Assessing organizational efficiency in AI-based GHRM using fuzzy SWARA and MOORA mathematical modeling - Kumar, Nitendra; Agarwal, Reema; Sharma, Neeti; Alam, Khursheed; Agrawal, Ankur (Frontiers in Applied Mathematics and Statistics, 2025)
IntroductionThis study explores the shift in Green Human Resource Management (GHRM) through Artificial Intelligence (AI) adoption by looking at sustainability-driven practices and assessing how they affect organizational eco-efficiency in six different companies in order to run them efficiently and effectively. This research paper evaluates the efficiency of six companies in implementing AI-GHRM practices using ten key criteria.MethodsTo ensure a robust and structured decision-making process under conditions of uncertainty, two prominent fuzzy Multi-Criteria Decision-Making (MCDM) mathematical modeling—Fuzzy Step-wise Weight Assessment Ratio Analysis (F-SWARA) and Fuzzy Multi-Objective Optimization on the basis of Ratio Analysis (F-MOORA) are applied in a fuzzy environment. Applying linguistic factors to account for the subjectivity and ambiguity of human evaluations, the SWARA approach is used to calculate the relative relevance of the ten AI-GHRM criteria based on buying managers’ judgments expressed in terms of triangular fuzzy numbers. These criteria weights are then used in the fuzzy MOORA mathematical modeling method to rank the companies in terms of their overall efficiency in AI-GHRM adoption.Results and discussionsThe results provide a comprehensive ranking of the companies, highlighting best practices and offering insights into strategic areas for improvement. This paper offers a unique hybrid paradigm for performance evaluation under fuzzy settings, advancing both academic and practical knowledge of sustainable HRM integration with AI technology. The findings of the paper are that the fifth company was placed first.