OVERCOMING BARIERS TO BIG DATA ADOPTION: STRATEGIC SOLUTIONS FOR STRENGTHENING DISASTER RISK RESILIENCE IN HUMANITARIAN SUPPLY CHAINS

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Published Aug 9, 2025
Sait Gül Gözde Yangınlar

Abstract

This article explores the barriers to big data adoption and proposes strategic solutions for improving disaster risk resilience to overcome these barriers in humanitarian supply chains (HSCs). Its theoretical model is grounded on the resource-based view (RBV). Based on a combination of a literature review and interviews with experts from humanitarian organizations, 24 barriers to big data adoption were identified. These barriers span infrastructural, technological, managerial, financial, and human-related factors within the humanitarian supply chain. Additionally, eight strategies were defined as solutions to overcome these barriers. The study uses the Fermatean Fuzzy Analytic Hierarchy Process (FF-AHP) to obtain the weights of barriers and the Borda Social Choice Function. The findings offer valuable insights into evaluating solutions to address barriers to big data adoption. The study ranks the barriers based on their influence. The most significant barrier is the shortage of facilities to research and develop big data tools. Other critical barriers include high costs, lack of training facilities, data quality issues, and lack of government support. The results show that the most effective solutions for improving disaster risk resilience involve increasing IT infrastructure, developing strategic plans, and securing government support to overcome barriers to big data adoption. A comprehensive understanding of the barriers to big data adoption can provide policymakers and practitioners with a roadmap for enhancing disaster risk resilience and addressing challenges associated with the adoption of big data.

How to Cite

Gül, S., & Yangınlar, G. (2025). OVERCOMING BARIERS TO BIG DATA ADOPTION: STRATEGIC SOLUTIONS FOR STRENGTHENING DISASTER RISK RESILIENCE IN HUMANITARIAN SUPPLY CHAINS . International Journal of the Analytic Hierarchy Process, 17(2). https://doi.org/10.13033/ijahp.v17i2.1328

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Keywords

Disaster risk resiliences, barriers to big dat adoption, humanitarian supply chains, AHP, Fermatean fuzzy sets, Borda function

References
Aflaki, A., & Pedraza‐Martinez, A.J. (2016). Humanitarian funding in a multi‐donor market with donation uncertainty. Production and Operations Management, 25(7), 1274-1291. https://doi.org/10.1111/poms.12563

Agarwal, S., Kant, R., & Shankar, R. (2019). Humanitarian supply chain management frameworks: A critical literature review and framework for future development. Benchmarking: An International Journal, 26(6), 1749-1780. https://doi.org/10.1108/bij-08-2018-0245

Alkan, N., & Kahraman, C. (2023). Prioritization of supply chain digital transformation strategies using multi-expert Fermatean Fuzzy Analytic Hierarchy Process. Informatica, 34(1), 1-33. https://doi.org/10.15388/22-infor493

Altay, N., & Pal, R. (2014). Information diffusion among agents: Implications for humanitarian operations. Production and Operations Management, 23(6), 1015-1027. https://doi.org/10.1111/poms.12102

Atanassov, K.T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20, 87-96. https://doi.org/10.1016/s0165-0114(86)80034-3

Aydoğan, H., & Ozkir, V. (2024). A Fermatean fuzzy MCDM method for selection and ranking Problems: Case studies. Expert Systems with Applications, 237, 121628. https://doi.org/10.1016/j.eswa.2023.121628

Ayyildiz, E. (2022). Fermatean fuzzy step-wise Weight Assessment Ratio Analysis (SWARA) and its application to prioritizing indicators to achieve sustainable development goal-7. Renewable Energy, 193, 136-148. https://doi.org/10.1016/j.renene.2022.05.021

Bag, S., Dhamija, P., Luthra, S., & Huisingh, D. (2023). How big data analytics can help manufacturing companies strengthen supply chain resilience in the context of the COVID-19 pandemic. The International Journal of Logistics Management, 34(4), 1141-1164. https://doi.org/10.1108/ijlm-02-2021-0095

Bahrami, M., & Shokouhyar, S. (2022). The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: a dynamic capability view. Information Technology & People, 35(5), 1621-1651. https://doi.org/10.1108/itp-01-2021-0048

