How to promote the quality of technical education? A fuzzy-set qualitative analysis of 16 technical colleges in China - Fan Zhang; Yile Liu; Lianhua Fan (PLoS ONE, 2025)
This study focuses on how technical and vocational education and training (TVET) institutions can effectively enhance educational quality to cultivate a globally competitive industrial workforce, addressing the World Banks projection that over 1.1 billion jobs worldwide will undergo skill transformations in the next decade due to automation, digitalization, and the green economy transition. Employing the fuzzy-set qualitative comparative analysis (fsQCA) method, the research conducts an in-depth analysis of developmental data from 16 Chinese TVET institutions to identify key factors influencing their competitiveness. The findings reveal that social service constitutes the foundation of high-quality vocational education. The social service capacity of TVET institutions is primarily reflected in vocational skill training, aligning with the core philosophy of Singapore Polytechnic’s “SkillsFuture” initiative. Through data analysis, a “social service-driven” development mechanism is identified: under similar conditions, TVET institutions achieve high-quality development by participating in government-funded vocational training programs. Simultaneously, two types of developmental bottlenecks are uncovered: (1) the “student skill level-international exchange constraints” type, where limited student proficiency and international collaboration hinder institutional progress; and (2) the “social service-technological R&D constraints” type, where weak social service delivery and technology transfer capabilities act as critical barriers. The outcomes provide a robust reference for global TVET stakeholders and policymakers to optimize industrial talent cultivation strategies and deepen integration into global value chains. The methodological framework also holds transferability to other domains, enabling regions to pinpoint success pathways and avoid ineffective measures.