An intelligent single valued neutrosophic MCDM framework for Business English language analysis curriculum planning and pedagogical support under uncertainty

Business English communication is a fundamental issue in the success of organizations in the modern globalized business world. Nevertheless, the analysis and selection of the most suitable Business English training strategies involve various qualitative and unclear aspects, which are not always well-managed through traditional decision-making (DM) methods. In an effort to fill this gap, the present research paper proposes a Single-Valued Neutrosophic fuzzy set (SVNFS) Rangement Et Synthèse De Données Relationnelles (ORESTE) Qualitative Flexible Multiple Criteria Method (QUALIFLEX) framework to support the analysis and DM process of Business English as a language in its entirety. The framework combines the ranking-based ORESTE technique with the outranking-based QUALIFLEX technique within a SVN environment, allowing for more accurate modeling of indeterminacy and expert hesitation in the evaluation process. The suggested methodology is capable of systematizing expert opinions into a format, standardizing linguistic measurements into SVN values, and calculating the degrees of importance to generate objective values of alternatives.The practical usefulness of the framework is explained by a structured case-study of the design of best Business English training strategies, which is based on the carefully designed hypothetical data, which is rather similar to the evaluation circumstances that may take place in the real world.Its capacity to deal with expression, facilitate group decision making, and provide consistent and reasonable results are pointed out by the results. The new model in decision support offers educators, policymakers and business organizations with an effective tool to improve language training strategies in complex and uncertain situations.