Bakioğlu, G., & Atahan, A.O. (2021). Evaluating the influencing factors on adoption of self-driving vehicles by using Interval-Valued Pythagorean Fuzzy AHP. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I., Cebi, S., Tolga, A. (eds) Intelligent and fuzzy techniques: Smart and innovative solutions. INFUS 2020. Advances in Intelligent Systems and Computing, vol 1197. Springer, Cham. https://doi.org/10.1007/978-3-030-51156-2_58

Balcik, B., Beamon, B.M., & Smilowitz, K. (2008). Last mile distribution in humanitarian relief. Journal of Intelligent Transportation Systems, 12(2), 51-63. https://doi.org/10.1080/15472450802023329

Balcik, B., Beamon, B.M., Krejci, C.C., Muramatsu, K.M., & Ramirez, M. (2010). Coordination in humanitarian relief chains: Practices, challenges and opportunities. International Journal of Production Economics, 126(1), 22-34. https://doi.org/10.1016/j.ijpe.2009.09.008

Bealt, J., Fernández Barrera, J.C., & Mansouri, S.A. (2016). Collaborative relationships between logistics service providers and humanitarian organizations during disaster relief operations. Journal of Humanitarian Logistics and Supply Chain Management, 6(2), 118-144. https://doi.org/10.1108/jhlscm-02-2015-0008

Behl, A., & Dutta, P. (2019). Humanitarian supply chain management: a thematic literature review and future directions of research. Annals of Operations Research, 283(1), 1001-1044. https://doi.org/10.1007/s10479-018-2806-2

Bouraima, M.B., Gore, A., Ayyildiz, E., Yalcin, S., Badi, I., Kiptum, C.K., & Qiu, Y. (2023). Assessing of causes of accidents based on a novel integrated interval-valued Fermatean fuzzy methodology: towards a sustainable construction site. Neural Computing and Applications, 35, 21725-21750. https://doi.org/10.1007/s00521-023-08948-5

Bozbura, F.T., Beskese, A., & Kahraman, C. (2007). Prioritization of human capital measurement indicators using fuzzy AHP. Expert Systems with Applications, 32(4), 1100-1112. https://doi.org/10.1016/j.eswa.2006.02.006

Brown, N. A., Rovins, J. E., Feldmann-Jensen, S., Orchiston, C., & Johnston, D. (2017). Exploring disaster resilience within the hotel sector: A systematic review of literature. International Journal of Disaster Risk Reduction, 22, 362-370. https://doi.org/10.1016/j.ijdrr.2017.02.005

Burkart, C., Besiou, M., & Wakolbinger, T. (2016). The funding—Humanitarian supply chain interface. Surveys in Operations Research and Management Science, 21(2), 31-45. https://doi.org/10.1016/j.sorms.2016.10.003

Camci, A., Ertürk, M.E., & Gül, S. (2022). A novel Fermatean Fuzzy Analytic Hierarchy Process proposition and its usage for supplier selection problem in Industry 4.0 transition. In Garg, H., (Ed.) q-Rung orthopair fuzzy sets, (pp. 405-437). Springer, Singapore. https://doi.org/10.1007/978-981-19-1449-2_16

Cao, Ci, Liu, Y., Tang, O. and Gao, X. (2021). A fuzzy bi-level optimization model for multi-period postdisaster relief distribution in sustainable humanitarian supply chains. International Journal of Production Economics, 235, 108081. https://doi.org/10.1016/j.ijpe.2021.108081

Deepika, M. (2023). Selection of ideal supplier in e-procurement for manufacturing industry using intuitionistic fuzzy AHP. International Journal of Business Performance and Supply Chain Modelling, 14(1), 56-78. https://doi.org/10.1504/ijbpscm.2023.130484

Delmonteil, F.X., & Rancourt, M.È. (2017). The role of satellite technologies in relief logistics. Journal of Humanitarian Logistics and Supply Chain Management, 7(1), 57-78. https://doi.org/10.1108/jhlscm-07-2016-0031

