreira, N. (2024). Urban expansion effects on real estate ecosystems: Identification and neutrosophic analysis of causal dynamics. Socio-Economic Planning Sciences, 93, Article 101858. https://doi.org/10.1016/j.seps.2024.101858
Dadashpour, R., Ahmadzadeh, H., & Valizadeh, R. (2025). Interpretive structural modeling the impact of urban management revenue on urban sustainability (case study: Urmia City). Journal of Studies of Human Settlements Planning, 19(4), 45–59.
Digalwar, A., & Giridhar, G. (2015). Interpretive Structural Modeling approach for development of electric vehicle market in India. Procedia CIRP, 26, 40–45. https://doi.org/10.1016/j.procir.2014.07.125
Dociu, M., & Dunarintu, A. (2012). The socio-economic impact of urbanization. International Journal of Academic Research in Accounting, Finance and Management Sciences, 2, 47–52.
Dong, Y., Zha, Q., Zhang, H., Kou, G., Fujita, H., Chiclana, F., & Herrera-Viedma, E. (2018). Consensus reaching in social network group decision making: Research paradigms and challenges. Knowledge-Based Systems, 162, 3–13. https://doi.org/10.1016/j.knosys.2018.06.036
Fernandes, I., Ferreira, F., Bento, P., Jalali, M., & António, N. (2018). Assessing sustainable development in urban areas using cognitive mapping and MCDA. International Journal of Sustainable Development & World Ecology, 25(3), 216–226. https://doi.org/10.1080/13504509.2017.1358221
Ferreira, F., Spahr, R., Sunderman, M., Govindan, K., & Meidutė-Kavaliauskienė, I. (2022). Urban blight remediation strategies subject to seasonal constraints. European Journal of Operational Research, 296(1), 277–288. https://doi.org/10.1016/j.ejor.2021.03.045
Freeman, R., & Yearworth, M. (2017). Climate change and cities: Problem structuring methods and critical perspectives on low-carbon districts. Energy Research & Social Science, 25, 48–64. https://doi.org/10.1016/j.erss.2016.11.009
Freire, C., Ferreira, F., Carayannis, E., & Ferreira, J. (2023). Artificial intelligence and smart cities: A DEMATEL approach to adaptation challenges and initiatives. IEEE Transactions on Engineering Management, 70(5), 1881–1899. https://doi.org/10.1109/TEM.2021.3098665
Godfrey, R., & Julien, M. (2005). Urbanisation and health. Clinical Medicine, 5(2), 137–141. https://doi.org/10.7861/clinmedicine.5-2-137
Gu, C. (2019). Urbanization: Processes and driving forces. Science China, 62(9), 1351–1360. https://doi.org/10.1007/s11430-018-9359-y
Guo, Z., Li, Y., & Stevens, K. (2012). Analyzing students’ technology use motivations: An interpretive structural modeling approach. Communications of the Association for Information Systems, 30(14), 199–224. https://doi.org/10.17705/1CAIS.03014
Ha, N., Le, N., & Trung-Kien, P. (2021). The impact of urbanization on poverty reduction: An evidence from Vietnam. Cogent Economics & Finance, 9(1), Article 1918838. https://doi.org/10.1080/23322039.2021.1918838
Huang, Y., Wei, W., & Ferreira, F. (2023). How to make urban renewal sustainable? Pathway analysis based on fuzzy-set qualitative comparative analysis (fsQCA). International Journal of Strategic Property Management, 27(3), 146–158. https://doi.org/10.3846/ijspm.2023.19352
Idowu, O. (2013). Challenges of urbanization and urban growth in Nigeria. American Journal of Sustainable Cities and Society, 2(1), 79–94.
