NEW DECISION MAKING MODEL FOR THE EVALUATION OF KEY CRITICAL SUCCESS FACTORS FOR THE IMPACTS OF DIGITIZATION, ARTIFICIAL INTELLIGENCE (AI), AND CYBER WARFARE ON THE HUMANITARIAN ENVIRONMENT DURING ARMED CONFLICTS

Main Article Content

MAJID KHAN, MIAN MUHAMMAD AKHTAR HAYAT

Abstract

In this article, we have proposed a novel decision making model to investigate the most important and critical success factors (CSFs) affecting the humanitarian environment due to the impacts of digitization, artificial intelligence (AI), and cyber warfare. We have proposed a hybrid decision making model by combining analytical hierarchy process (AHP) with the multi attributive border approximation area comparison (MABAC) technique to evaluate the CSFs of anticipated study. The anticipated hybrid decision making model highlights and measures the effects of different key success factors on humanitarian outcomes, enabling more informed decision-making in the context of evolving digital technologies and security threats. Our projected decision making model offers a broad methodology to deal with complexity of connections among digitalization, artificial intelligence (AI) and cyber warfare on the humanitarian environment ensuring accurate evaluation and strategic planning.

Article Details

Section
Articles

References

Khidhir, S. N. K. (2024). Digitalization of humanitarian supply chain: Opportunities and challenges for humanitarian organizations (Doctoral dissertation, LCC tarptautinis universitetas.).Hanna, P., M. A., & H. S. (2020).

Patil, A., & Madaan, J. (2024). A Study on the Research Clusters in the Humanitarian Supply Chain Literature: A Systematic Review. Logistics, 8(4), 128.

Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538(7625), 311-313.O'neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.

Partipilo, F. R., & Stroppa, M. (2023). Humanitarian organisations under cyber-attack: emerging threats and humanitarian actors’ responsibilities under international human rights law. In Responsible Behaviour in Cyberspace: Global Narratives and Practice (pp. 238-257). Publications Office of the European Union.

Al-Nassiri, M. M., & Al-Balatah, I. A. (2024, November). Enhancing Information Security Awareness Model to Advance Human Aspects in Humanitarian Organizations. In 2024 1st International Conference on Emerging Technologies for Dependable Internet of Things (ICETI) (pp. 1-8). IEEE.

Saaty, T. L. (1980). The analytic hierarchy process (AHP). The Journal of the Operational Research Society, 41(11), 1073-1076.

Wang, J., Darwis, D., Setiawansyah, S., & Rahmanto, Y. (2024). Implementation of MABAC Method and Entropy Weighting in Determining the Best E-Commerce Platform for Online Business. JiTEKH, 12(2), 58-68.

Du, J. L., Liu, S. F., Javed, S. A., Goh, M., & Chen, Z. S. (2023). Enhancing quality function deployment through the integration of rough set and ordinal priority approach: A case study in electric vehicle manufacturing. IEEE Transactions on Engineering Management, 71, 7541-7552.

Dai, J., & Azhar, A. (2024). Collaborative governance in disaster management and sustainable development. Public Administration and Development, 44(4), 358-380.

Kendra, J. M., & Wachtendorf, T. (2003). Elements of resilience after the world trade center disaster: reconstituting New York City's Emergency Operations Centre. Disasters, 27(1), 37-53.

Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International journal of services sciences, 1(1), 83-98.

Franek, J., & Kresta, A. (2014). Judgment scales and consistency measure in AHP. Procedia economics and finance, 12, 164-173.

Zardari, N. H., Ahmed, K., Shirazi, S. M., & Yusop, Z. B. (2014). Weighting methods and their effects on multi-criteria decision making model outcomes in water resources management. Springer.

Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of operational research, 169(1), 1-29.

Delice, A. P. E. K. (2017). Selection of on-site energy generation technology with a new MCDM approach using MABAC & AHP. Jaffar Beikzad and Amir Gharifard/5, 126.

Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation Area Comparison (MABAC). Expert systems with applications, 42(6), 3016-3028.

Wei, G., Wei, C., Wu, J., & Wang, H. (2019). Supplier selection of medical consumption products with a probabilistic linguistic MABAC method. International Journal of Environmental Research and Public Health, 16(24), 5082.

Božanić, D., Tešić, D., & Milić, A. (2020). Multicriteria decision making model with Z-numbers based on FUCOM and MABAC model. Decision Making: Applications in Management and Engineering, 3(2), 19-36.

Altuntas, S., Dereli, T., & Yilmaz, M. K. (2015). Evaluation of excavator technologies: application of data fusion based MULTIMOORA methods. Journal of Civil Engineering and Management, 21(8), 977-997.

Kumar, A., & Kaur, K. (2024). A Novel MCDM-Based Framework to Recommend Machine Learning Techniques for Diabetes Prediction. International Journal of Engineering & Technology Innovation, 14(1).