10.57647/fomj.2026.0701.01

Developing a Hybrid SWOT-MCDM Framework for Formulating Smart Technology Adoption Strategies in Urban Construction Under Conditions of Uncertainty: A Case Study of Qom Municipality

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  • Reza Bakhshipour,
  • Seyed Mohammad Mirhossseini*1,
  • Ehsanollah Zeighami,
  • Mohammad Ehsanifar2,
  1. Department of Civil Engineering, Ar.C., Islamic Azad University, Arak, Iran
  2. Department of Industrial Engineering, Ar.C., Islamic Azad University, Arak, Iran

Received: 2025-12-18

Revised: 2026-02-20

Accepted: 2026-03-25

Published in Issue 2026-03-30

Copyright (c) 2026 Reza Bakhshipour, Seyed Mohammad Mirhossseini, Ehsanollah Zeighami, Mohammad Ehsanifar (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Bakhshipour, R., Mirhossseini, S. M., Zeighami, E., & Ehsanifar, M. (2026). Developing a Hybrid SWOT-MCDM Framework for Formulating Smart Technology Adoption Strategies in Urban Construction Under Conditions of Uncertainty: A Case Study of Qom Municipality. Fuzzy Optimization and Modeling Journal (FOMJ), 7(1). https://doi.org/10.57647/fomj.2026.0701.01
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Abstract

Smart technology adoption in urban construction is a key driver toward the development of smarter cities. However, the complexities and uncertainties associated with technology acceptance necessitate strategic planning based on structured decision-making frameworks. A hybrid SWOT–MCDM framework was
developed under uncertainty for formulating effective strategies for smart technology adoption. In the first phase of the study, a modified six-dimensional SWOT model, including the traditional dimensions (Strengths Opportunitie (SO), Weaknesses Opportunities (WO), Strengths Threats (ST), Weaknesses Threats (WT))
along with two additional dimensions (Weaknesses Strengths (WS), Opportunitie Threats (OT)) was employed to identify and categorize the key factors influencing technology adoption. In the second phase, the Multi-Criteria Decision-Making (MCDM) approach under Uncertainty was utilized to prioritize the resulting
strategic alternatives. The proposed framework was implemented in Qom Municipality - Iran. The findings revealed that among the identified factors, “holding training workshops and seminars” with a weight of 0.1152 was the most influential sub-factor in the strengths category. Furthermore, the evaluation of proposed strategies
based on the SWOT framework indicated that the SO strategy (leveraging strengths to exploit opportunities) ranked first with a score of 4.826, followed by the SW (4.660), ST (3.809), WO (3.845), WT (3.338), and OT (3.259) strategies, respectively. This prioritization demonstrates that focusing on maximizing strengths and opportunities can facilitate the successful development and implementation of smart technologies. Sensitivity analysis confirmed the robustness and validity of the prioritization results. Hence, the framework can serve as a tool for policy-makers and urban planners in formulating targeted strategies and making informed investment decisions in innovative construction infrastructures.

Keywords

  • Multi-Criteria Decision-Making (MCDM),
  • Urban Construction Industry,
  • Six-Dimensional SWOT matrix
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References

