A Novel Framework for Global Business Partner Selection Using \(p,q\)-Quasi-Rung Orthopair Normal Fuzzy Sets

In today's interconnected global economy, businesses encounter complex challenges that necessitate sophisticated strategies to maintain competitiveness. One critical challenge is selecting a suitable global business partner, a decision often complicated by high levels of uncertainty and incomplete information. To address these challenges, this paper introduces the concept of $p,q$-QRONFSs, which combine the advantages of $p,q$-quasi-rung orthopair fuzzy sets and normal fuzzy numbers. The proposed $p,q$-QRONFS framework is particularly effective in dealing with high uncertainty, providing a more human-like approach to decision-making. We develop novel weighted averaging and geometric operators for $p,q$-QRONFNs that incorporate the decision-maker's confidence level, enhancing the robustness of the decision-making process. A multiple attribute decision-making (MADM) strategy based on these operators is proposed, demonstrating superior performance compared to existing methods. The practical application of the $p,q$-QRONFMADM approach is illustrated through a case study where an organization seeks to identify an optimal global business partner. Finally, a comparative analysis with other methodologies highlights the effectiveness and superiority of the proposed approach