Study Maps Complex System Driving TVET Graduate Job Mismatch in Malaysia

A Malaysian vocational graduate finishes their training in electrical installation, passes their exams, and enters the job market—only to find themselves working in retail, or hospitality, or not working at all. This mismatch between what someone trained to do and what they actually end up doing is not random. A new study mapping the forces behind this problem has identified 21 interconnected factors that shape whether a TVET graduate lands in their field or drifts elsewhere, and the research reveals that the system is far more complex than previous work has suggested.

The study, published in Nature, focused specifically on horizontal mismatch—the gap between a graduate's field of study and their actual employment—among technical and vocational education and training graduates in Malaysia. Researchers used a technique called Total Interpretive Structural Modelling to trace how different forces interact within the vocational education system. They identified 21 factors across five dimensions: internal factors (like a student's own capabilities), external factors (like the state of the economy), interpersonal dynamics, institutional elements, and sociodemographic characteristics. Seventeen of these factors came from existing research literature; four were specific to Malaysia's context, including the role schools play in helping students choose careers, the reputation of training institutions, and the quality of industrial training programmes.

What emerged from the analysis was a hierarchy of influence, but not a simple one. At the foundation—Level 4 in the structural model—sit three foundational forces: labour market conditions, gender, and socioeconomic background. These are the bedrock. Above them, at Level 3, sits something that might seem more individual: the personal interests of graduates themselves. The researchers then used a second analytical method, called MICMAC analysis, to measure how much driving power each factor actually exerts across the entire system. This revealed something important: labour market conditions and socioeconomic background emerged as independent or driving factors—they push the system forward. Gender, by contrast, was classified as autonomous, meaning it operates somewhat independently but does not drive the broader system in the same way. Personal interests, despite sitting higher in the hierarchy, also showed significant driving power.

The distinction matters because it prevents a common misreading: a factor's position in a hierarchy does not automatically tell you how much influence it wields across the whole system. TISM shows you the directional flow and structure; MICMAC shows you the actual force. Together, they paint a picture of employability mismatch as something that cannot be solved by tweaking one lever. A student's interests matter, but they operate within constraints set by the job market and by circumstances of birth—family income, gender, the economic conditions they graduate into.

The research provides a framework for policymakers and educational institutions to think about intervention. If labour market conditions and socioeconomic background are the primary drivers, then solutions cannot stop at the classroom door. They require alignment between what vocational schools teach and what employers actually hire for, and they require attention to whether students from lower-income backgrounds have equal access to the training and networks that lead to jobs in their field. The study does not prescribe specific policies, but it maps the terrain where they would need to work. The authors note that this framework now provides a foundation for future quantitative research and predictive modelling—the next step toward understanding not just why mismatch happens, but how to predict and prevent it.