Abstract/Summary

The lack of communication between educational institutions and businesses has led to a skills mismatch and a shortage of qualified professionals, leaving many graduates unable to secure jobs. The rise in self-employment among young professionals raises concerns about their commitment to specialised industries. Inspired by the successful implementation of Germany’s dual learning system (DLS), Kazakhstan introduced this approach into its vocational and higher education sectors to address the shortage of skilled workers across its economy. DLS emphasises immersive, work-based training in collaboration with businesses, equipping students with essential skills for the modern workforce and easing their transition into employment. However, adopting international best practices, particularly in developing countries, requires tailoring to fit the specific social and economic contexts and industry needs. This study contributes to the literature by empirically studying the implementation effectiveness of DLS versus traditional education in Kazakhstan’s agri-food business sector based on the perceptions of learners and businesses. Implementing Focus Group Discussions with employers of 19 businesses of different sizes utilised a qualitative methodology based on the expectancy theory of motivation to understand the attitudes of employers and their motivation for participation in DLS. This study also aims to test satisfaction levels with the practice experience of learners (students and graduates) and factors influencing their intentions after graduation, i.e., whether they remain with the same employer in the agri-food industry, change employers in the same industry or leave the industry altogether. Evaluation is based on a multistage random sample of 651 learners undertaking dual learning system (hence dual) and 217 learners undertaking traditional education (hence non-dual). Kirkpatrick's training evaluation model provided the theoretical framework for designing satisfaction dimensions potentially influencing post-graduation decisions. Multinomial Logistic Regression was used to examine the career intentions of both dual and non-dual groups after graduation.