TY - JOUR
T1 - A novel Pythagorean fuzzy correlation coefficient based on Spearman's technique of correlation coefficient with applications in supplier selection process
AU - Ejegwa, Paul Augustine
AU - Kausar, Nasreen
AU - Aydin, Nezir
AU - Deveci, Muhammet
N1 - Publisher Copyright:
© 2024 Elsevier Inc.
PY - 2024/12/30
Y1 - 2024/12/30
N2 - A Pythagorean fuzzy correlation coefficient (PFCC) is a reliable approach for eliminating ambiguity during the measure of relationships. Numerous Pythagorean fuzzy correlation coefficient methods (PFCCMs) have been constructed using Pearson's correlation coefficient technique. In this study, a new PFCCM is constructed based on Spearman's correlation coefficient to eliminate all possible uncertainties that may impede decision-makers from making a dependable selection. To validate the construction of a new PFCCM, we examine the existing PFCCMs and pinpoint their inadequacies. Among the extant PFCCMs, one approach was constructed through Spearman's correlation coefficient but it does not takes into cognizance the properties of the PFSs. In addition, it sometimes fails the axiomatic conditions of the PFCC, and yields invalid result for PFSs that are defined on a singleton set. These setbacks justify the construction of a new Spearman's correlation coefficient-like PFCCM, which is shown to overcome the limitations of the extant PFCCMs. Equally, the strength of the new PFCCM is verified by some theoretical results, and it fulfills the conditions of PFCC. Additionally, the use of the novel PFCCM is discussed in the solution of supplier selection problems to eliminate supplier selection ambiguity through the multiple criteria decision-making (MCDM) approach. To unarguably show the intrinsic worth of the new PFCCM, the effectiveness of the new PFCCM is compared with the existing PFCCMs and it is observed that the new PFCCM is reliable, consistent and precise, and in the same way satisfies the axioms of the PFCC. In particular, the existing Spearman's PFCCM yields ∞ in Example 4, while the new PFCCM produces 0.7603, which justifies the construction of a new Spearman's PFCCM. Finally, it is found that the new approach can suitably handle the hesitancies associated with the art of selection.
AB - A Pythagorean fuzzy correlation coefficient (PFCC) is a reliable approach for eliminating ambiguity during the measure of relationships. Numerous Pythagorean fuzzy correlation coefficient methods (PFCCMs) have been constructed using Pearson's correlation coefficient technique. In this study, a new PFCCM is constructed based on Spearman's correlation coefficient to eliminate all possible uncertainties that may impede decision-makers from making a dependable selection. To validate the construction of a new PFCCM, we examine the existing PFCCMs and pinpoint their inadequacies. Among the extant PFCCMs, one approach was constructed through Spearman's correlation coefficient but it does not takes into cognizance the properties of the PFSs. In addition, it sometimes fails the axiomatic conditions of the PFCC, and yields invalid result for PFSs that are defined on a singleton set. These setbacks justify the construction of a new Spearman's correlation coefficient-like PFCCM, which is shown to overcome the limitations of the extant PFCCMs. Equally, the strength of the new PFCCM is verified by some theoretical results, and it fulfills the conditions of PFCC. Additionally, the use of the novel PFCCM is discussed in the solution of supplier selection problems to eliminate supplier selection ambiguity through the multiple criteria decision-making (MCDM) approach. To unarguably show the intrinsic worth of the new PFCCM, the effectiveness of the new PFCCM is compared with the existing PFCCMs and it is observed that the new PFCCM is reliable, consistent and precise, and in the same way satisfies the axioms of the PFCC. In particular, the existing Spearman's PFCCM yields ∞ in Example 4, while the new PFCCM produces 0.7603, which justifies the construction of a new Spearman's PFCCM. Finally, it is found that the new approach can suitably handle the hesitancies associated with the art of selection.
KW - Correlation coefficient
KW - Multiple criteria decision-making
KW - Pythagorean fuzzy sets
KW - Supplier selection
UR - https://www.scopus.com/pages/publications/85213967549
U2 - 10.1016/j.jii.2024.100762
DO - 10.1016/j.jii.2024.100762
M3 - Article
AN - SCOPUS:85213967549
SN - 2452-414X
VL - 44
JO - Journal of Industrial Information Integration
JF - Journal of Industrial Information Integration
M1 - 100762
ER -