Lifelong learning participation
How is participation in lifelong learning shaped? As automation, AI, and demographic change continue to transform the way we work, continuous skill development becomes increasingly important for maintaining employability, adaptability, and overall economic resilience. To develop better policies, it is necessary to better understand what shapes participation.
This dissertation shows that participation in lifelong learning is not determined by a single factor, but by the interaction of multiple factors across different contexts. Furthermore, it shows that those with higher education levels, stable employment, and higher incomes are more likely to participate, whereas people in vulnerable positions, those with fewer skills, lower incomes, or less job security, participate less often. This increases their risk of being left behind.Other findings are that technological change affects training participation differently; those exposed to AI tend to have higher training participation, while those exposed to robotization have lower participation.
Furthermore, organizations with a strong learning climate have higher training participation, but these effects are mediated by financial incentives. Additionally, we discern types of people based on motivation; career-enriching learners, who are driven intrinsically, and practical implementers, who rely on extrinsic motivation.
Lastly, national institutions are more important than sector institutions, showing that lifelong learning is strongly influenced by broad policy environments. However, this thesis also finds that research on the topic is hampered by definitional ambiguity and related measurement issues. Overall, high participation depends on awareness of change, intrinsic motivation, organizational support, and strong institutions and requires shared investment from individuals, employers, and governments.