  1. Habib A, Alsmadi D, and Prybutok VR. PrybutokFactors that determine residents’ acceptance of smart city technologies. Smart Cities at Play: Technology and Emerging Forms of Playfulness 2023 :4–17. doi: 10.1201/9781003461043-2
  2. Fallahi A, Faraji A, and Gharibi A. Analysis of Key Barriers to theUse of the Internet of Things in Iranian Smart Cities (Structural Analysis Method).Business Intelligence Management Studies 2021; 10:137–71. doi: 10.22054/ims.2021.63159.2037
  3. Ageed ZS, Zeebaree SR, Sadeeq MM, Kak SF, Rashid ZN, Salih AA, and Abdullah WM. A survey of data mining implementation in smart city applications. Qubahan Academic Journal 2021; 1:91–9. doi: 10.48161/qaj.v1n2a52
  4. Gharibi A and Moghtaderi F. Smart transformation in Iran (Part 1). 2021
  5. Ellsmoor J. Smart cities: the future of urban development. Forbes 2019
  6. Attoue N, Shahrour I, and Younes R. Smart building: Use of the artificial neural network approach for indoor temperature forecasting. Energies 2018; 11:395. doi: 10.3390/en11020395
  7. Balta-Ozkan N, Davidson R, Bicket M, and Whitmarsh L. Social barriers to the adoption of smart homes. Energy Policy 2013; 63:363–74. doi: 10.1016/j.enpol.2013.08.043
  8. Nazmfar H, Eshgheichaharborj A, and Esmaeili A. Analysis of urban growth indicators in Urmia. Journal of Urban Ecology Researches 2018; 9:35–48. doi: 20.1001.1.25383930.1397.9.17.3.5
  9. Karimi SH and Khameneh AH. A review of the barriers and challenges of adopting smart technology in project management processes. The Fifth International Conference and the Sixth National Conference on Civil Engineering, Architecture, Art and Urban Design 2022
  10. Apanaviciene R, Vanagas A, and Fokaides PA. Smart building integration into a smart city (SBISC): Development of a new evaluation framework. Energies 2020; 13:2190. doi: 10.3390/en13092190
  11. Shaikh PH, Nor NBM, Nallagownden P, Elamvazuthi I, and Ibrahim T. A review on optimized control systems for building energy and comfort management of smart sustainable buildings. Renewable and Sustainable Energy Reviews 2014; 34:409–29. doi: 10.1016/j.rser.2014.03.027
  12. Hwang BG, Ngo J, and Teo JZK. Challenges and strategies for the adoption of smart technologies in the construction industry: The case of Singapore. Journal of Management in Engineering 2022; 38:05021014. doi: 10.1061/(ASCE)ME.1943-5479.00009
  13. Sepasgozar SM, Loosemore M, and Davis SR. Conceptualising information and equipment technology adoption in construction: A critical review of existing research. Engineering, Construction and Architectural Management 2016; 23:158–76. doi:10.1108/ECAM-05-2015-0083
  14. Byun J, Sa S, Kim J, Kim S, Shin YT, and Kim JB. Smart city implementation models based on IoT technology. Advanced Science and Technology Letters 2016; 129:209–12. doi: 10.14257/astl. 2016.129.41
  15. Motallebi F, Ashtiani PG, and Ehsanifar M. Development of a New SWOT-MCDM Model to Create Marketing and Financial Strategies in Conditions of Uncertainty (Case Study of Iralco Company). Advances in Mathematical Finance and Applications 2024; 9(3):1043–69. doi: 10.71716/amfa.2024.23061898
  16. Fahey L and Narayanan VK. Macroenvironmental analysis for strategic management. West Publishing 1986
  17. Courtney H, Kirkland J, and Viguerie P. Strategy under uncertainty. Harvard Business Review 1997; 75:67–79
  18. Ataei-Qaracha M and Davoudi MR. Strategic Planning Development Using SWOT Matrix and Fuzzy AHP Technique in a State Bank. Journal of Operations Management. 2
  19. Bararinia E. Analysis and Development of Strategy Resulting from SWOT Matrix Using Fuzzy Theory and Analytic Network Process. Second International Conference on Industrial Management and Engineering in the Modern Era, Tehran 2019. Available from: https://civilica.com/doc/91620
  20. Kissi E, Aigbavboa C, and Kuoribo E. Emerging technologies in the construction industry: challenges and strategies in Ghana. Construction Innovation 2023; 23:383–405. doi: 10.1108/CI-11-2021-0215
  21. Ejidike CC and Mewomo MC. Benefits of adopting smart building technologies in building construction of developing countries: Review of literature. SN Applied Sciences 2023; 5:52. doi:10.1007/s42452-022-05262-y
  22. Zhu H, Hwang BG, Ngo J, and Tan JPS. Applications of smart technologies in construction project management. Journal of Construction Engineering and Management 2022; 148:04022010. doi: 10.1061/(ASCE)CO.1943-7862.0002260
  23. DixonLRandUmeokafor N. Determinants of smart technology adoption in the construction phase of projects: a scoping study of the United Kingdom. 2021
  24. Ngo J, Hwang BG, and Teo J. Impact of smart technologies on construction projects: Improvements in project performance. Proc. of the Conference CIB W78 2021; 2021:11–5
  25. Ghansah FA, Owusu-Manu DG, and Ayarkwa j. Project management processes in the adoption of smart building technologies: a systematic review of constraints. Smart and Sustainable Built Environment 2021; 10:208–26. doi: 10.1108/SASBE-12-2019-0161
