This study proposes novel extensions to overcome the limitations of classical aggregation methods, namely ordered weighted averaging (OWA) and probabilistic OWA (POWA) operators, in handling hierarchical or subdivided attributes under uncertainty within the hypersoft set (HS) framework, resulting in the hypersoft set-based OWA (HS-OWA) and hypersoft set-based POWA (HS-POWA) operators. These extensions (HS-OWA and HS-POWA operators) preserve sub-attribute information, enhance decision accuracy, and handle uncertainty, including fuzzy, intuitionistic, and neutrosophic data. We formalize the mathematical definitions and theoretical properties of HS-OWA and HS-POWA, demonstrating their practical applicability through a case study of sustainable wastewater treatment method selection. Additionally, we generalize the proposed operators under various fuzzy extensions, including intuitionistic fuzzy sets (IFS), pythagorean fuzzy sets (PFS), q-Rung orthopair fuzzy sets (q-ROFS), and neutrosophic sets (NS), allowing flexible modeling of uncertainty, hesitation, and conflict in expert assessments. The results from our study validate the superiority of the proposed framework in aggregating distributed evaluations while preserving semantic depth and interoperability. The proposed operators are effective in complex multi-criteria and group decision-making problems, such as sustainable technology assessment and policy-making, and provide a robust framework for future research in dynamic and large-scale MCDM applications.

The ordered weighted averaging (OWA) operator is a fundamental tool in decision-making processes, particularly under conditions of uncertainty, by aggregating inputs through a reordering mechanism based on predefined weights. Despite its utility, the classical OWA operator is limited in addressing complex decision scenarios characterized by uncertainty, indeterminacy, and inconsistency. This study introduces two innovative aggregation operators, the induced ordered weighted averaging operator under a neutrosophic environment (IOWAN) and its generalized form (GIOWAN), which integrate the ordering flexibility of induced OWA with the expressive power of neutrosophic sets. The proposed operators advance existing models by (i) enabling simultaneous aggregation of truth, indeterminacy, and falsity information, (ii) incorporating application-driven inducing functions for dynamic ordering, and (iii) offering a unified framework encompassing arithmetic, geometric, harmonic, quadratic, and extreme-case induced operators. We formally define the operators, establish their mathematical properties, and present several novel extensions, including interval-valued, bipolar, probabilistic, entropy-based, and time-dynamic neutrosophic versions. An illustrative case study of the economic assessment of Southeast Asian countries was performed using a methodology based on (GIOWAN) Operators and results show that rankings align with real-world economic data. Comparative analyses highlight its superior performance in modeling intricate decision dynamics. The proposed algorithms are effective in a group decision-making environment within uncertain domains, solving problems of uncertainty, complexity, and multidimensional information, including sustainable development, policy formulation, and healthcare decision analysis.