Deveci, M., Varouchakis, E.A., Brito-Parada, P.R., Mishra, A.R., Rani, P., Bolgkoranou, M., & Galetakis, M. (2023). Evaluation of risks impeding sustainable mining using Fermatean fuzzy score function based SWARA method. Applied Soft Computing, 139, 110220. https://doi.org/10.1016/j.asoc.2023.110220

Dubey, R., Altay, N., & Blome, C. (2019). Swift trust and commitment: The missing links for humanitarian supply chain coordination? Annals of Operations Research, 283, 159-177. https://doi.org/10.1007/s10479-017-2676-z

Dubey, R., Bryde, D. J., Dwivedi, Y. K., Graham, G., & Foropon, C. (2022). Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view. International Journal of Production Economics, 250, 108618. https://doi.org/10.1016/j.ijpe.2022.108618

Dubey, R., & Gunasekaran, A. (2016). The sustainable humanitarian supply chain design: agility, adaptability and alignment. International Journal of Logistics Research and Applications, 19(1), 62-82. https://doi.org/10.1080/13675567.2015.1015511

Duleba, S., Alkharabsheh, A., & Gündoğdu, F.K. (2022). Creating a common priority vector in intuitionistic fuzzy AHP: a comparison of entropy-based and distance-based models. Annals of Operations Research, 318(1), 163-187. https://doi.org/10.1007/s10479-021-04491-5

Fontainha, T.C., Leiras, A., de Mello Bandeira, R.A., & Scavarda, L.F. (2017). Public-private-people relationship stakeholder model for disaster and humanitarian operations. International Journal of Disaster Risk Reduction, 22, 371-386. https://doi.org/10.1016/j.ijdrr.2017.02.004

Gao, F., Han, M., Wang, S., & Gao, J. (2024). A novel Fermatean fuzzy BWM-VIKOR based multi-criteria decision-making approach for selecting health care waste treatment technology. Engineering Applications of Artificial Intelligence, 127, 107451. https://doi.org/10.1016/j.engappai.2023.107451

Gavidia, J.V. (2017). A model for enterprise resource planning in emergency humanitarian logistics. Journal of Humanitarian Logistics and Supply Chain Management, 7(3), 246-265. https://doi.org/10.1108/jhlscm-02-2017-0004

Gonzales, G., Costan, F., Suladay, D., Gonzales, R., Enriquez, L., Costan, E., Atibing, N.M., Aro, J.L., Evangelista, S.S., Maturan, F., Selerio, Jr., E., & Ocampo, L. (2022). Fermatean Fuzzy DEMATEL and MMDE algorithm for modelling the Barriers of Implementing Education 4.0: Insights from the Philippines. Applied Sciences, 12, 689. https://doi.org/10.3390/app12020689

Görçün, Ö.F., Aytekin, A., Korucuk, S., & Tirkolaee, E.B. (2023). Evaluating and selecting sustainable logistics service providers for medical waste disposal treatment in the healthcare industry. Journal of Cleaner Production, 408, 137194. https://doi.org/10.1016/j.jclepro.2023.137194

Grange, R., Heaslip, G., & McMullan, C. (2020). Coordination to choreography: the evolution of humanitarian supply chains. Journal of Humanitarian Logistics and Supply Chain Management, 10(1), 21-44. https://doi.org/10.1108/jhlscm-12-2018-0077

Hunt, K., Narayanan, A. & Zhuang, J. (2022). Blockchain in humanitarian operations management: A review of research and practice. Socio-Economic Planning Sciences, 80, 101175. https://doi.org/10.1016/j.seps.2021.101175

Hwang, C.L., & Lin, M.J. (1987). Group decision making under multiple criteria: Methods and applications. Berlin, New York: Springer-Verlag.

