Sustainable Training Quality for Labor Export to Japan: A Socio-Economic Perspective in the Era of Digital Transformation and Artificial Intelligence

Abstract

This study examines the overall training quality outcome (TQO) provided by firms for Vietnamese workers entering the Japanese labor market in the context of digital transformation and artificial intelligence (AI). Drawing on human capital theory, economic sociology, and institutional theory, the research conceptualizes training quality as a multidimensional construct encompassing occupational competence, job readiness, discipline, and adaptability to technology-enabled environments. A quantitative research design was employed, with data collected from 360 respondents, including trainees and stakeholders involved in labor export training programs. Reliability analysis and exploratory factor analysis confirm the validity of the measurement scale, while group comparison techniques (T-test and ANOVA) reveal no significant differences in perceived training outcomes across gender, job position, and seniority groups. The findings indicate that training programs achieve moderate effectiveness but require further improvement to meet the stringent requirements of the Japanese labor market. Importantly, the study highlights the growing importance of digital skills and AI adaptability as key components of workforce readiness. The results contribute to the literature by integrating socio-economic and technological perspectives into the analysis of training quality and offer practical implications for firms and policymakers aiming to promote sustainable labor export.