  26. Khan MI. Evaluating the strategies of compressed natural gas industry using an integrated SWOT and MCDM approach. Journal of Cleaner Production 2018; 172:1035–52. doi: 10.1016/j.jclepro.2017.10.231
  27. Nikolaou IE and Evangelinos KI. A SWOT analysisof environmental management practices in Greek Mining and Mineral Industry. Resources Policy 2010; 35:226–34. doi: 10.1016/j.resourpol.2010.02.002
  28. Terrados J, Almonacid G, and Hontoria L. Hontoria Regional energy planning through SWOT analysis and strategic planning tools: Impact on renewables development. Renewable and sustainable energy reviews 2007; 11:1275–87. doi: 10.1016/j.rser.2005.08.003
  29. Kotler P. Reconceptualizing marketing: an interview with Philip Kotler. European Management Journal 1994; 12:353–61. doi: 10.1016/0263-2373(94)90021-3
  30. Dyson RG. Strategic development and SWOT analysis at the University of Warwick. European Journal of Operational Research 2004; 152:631–40. doi:10.1016/S0377-2217(03)00062-6
  31. Terrados J, Almonacid G, andHontoria L. Regional energy planning through SWOT analysis and strategic planning tools: Impact on renewables development. Renewable and Sustainable Energy Reviews 2007; 11:1275–87. doi: 10.1016/j.rser.2005.08.003
  32. Markovska N, Taseska V, and Pop-Jordanov J. SWOT analyses of the national energy sector for sustainable energy development. Energy 2009; 34:752–6. doi:10.1016/j.energy.2009.02.006
  33. Maihemuti S, Wang W, Wu J, and Wang H. New energy power system operation security evaluation based on the SWOT analysis. Scientific Reports 2022; 12:12680. doi: 10.1038/s41598-022-16444-4
  34. Mohanaravi K, Samykano M, Pandey AK, Noor MM, and Kadirgama K. A Succinct review of strengths, weaknesses, opportunities, and threats (SWOT) analyses, challenges and prospects of solar and wind tree technologies for hybrid power generation. Frontiers in Energy Research 2024; 12:1417511. doi: 10.3389/fenrg.2024.1417511
  35. Guangul FM and Chala GT. Solar energy as renewable energy source: SWOT analysis. IEEE 2019 :5–11. doi: 10.1109/ICBDSC.2019.8645580
  36. Nikolaou IE and Evangelinos KI. A SWOT analysis of environmental management practices in Greek Mining and Mineral Industry. Resources Policy 2010; 35:226–34. doi: 10.1016/j.resourpol.2010.02.002
  37. Xingang Z, Jiaoli K, and Bei L. Focus on the development of shale gas in China-Based on SWOT analysis. Focus on the development of shale gas in China—Based on SWOT analysis. 21
  38. Srivastava PK, Kulshreshtha K, Mohanty CS, Pushpangadan P, and Singh A. Stakeholder-based SWOT analysis for successful municipal solid waste management in Lucknow, India. Waste Management 2005; 25:531–7. doi: 10.1016/j.wasman.2004.08.010
  39. Ocampo L. Full consistency method (FUCOM) and weighted sum under fuzzy information for evaluating the sustainability of farm tourism sites. Soft Computing 2022; 26:12481–508. doi: 10. 1007/s00500-022-07184-8
  40. Demir G, Damjanovi´c M, Matovi´c B, and Vujadinovi ´c R. Toward sustainable urban mobility by using fuzzy-FUCOM and fuzzy-CoCoSo methods: the case of the SUMP podgorica. Sustainability 2022; 14:4972. doi: 10.3390/su14094972
  41. Okoli C and Pawlowski SD. The Delphi method as a research tool: an example, design considerations and applications. Information and Management 2004; 42:15–29
  42. Bashartian H, Gorji B, and Naraghi M. Towards the Municipality and Smart City of Qom. 2019. Available from: https : / /www.qom. ir / uploads /media/files/report-map-smart-qom.pdf
  43. Yarahmadi M, Mirhoseini M, Komasi M, and Ehsanifar M. The Factors Affecting Human Resources Productivity in Urban Construction Projects A Comparison of Relative Importance Index and Fuzzy Logic Methods. Fuzzy Optimization and Modeling Journal (FOMJ) 2023; 4:54–71. doi: 10.30495/fomj.2023.1992756.1106
  44. Moradpour N, Pourahmad A, Ziari K, Hataminejad H, and Sharifi A. Downscaling urban resilience assessment: A spatiotemporal analysis of urban blocks using the fuzzy Delphi method and Kmeans clustering. Building and Environment 2024;263:111898. doi: 10.1016/j.buildenv.2024.111898
  45. Pamuˇcar D, Stevi´c Z, and Sremac S. A new model for determining weight coefficients of criteria in mcdm models: Full consistency method (fucom). Symmetry 2018; 10:393. doi: 10.3390/sym10090393
  46. Pamucar D and Ecer F. Prioritizing the weights of the evaluation criteria under fuzziness: The fuzzy full consistency method–FUCOM-F. Facta Universitatis, Series: Mechanical Engineering 2020;18:419–37. doi: 10.22190/FUME200602034P
  47. Gupta H. Assessing organizations performance on the basis of GHRM practices using BWM and Fuzzy TOPSIS. Journal of Environmental Management 2018; 226:201–16. doi: 10.1016/j.jenvman.2018.08.005
  48. Lestari PFI, Prabowo TT, and Utomo WM. The effectiveness of fuzzy-SAW method for the selection of new student admissions in vocational high school. Lett. Inf. Technol. Educ 2020; 3:18–22. doi: 10.17977/um010v3i12020p018