Vocational education quality and students’ satisfaction: an empirical analysis in the UAE context
Year: 2026
Type: article
Study design: Observational Study
Abstract: Purpose Higher vocational education has gained popularity over the years. The integration of competence orientation in higher education is a current market demand. The purpose of this study is to measure and manage the quality of vocational education and training (VET) in universities as an antecedent of student satisfaction. Design/methodology/approach Literature review highlighted program quality, instructor quality and support service quality as key components of VET quality. Research hypotheses were formulated and tested using empirical data gathered from 112 students enrolled in VET programs in the United Arab Emirates. Partial Least Squares−Structural Equation Modeling and importance−performance analysis were used to process the data. Findings Results revealed the moderated mediating role of VET program quality on the relationship between instructor quality and students’ overall satisfaction. Instructor quality emerged as the most important quality component in the model. Research limitations/implications Future studies can explore other dimensions of VET quality, such as the technological factors, as well as the perceptions of other stakeholders, such as the instructors or the employers. Originality/value This study fills a gap in existing literature by exploring the impact of VET quality components on students’ satisfaction. The findings assist VET institutions in prioritizing areas for improvement and guide decision-makers to develop strategies and allocate resources. The proposed model sheds light on the complex relationships among VET key success factors. This case is a unique one in the country-specific context.
Source: Quality Education for All
Authors: Hazem Aldabbas, Maria Gianni, Adnan Alzoukani
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W7130517071
