The COVID-19 pandemic impacted workers globally during 2020–2022 and it has had major psychological implications for workers’ wellbeing. This longitudinal study analyzed risk and protective factors predicting COVID-19 anxiety among workers in Finland. Longitudinal national sample of Finnish workers (n = 685) participated in a five-wave study conducted in 2020–2022, covering multiple waves of the COVID-19 pandemic and its aftermath. Our outcome measure was COVID-19 anxiety. Predictors were psychological distress, work exhaustion, technostress, and loneliness. Models also controlled for self-regulation; social support at work and remote working; and socio-demographic background factors. Both within-person and between-person effects were analyzed using multilevel hybrid regression models. COVID-19 anxiety varied between time points which is explained by changes in circumstances during the pandemic. Highest anxiety was expressed in the middle of the Delta variant surge and lockdown in spring 2021. Within-person changes in psychological distress, work exhaustion, technostress, self-regulation, and perceived loneliness were all associated with COVID-19 anxiety. Between-person results showed that distressed, exhausted, technostressed, and lonely workers expressed more anxiety than others. Remote workers reported higher anxiety over time than others. Those who had reported high self-regulation reported lower anxiety than others. Female gender and younger age were associated with higher anxiety. COVID-19 anxiety continues to be an important phenomenon with a magnitude of consequences on people and numerous industries. This study showed that general mental health and work stressors predict COVID-19 anxiety. Promoting social support and workers’ self-regulation skills can be beneficial for overcoming anxiety during and after the pandemic.

The fourth industrial revolution is bringing artificial intelligence (AI) into various workplaces, and many businesses worldwide are already capitalizing on AI assistants. Trust is essential for the successful integration of AI into organizations. We hypothesized that people have higher trust in human assistants than AI assistants and that people trust AI assistants more if they have more control over their activities. To test our hypotheses, we utilized a survey experiment with 828 participants from Finland. Results showed that participants would rather entrust their schedule to a person than to an AI assistant. Having control increased trust in both human and AI assistants. The results of this study imply that people in Finland still have higher trust in traditional workplaces where people, rather than smart machines, perform assisting work. The findings are of relevance for designing trustworthy AI assistants, and they should be considered when integrating AI technology into organizations.