Decoding Employer Demands: Reshaping Core Competencies of Fashion Graduates through Labor Market Analysis

Abstract

The digital transformation of the global fashion industry has reshaped the skill requirements of the labor market. A significant skills mismatch exists between the talent cultivation programs of higher education institutions and the realities of the actual employment market. This study aims to decode the core competency requirements of fashion industry employers through empirical data. The research adopts a content analysis methodology integrated with data mining techniques. By collecting 119 job advertisements from 13 representative apparel enterprises, the study conducted quantitative mining on 1,596 valid textual records. Data processing utilized Term Frequency-Inverse Document Frequency (TF-IDF) indexing and Latent Dirichlet Allocation (LDA) topic modeling. The analytical results indicate that enterprise demand for talent has shifted from singular professional skills toward interdisciplinary talent equipped with cross-functional proficiencies and digital acumen. High-weight feature words corroborate those digital capabilities, such as live-streaming and new media operations, have become core technical barriers within the industry. The topic model extracted two core dimensions: core job responsibilities and business operations, alongside professional skills and role competencies. Furthermore, the semantic network structure reveals that employers’ recruitment criteria are highly oriented toward practical experience and results-driven outcomes. This study proposes that higher education institutions should deeply embed cutting-edge digital technologies into professional curricula and vigorously promote work-based learning (WBL) systems. The findings provide objective data support for narrowing the industry-education supply-demand gap and reconfiguring interdisciplinary talent cultivation paradigms.