Evaluating the Effectiveness of Digital Skills Training in Manufacturing Apprenticeships

Thesis Abstract

The rapid integration of digital technologies into manufacturing processes necessitates a parallel enhancement of digital skills among apprentices to ensure workforce preparedness and industry competitiveness. Despite the widespread adoption of digital training initiatives within manufacturing apprenticeship programs, there remains limited empirical evidence regarding their effectiveness in developing requisite digital competencies and translating these skills into improved job performance. This study aims to evaluate the effectiveness of digital skills training within manufacturing apprenticeships, with specific objectives to assess apprentices’ competency development, identify factors influencing training outcomes, and explore the relationship between training participation and workplace productivity. The research adopts a mixed-methods approach, combining quantitative surveys and qualitative interviews to provide a comprehensive understanding of training effectiveness. The target population comprises 300 apprentices enrolled in manufacturing apprenticeship programs within a prominent manufacturing company in the region, selected through stratified random sampling to ensure representation across various trades (e.g., mechanical, electronic, automation). Data collection instruments include standardized digital skills assessment tests, pre- and post-training questionnaires measuring participants' self-efficacy and perceived competency, and semi-structured interview guides aimed at capturing experiential insights from both apprentices and trainers. Validity and reliability of the instruments are ensured through pilot testing, expert validation, and calculation of Cronbach's alpha, which exceeds the acceptable threshold of 0.7. Quantitative data are analyzed using descriptive statistics, paired sample t-tests to measure competency gains, and multiple regression analysis to identify predictors of training effectiveness, based on the Technology Acceptance Model (TAM) and the Human Capital Theory. The qualitative data are subjected to thematic analysis following Braun and Clarke’s framework, aiming to uncover contextual factors and perceptions affecting training outcomes. The study also employs appropriate data triangulation to enhance validity and robustness of findings. Expected findings suggest that digital skills training significantly improves apprentices’ technical competencies, with variations influenced by factors such as training duration, prior digital literacy, and access to digital resources. The regression analysis is anticipated to reveal that perceived usefulness and ease of access to digital tools are significant predictors of successful skill acquisition, in line with TAM propositions. Moreover, qualitative insights are expected to highlight challenges faced by apprentices, including resource constraints and training design deficiencies, as well as facilitators like effective mentorship and practical application opportunities. This research will contribute to existing knowledge by providing an evidence-based assessment of digital training programs within manufacturing apprenticeships, integrating theoretical insights from TAM and Human Capital Theory with empirical data. It also addresses a critical gap by illuminating the contextual and behavioral factors influencing digital skills acquisition in industrial settings. Practically, the findings will inform training providers, industry stakeholders, and policymakers on designing more effective digital competency development initiatives, fostering increased productivity and technological adaptation. The study concludes that targeted interventions, such as enhancing digital resource accessibility, customizing training modules to apprentices’ prior knowledge, and fostering a supportive learning environment, are essential for maximizing training outcomes. Recommendations include adopting a systematic approach to digital skills development, integrating continuous assessment mechanisms, and strengthening industry-trainer partnerships to ensure training relevance. Future research avenues proposed encompass longitudinal studies to track skill retention over time and cross-sectoral analyses to generalize findings across diverse manufacturing contexts.