Automation, Augmentation and Well-Being of Young Workers: A PLS-SEM Study with Skill as a Moderator

Advancements in technologies, particularly in AI and robotics, have prompted significant concerns regarding the automation of human tasks, potentially resulting in the displacement and reduction of traditional employment opportunities. Conversely, some perspectives suggest that such advancements may augment workers' capabilities by automating specific tasks rather than replacing entire occupations. These contrasting viewpoints on automation and augmentation regarding technological advancements are critical as they may influence workers' job satisfaction differently. This study aims to simultaneously analyze the effects of automation and augmentation on workers' job satisfaction, while investigating potential differences between low-skilled and high-skilled workers using the PLS-SEM method. To achieve this, data from the Youth Panel (YP) 2022 in South Korea were integrated with datasets from the Korea Network for Occupations and Workers (KNOW), Frey and Osborne's (2017) Automation Risk Index, and Felten et al.'s (2019) AI Impact Score (AIOI). The results reveal that technological advancements associated with automation risks negatively affect job satisfaction, while the augmentation of human tasks through AI positively influences job satisfaction. Notably, for high-skilled workers, the negative effects of automation risks were attenuated, and the positive effects of augmentation were amplified. By concurrently analyzing the contrasting perspectives of automation and augmentation, this study provides insights of the complex effects of technological advancements on job satisfaction. Additionally, the empirical demonstration of skill-level moderation provides insights for developing targeted and practical welfare policies and institutional frameworks.