Jones, E., & Leather, S. (2012). Invertebrates in urban areas: A review. European Journal of Entomology, 109(4), 463–478. https://doi.org/10.14411/eje.2012.060
Karadayi-Usta, S. (2020). An interpretive structural analysis for Industry 4.0 adoption challenges. IEEE Transactions on Engineering Management, 67(3), 973–978. https://doi.org/10.1109/TEM.2018.2890443
Kim, I., & Watada, J. (2009). Decision making with an interpretive structural modeling method using a DNA-based algorithm. IEEE transactions on Nanobioscience, 8(2), 181–191. https://doi.org/10.1109/TNB.2009.2023788
Li, G., Huang, D., Sun, C., & Li, Y. (2019). Developing interpretive structural modeling based on factor analysis for the water-energy-food nexus conundrum. Science of the Total Environment, 651(1), 309–322. https://doi.org/10.1016/j.scitotenv.2018.09.188
Li, G., Kou, G., & Peng, Y. (2018). A group decision making model for integrating heterogeneous information. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(6), 982–992. https://doi.org/10.1109/TSMC.2016.2627050
Limburg, K., Stainbrook, K., Erickson, J., & Gowdy, J. (2005). Urbanization consequences: Case studies in the Hudson River Watershed. American Fisheries Society Symposium, 47, 23–37.
Lishuang, C., Juling, Q., & Lie, J. (2013). The study on citizenization of migrant workers in the process of China’s urbanization. In Proceedings of the 2nd International Conference on Management Science and Industrial Engineering (pp. 786–789). Atlantis Press.
Lyer, K., & Sagheer, M. (2010). Hierarchical structuring of PPP risks using interpretative structural modeling. Journal of Construction Engineering and Management, 136(2), 151–159. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000127
Ma, M., & Tang, J. (2022) Interactive coercive relationship and spatio-temporal coupling coordination degree between tourism urbanization and eco-environment: A case study in Western China. Ecological Indicators, 142(2), Article 109149. https://doi.org/10.1016/j.ecolind.2022.109149
Martinez-Vazquez, J., Panudulkitti, P., & Timofeev, A. (2014). Urbanization and the poverty level. Revista de Estudios Regionales, 100, 19–46.
Mingers, J., & Rosenhead, J. (2004). Problem structuring methods in action. European Journal of Operational Research, 152(3), 530–554. https://doi.org/10.1016/S0377-2217(03)00056-0
Mitchell, J. (1956). Urbanization, detribalization and stabilization in Southern Africa: A problem of definition and measurement. In Social implications of industrialization and urbanization in Africa south of the Sahara (pp. 693–711). UNESCO International African Institute.
National Geographic Society. (2022). Urban area. https://education.nationalgeographic.org/resource/urban-area
Oliveira, P., Ferreira, F., Dabić, M., Ferreira, J., & Ferreira, N. (2024). Analyzing the causal dynamics of circular-economy drivers in SMEs using interpretive structural modeling. Energy Economics, 138, Article 107842. https://doi.org/10.1016/j.eneco.2024.107842
Ormerod, R. (2013). Logic and rationality in OR interventions: An examination in the light of the ‘critical rationalist’ approach. Journal of the Operational Research Society, 64(4), 469–487. https://doi.org/10.1057/jors.2012.58
Pinto, B., Ferreira, F., Spahr, R., Sunderman, M., & Pereira, L. (2023). Analyzing causes of urban blight using cognitive mapping and DEMATEL. Annals of Operations Research, 325(2), 1083–1110. https://doi.org/10.1007/s10479-022-04614-6
Ran, Y., Lei, D., Li, J., Gao, L., Mo, J., & Liu, X. (2022). Identification of crucial areas of territorial ecological restoration based on ecological security pattern: A case study of the central Yunnan urban agglomeration, China. Ecological Indicators, 143, Article 109318. https://doi.org/10.1016/j.ecolind.2022.109318
Rodrigues, M., Ferreira, F., & Ferreira, N. (2025). Constructing smarter and more sustainable urban ecosystems: A dynamic analysis of challenges and initiatives. Annals of Operations Research, 347(1), 131–171. https://doi.org/10.1007/s10479-023-05406-2
Rosenhead, J., & Mingers, J. (2001). Rational analysis for a problematic world revisited: Problem structuring methods for complexity, uncertainty and conflict. Wiley.