Iftikhar, A., Purvis, L., Giannoccaro, I., & Wang, Y. (2022). The impact of supply chain complexities on supply chain resilience: The mediating effect of big data analytics. Production Planning & Control, 34(16), 1562-1582. https://doi.org/10.1080/09537287.2022.2032450

Ilbahar, E., Karasan, A., Cebi, S., & Kahraman, C. (2018). A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Safety Science, 103, 124-136. https://doi.org/10.1016/j.ssci.2017.10.025

Jahre, M. (2017). Humanitarian supply chain strategies–a review of how actors mitigate supply chain risks. Journal of Humanitarian Logistics and Supply Chain Management, 7(2), 82-101. https://doi.org/10.1108/jhlscm-12-2016-0043

Jeble, S., Kumari, S., Venkatesh, V.G., & Singh, M. (2020). Influence of big data and predictive analytics and social capital on performance of humanitarian supply chain: Developing framework and future research directions. Benchmarking: An International Journal, 27(2), 606-633. https://doi.org/10.1108/bij-03-2019-0102

Kabak, Ö., & Ervural, B. (2017). Multiple attribute group decision making: A generic conceptual framework and a classification scheme. Knowledge-Based Systems, 123, 13-30. https://doi.org/10.1016/j.knosys.2017.02.011

Kabra, G., Ramesh, A., Jain, V., & Akhtar, P. (2023). Barriers to information and digital technology adoption in humanitarian supply chain management: a fuzzy AHP approach. Journal of Enterprise Information Management, 36(2), 505-527. https://doi.org/10.1108/jeim-10-2021-0456

Karasan, A., Ilbahar, E., & Kahraman, C. (2019). A novel Pythagorean fuzzy AHP and its application to landfill site selection problem. Soft Computing, 23, 10953-10968. https://doi.org/10.1007/s00500-018-3649-0

Kapucu, N., Garayev, V., & Wang, X. (2013). Sustaining networks in emergency management: A study of counties in the United States. Public Performance & Management Review, 37(1), 104-133. https://doi.org/10.2753/pmr1530-9576370105

Karuppiah, K., Sankaranarayanan, B., Ali, S.M., AlArjani, A., & Mohamed, A. (2022). Causality analytics among key factors for green economy practices: Implications for sustainable development goals. Frontiers in Environmental Science, 10, 933657. https://doi.org/10.3389/fenvs.2022.933657

Korucuk, S., Aytekin, A., Ecer, F., Karamaşa, Ç., & Zavadskas, E.K. (2022). Assessing green approaches and digital marketing strategies for twin transition via Fermatean Fuzzy SWARA-COPRAS. Axioms, 11, 709. https://doi.org/10.3390/axioms11120709

Kovács, G., & Tatham, P. (2010). What is special about a humanitarian logistician? A survey of logistic skills and performance. Supply Chain Forum: An International Journal, 11(3), 32-41. https://doi.org/10.1080/16258312.2010.11517238

Kumar, P., & Singh, R.K. (2022). Application of Industry 4.0 technologies for effective coordination in humanitarian supply chains: a strategic approach. Annals of Operations Research, 319(1), 379-411. https://doi.org/10.1007/s10479-020-03898-w

Kutlu Gündoğdu, F., & Kahraman, C. (2020). A novel spherical fuzzy analytic hierarchy process and its renewable energy application. Soft Computing, 24, 4607-4621. https://doi.org/10.1007/s00500-019-04222-w

Lahane, S., & Kant, R. (2023). Investigating the circular supply chain implementation challenges using Pythagorean Fuzzy AHP approach. Materials Today: Proceedings, 72(3), 1158-1163. https://doi.org/10.1016/j.matpr.2022.09.189

Maghsoudi, A., Zailani, S., Ramayah, T., & Pazirandeh, A. (2018). Coordination of efforts in disaster relief supply chains: The moderating role of resource scarcity and redundancy. International Journal of Logistics-Research and Applications, 21, 407–430. https://doi.org/10.1080/13675567.2018.1437894

Marie-Allen, A., Kovács, G., Masini, A., Vaillancourt, A., & Van Wassenhove, L. (2013). Exploring the link between the humanitarian logistician and training needs. Journal of Humanitarian Logistics and Supply Chain Management, 3(2), 129-148. https://doi.org/10.1108/jhlscm-10-2012-0033

Moktadir, M.A., & Ren, J. (2023). Modeling challenges for Industry 4.0 implementation in new energy systems towards carbon neutrality: Implications for impact assessment policy and practice in emerging economies. Resources, Conservation, and Recycling, 199, 107246. https://doi.org/10.1016/j.resconrec.2023.107246

