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Educational Technology – January 2026, volume 25 Issue 1  \nEnhancing Vocational Graduate Employability through Mobile Application on Advanced Quantitative Modeling of Skills and Partnerships  \nYang WANG  \nVocational Education Division, Faculty of Technical Education, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand.  \n[wang_ya@mail.rmutt.ac.th](wang_ya@mail.rmutt.ac.th), ORCID:0009-0000-3789-7964  \nThosporn SANGSAWANG  \nEducational Technology and Communications Division, Faculty of Technical Education, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand.  \n*corresponding author: [sthosporn@rmutt.ac.th](sthosporn@rmutt.ac.th), ORCID:0000-0002-7926-6949  \nAbstract  \nThis study investigates the multidimensional factors influencing employability among vocational students in China by applying an advanced quantitative framework. Data were collected from 17 experts, 100 faculty members, and 30 students, and analyzed using a sequential process of Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM) . EFA extracted six constructsProfessional Skills, Soft Skills, Career Guidance, Industry-Academia Collaboration, Technological Literacy, and Employability Outcomes-explaining 72.6% of total variance. CFA confirmed measurement validity and reliability (Cronbach’s α > 0.80; CR > 0.84; AVE > 0.50; HTMT \u003C 0.85) . SEM results demonstrated that all hypothesized relationships were supported, with Soft Skills (β = 0.35) identified as the strongest predictor of employability, followed by Professional Skills (β = 0.29), Technological Literacy (β = 0.24), Industry-Academia Collaboration (β = 0.21), and Career Guidance (β = 0.18) . Mediation analysis revealed that Career Guidance indirectly influenced employability through Soft Skills (β = 0.12, p \u003C 0.01), while moderation analysis confirmed that Industry-Academia Collaboration enhanced the effect of Professional Skills on employability (β = 0.09, p \u003C 0.05) . The structural model accounted for 68% of variance (R² = 0.68) in employability outcomes, demonstrating strong explanatory power. The novelty of this research lies in integrating mediation and moderation mechanisms within a validated employability model, moving beyond traditional exploratory methods. Conceptually, the findings highlighted the centrality of Soft Skills in determining employability, challenging the dominance of technical training in vocational education. Practically, the study provides evidence-based recommendations for balancing technical and soft skill training, strengthening career guidance services, and deepening industry-academia partnerships to enhance graduate competitiveness in dynamic labor markets through a Mobile Application on Advanced Quantitative Modeling of Skills and Partnerships.  \nKeyword: Industry-Academia Collaboration; Technological Literacy; Exploratory Factor Analysis; Mobile Application  \nIntroduction  \nVocational education has emerged as a crucial driver for bridging the gap between academic learning and labor market requirements, especially in countries experiencing rapid economic growth such as China [1] . With the transition from elite to mass higher education in the 1990s, the number of graduates has surged dramatically, reaching over 11 million in 2023, thereby intensifying job market competition [2] . This massification of higher education has created an unprecedented challenge: many graduates face unemployment or underemployment due to the misalignment between acquired academic qualifications and the dynamic needs of the labor market [3] .  \nScholars and policymakers agree that employability is a multidimensional construct that extends beyond technical competence [4] . It encompasses soft skills, adaptability, problem-solving, resilience, career planning, industry exposure, and technological literacy [5] . Employers increasingly emphasize interpersonal c","cbCaihXrqVpGNiYs","https://ap.wps.com/l/cbCaihXrqVpGNiYs","pdf",287584,11,"English","# Abstract\n# Introduction","[{\"question\":\"What variables and constructs were identified as influencing employability?\",\"answer\":\"The study uses six constructs: Professional Skills, Soft Skills, Career Guidance, Industry-Academia Collaboration, Technological Literacy, and Employability Outcomes.\"},{\"question\":\"Which factor was found to be the strongest predictor of employability?\",\"answer\":\"Soft Skills were identified as the strongest predictor of employability (β = 0.35), followed by Professional Skills and Technological Literacy.\"},{\"question\":\"How do mediation and moderation analyses contribute to the model?\",\"answer\":\"Career Guidance was shown to influence employability indirectly through Soft Skills, while Industry-Academia Collaboration strengthened the effect of Professional Skills on employability.\"}]","Enhancing Vocational Graduate Employability through Mobile Application on Advanced Quantitative Modeling of Skills and Partnerships | PDF",1792042941,28]