Santos, M., Ferreira, F., Ferreira, N., & Carayannis, E. (2024). “How can more be done with less?”: Investigating frugal innovation determinants using design thinking and interpretive structural modeling. IEEE Transactions on Engineering Management, 71, 14543–14558. https://doi.org/10.1109/TEM.2024.3453938
Satapathy, S., Patel, S., Biswas, A., & Mishra, P. (2012). Interpretive structural modeling for e-electricity utility service. Service Business, 6, 349–367. https://doi.org/10.1007/s11628-012-0139-9
Shaban, A., Kourtit, K., & Nijkamp, P. (2022). Causality between urbanization and economic growth: Evidence from the Indian states. Frontiers in Sustainable Cities, 4, Article 901346. https://doi.org/10.3389/frsc.2022.901346
Shen, L., Peng, Y., Zhang, X., & Wu, Y. (2012). An alternative model for evaluating sustainable urbanization. Cities, 29(1), 32–39. https://doi.org/10.1016/j.cities.2011.06.008
Shoar, S., & Chileshe, N. (2021). Exploring the causes of design changes in building construction projects: An interpretive structural modeling approach. Sustainability, 13, Article 9578. https://doi.org/10.3390/su13179578
Soares, R., Ferreira, F., Teixeira, F., & Ferreira, N. (2022). A multicriteria evaluation system for large real estate investments. International Journal of Strategic Property Management, 26(4), 305–317. https://doi.org/10.3846/ijspm.2022.17922
Sushil. (2012). Interpreting the interpretive structural model. Global Journal of Flexible Systems Management, 13(2), 87–106. https://doi.org/10.1007/s40171-012-0008-3
Talib, F., Rahman, Z., & Qureshi, M. (2011). An interpretive structural modelling approach for modelling the practices of total quality management in service sector. International Journal of Modelling in Operations Management, 1(3), 223–250. https://doi.org/10.1504/IJMOM.2011.039528
Tyagi, S., Sharma, S., Krishankumar, R., & Ravichandran, K. (2022). An extension of interpretive structural modeling using linguistic term sets for business decision-making. OPSEARCH, 59, 1158–1177. https://doi.org/10.1007/s12597-021-00565-x
United Nations. (2018). Annual percentage of population at mid-year residing in urban areas. https://population.un.org/wup/downloads
Varela, M., Ferreira, F., Ferreira, N., & Correia, R. (2025). Digital health at Central Lisbon University Hospital Center: Strategic reflections and value proposition. International Transactions in Operational Research, 32(4), 2089–2116. https://doi.org/10.1111/itor.13336
Vaz, A., Ferreira, F., Pereira, L., Correia, R., & Banaitis, A. (2022). Strategic visualization: The (real) usefulness of cognitive mapping in smart city conceptualization. Management Decision, 60(4), 916–939. https://doi.org/10.1108/MD-11-2020-1512
Vaz-Patto, C., Ferreira, F., Govindan, K., & Ferreira, N. (2024). Rethinking urban quality of life: Unveiling causality links using cognitive mapping, neutrosophic logic and DEMATEL. European Journal of Operational Research, 316(1), 310–328. https://doi.org/10.1016/j.ejor.2023.12.034
Vieira, F., Ferreira, F., Govindan, K., Ferreira, N., & Banaitis, A. (2022). Measuring urban digitalization using cognitive mapping and the best worst method (BWM). Technology in Society, 71, Article 102131. https://doi.org/10.1016/j.techsoc.2022.102131
Vlahov, D., & Galea, S. (2002). Urbanization, urbanicity, and health. Journal of Urban Health, 79(4), S1–S12. https://doi.org/10.1093/jurban/79.suppl_1.S1
Warfield, J. (1974). Developing interconnection matrices in structural modeling. IEEE Transactions on Systems, Man, and Cybernetics, 4(1), 81–87. https://doi.org/10.1109/TSMC.1974.5408524
Warshall, S. (1962). A theorem on Boolean matrices. Journal of the Association for Computing Machinery, 9, 11–12. https://doi.org/10.1145/321105.321107
Yadav, G., Mangla, S., Luthra, S., & Rai, D. (2019). Developing a sustainable smart city framework for developing economies: An Indian context. Sustainable Cities and Society, 47, Article 101462. https://doi.org/10.1016/j.scs.2019.101462
Zhang, Z., Guo, C., & Martínez, L. (2017). Managing multigranular linguistic distribution assessments in large-scale multi-attribute group decision making. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(11), 3063–3076. https://doi.org/10.1109/TSMC.2016.2560521
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