Moshtari, M., & Gonçalves, P. (2017). Factors influencing interorganizational collaboration within a disaster relief context. VOLUNTAS: International Journal of Voluntary and Non-profit Organizations, 28, 1673-1694. https://doi.org/10.1007/s11266-016-9767-3

Oloruntoba, R., & Gray, R. (2009). Customer service in emergency relief chains. International Journal of Physical Distribution & Logistics Management, 39(6), 486-505. https://doi.org/10.1108/09600030910985839

Ortiz-Barrios, M.A., Madrid-Sierra, S.L., Petrillo, A., & Quezada, L.E. (2023). A novel approach integrating IF-AHP, IF-DEMATEL and CoCoSo methods for sustainability management in food digital manufacturing supply chain systems. Journal of Enterprise Information Management, 0, 0. https://doi.org/10.1108/jeim-04-2023-0199

Patil, A., Shardeo, V., & Madaan, J. (2021). Modelling performance measurement barriers of humanitarian supply chain. International Journal of Productivity and Performance Management, 70(8), 1972-2000. https://doi.org/10.1108/ijppm-01-2020-0031

Pettit, S., & Beresford, A. (2009). Critical success factors in the context of humanitarian aid supply chains. International Journal of Physical Distribution & Logistics Management, 39(6), 450-468. https://doi.org/10.1108/09600030910985811

Prasad, S., Zakaria, R., & Altay, N. (2018). Big data in humanitarian supply chain networks: A resource dependence perspective. Annals of Operations Research, 270(1), 383-413. https://doi.org/10.1007/s10479-016-2280-7

ReliefWeb. (2024 September). The World Risk Report. UN Office for the Coordination of Humanitarian Affairs. https://reliefweb.int/report/world/worldriskreport-2024-focus-multiple-crises.

Saaty, T.L. (1980). The Analytic Hierarchy Process. Pittsburgh, PA: RWS Publications.

Saaty, T.L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9-26. https://doi.org/10.1016/0377-2217(90)90057-i

Saja, A. A., Goonetilleke, A., Teo, M., & Ziyath, A. M. (2019). A critical review of social resilience assessment frameworks in disaster management. International Journal of Disaster Risk Reduction, 35, 101096. https://doi.org/10.1016/j.ijdrr.2019.101096

Santarelli, G., Abidi, H., Klumpp, M., & Regattieri, A. (2015). Humanitarian supply chains and performance measurement schemes in practice. International Journal of Productivity and Performance Management, 64(6), 784-810. https://doi.org/10.1108/ijppm-11-2013-0185

Sarker, M. N. I., Peng, Y., Yiran, C., & Shouse, R. C. (2020). Disaster resilience through big data: Way to environmental sustainability. International Journal of Disaster Risk Reduction, 51, 101769. https://doi.org/10.1016/j.ijdrr.2020.101769

Seikh, M.R., & Mandal, U. (2023). Interval-valued Fermatean fuzzy Dombi aggregation operators and SWARA based PROMETHEE II method to bio-medical waste management. Expert Systems with Applications, 226, 120082. https://doi.org/10.1016/j.eswa.2023.120082

Senapati, T., & Yager, R.R. (2019a). Some new operations over Fermatean fuzzy numbers and application of Fermatean fuzzy WPM in Multiple Criteria Decision Making. Informatica, 30(2), 391-412. https://doi.org/10.15388/informatica.2019.211

Senapati, T., & Yager, R.R. (2019b). Fermatean fuzzy weighted averaging/geometric operators and its application in multi-criteria decision-making methods. Engineering Applications of Artificial Intelligence, 85, 112-121. https://doi.org/10.1016/j.engappai.2019.05.012

Senapati, T., & Yager, R.R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing, 11, 663-674. https://doi.org/10.1007/s12652-019-01377-0

Sharma, V., Kumar, A., & Kumar, M. (2021). A framework based on BWM for big data analytics (BDA) barriers in manufacturing supply chains. Materials Today: Proceedings, 47, 5515-5519. https://doi.org/10.1016/j.matpr.2021.03.374

Shete, P.C., Ansari, Z.N., & Kant, R. (2020). A Pythagorean fuzzy AHP approach and its application to evaluate the enablers of sustainable supply chain innovation. Sustainable Production and Consumption, 23, 77-93. https://doi.org/10.1016/j.spc.2020.05.001

