Page 100 - UKZN Proceedings of the Conference Report

Ljubisavljević and Samčović 2024). Other examples are in the selection of cloud computing and telecommunications service providers (Mostafa 2021; Tanoumand et al. 2017; Niemcewicz 2021), in the prioritisation and management of natural resources (Abdul and Wenqi 2024; Doke et al. 2021), in the performance assessment of public transport (Daimi and Rebai, 2023), in environmental impact assessment (Dano 2020), and in advancing digital innovation in public administration (Seo and Myeong 2020). The AHP has also been used in validation assignments, such as in a recent study commissioned by UNICEF (Godfrey et al. 2022). Further evidence of an upward trend in the use of the AHP/ANP method in collaborative decision-making can be found in the Expert Choice AHP Software (Expert Choice Solutions 2023), in Super Decisions AHP Software (Creative Decisions Foundation 2023), and MakeItRational AHP Software (TransparentChoice 2024).
theoretical framework
Scientific decision-making techniques are generally modelled around subjective judgements (Fischhoff and Broomell 2020). Human judgements naturally involve
comparisons, as is integral in the most popular AHP/ ANP MCDM method (Sotoudeh-Anvari 2022). The quality of judgement can be scientifically evaluated by its consistency. Studies have established that the human elements that come into play during decision-making are important determinants of the final decision (Gratian et al. 2018). It is therefore important that they are captured using proven scientific techniques like paired comparisons that will guarantee consistency (Pant et al. 2022; Yuan et al. 2023). Consistency is undoubtedly the most desirable element in decision-making.
AHP has been regarded as being easy to use, and having a high degree of mathematical stability (Vinogradova- Zinkevič et al. 2021). A 2022 study reported that the popularity of AHP among the MCDM methods is at 37.5% (Sotoudeh-Anvari 2022). Other recent studies have reported its popularity globally and suitability across disciplines (Stofkova et al. 2022; Singh and Pant 2021). Figure 3 shows the theoretical structure of AHP and its generalisation the ANP.
C=criteria,
Xij=weights of decision factors in level i with respect to level j relative to the goal.
Level 1: The goal of the decision problem is established.
Level 2: Elicitation of the main criteria in the case of AHP and sub-criteria in the case of ANP. The criteria in level 2 are weighted with respect to the goal in level 1 (x21).
Level 3: A suitable alternative is chosen based on the weighting of criteria in level 2 (x32).
Figure 3: Theoretical Structure of (a) AHP, and (b) ANP
98 | Proceedings of the conference on Public innovation, develoPment and sustainability