Needs analysis demonstrates role-specific linguistic variations across textile supply chains, suggesting tailored curricula enhance workplace communication efficiency.
The English usage scenarios in global supply chain management are highly specialized and context-dependent, resulting in a mismatch between existing English course content and actual workplace language requirements. Based on corpus linguistics and task-based needs analysis theory, this study develops a demand analysis framework that integrates language data-driven and task-oriented principles to transform industry language requirements into curriculum objectives through systematic job-task identification. The results show that communication competence accounts for 37.9% of the language requirements in procurement positions, while comprehension competence reaches 46.8% in production positions, demonstrating significant differences in language priorities across supply chain functions. Furthermore, the analysis reveals distinctive syntactic structures and modal expression patterns in procurement, production, and sales discourse, illustrating how conditional constructions, obligation modals, and evaluative expressions support different communicative purposes in professional contexts. By providing an objective and structured linguistic validation mechanism, the proposed framework establishes an evidence-based pathway for ESP curriculum development and offers methodological support for semantic information organization, intelligent communication, and knowledge transmission in digitally interconnected engineering systems, including electromagnetic information networks where accurate context-aware information exchange is increasingly essential.
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Li et al. (2026) studied this question.
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