Singh, R.K., Gupta, A., & Gunasekaran, A. (2018). Analysing the interaction of factors for resilient humanitarian supply chain. International Journal of Production Research, 56(21), 6809-6827. https://doi.org/10.1080/00207543.2018.1424373

Stewart, G.T., Kolluru, R., & Smith, M. (2009). Leveraging public‐private partnerships to improve community resilience in times of disaster. International Journal of Physical Distribution & Logistics Management, 39(5), 343-364. https://doi.org/10.1108/09600030910973724

Sun, Y., Zhou, X., Yang, C., & Huang, T. (2023). A visual analytics approach for multi-attribute decision making based on intuitionistic fuzzy AHP and UMAP. Information Fusion, 96, 269-280. https://doi.org/10.1016/j.inffus.2023.03.019

Tint, B.S., McWaters, V., & van Driel, R. (2015). Applied improvisation training for disaster readiness and response: Preparing humanitarian workers and communities for the unexpected. Journal of Humanitarian Logistics and Supply Chain Management, 5(1), 73-94. https://doi.org/10.1108/jhlscm-12-2013-0043

Tomasini, R., & van Wassenhove, L. (2009). Humanitarian logistics. Springer.

Vogt, M., Hertweck, D., & Hales, K. (2011). Strategic ICT alignment in uncertain environments: an empirical study in emergency management organizations. In 2011 44th Hawaii International Conference on System Sciences (pp. 1-11). IEEE. https://doi.org/10.1109/hicss.2011.387

Van Wassenhove, L.N. (2006). Humanitarian aid logistics: supply chain management in high gear. Journal of the Operational Research Society, 57(5), 475-489. https://doi.org/10.1057/palgrave.jors.2602125

Wakolbinger, T., & Toyasaki, F. (2014). Impacts of funding systems on humanitarian operations. In Humanitarian logistics: Meeting the challenge of preparing and responding to disasters and complex emergencies (pp. 21-24). Kogan Page Limited.

Wei, D., Meng, D., Rong, Y., Liu, Y., Garg, H., & Pamucar, D. (2022). Fermatean fuzzy Schweizer–Sklar operators and BWM-Entropy-Based combined compromise solution approach: An application to green supplier selection. Entropy, 24, 776. https://doi.org/10.3390/e24060776

Wild, N., & Zhou, L. (2011). Ethical procurement strategies for international aid non‐government organisations. Supply Chain Management: An International Journal, 16(2), 110-127. https://doi.org/10.1108/13598541111115365

World Health Organization (WHO). (2023 February). Earthquake response in Türkiye and Whole of Syria. https://www.who.int/publications/m/item/who-flash-appeal--earthquake-response-in-t-rkiye-and-whole-of-syria

Xu, Z., & Liao, H. (2014). Intuitionistic fuzzy analytic hierarchy process. IEEE Transactions on Fuzzy Systems, 22(4), 749-761. https://doi.org/10.1109/tfuzz.2013.2272585

Yadav, D.K., & Barve, A. (2015). Analysis of critical success factors of humanitarian supply chain: An application of Interpretive Structural Modeling. International Journal of Disaster Risk Reduction, 12, 213-225. https://doi.org/10.1016/j.ijdrr.2015.01.008

Yager, R.R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems, 22(4), 958-965. https://doi.org/10.1109/tfuzz.2013.2278989

Yesilcayir, N., Ayvazoglu, G., Celik, S., & Peker, I. (2024). Transit warehouse location selection by IF AHP- TOPSIS integrated methods for disaster logistics: A case study of Turkey. Research in Transportation Business and Management, 57, 101232. https://doi.org/10.1016/j.rtbm.2024.101232

Zadeh, L.A. (1965). Fuzzy sets. Information & Control, 8, 338-353.

Zeng, S., Chen, W., Gu, J., & Zhang, E. (2023). An integrated EDAS model for Fermatean fuzzy multi-attribute group decision making and its application in green-supplier selection. Systems, 11, 162. https://doi.org/10.3390/systems11030162

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