Adapting to digital transformation: Determinants of training motivation in response to digital automation among workers in six EU countries

Research background: The increasing automation of work tasks is transforming labour markets, creating both challenges and opportunities for workers. Reskilling and upskilling through training are essential for maintaining employability in the rapidly changing digital economy. While automation may complement certain job roles, it substitutes others, leading to skills mismatches and heightened concerns about job security. Previous studies have provided inconsistent findings regarding the influence of automation on workers' training motivations, lacking detailed distinctions between task complementarity and substitution effects, as well as differentiations in types of job insecurity. Purpose of the article: This article examines the key determinants of workers' motivation to participate in training in response to automation. It specifically addresses the gaps in literature by clearly distinguishing between the complementarity and substitution effects of automation on job tasks, differentiating general fear of job loss from specific technological unemployment fears, and exploring the role of previous training experiences, formal education levels, and structural barriers in shaping training decisions. The study contributes to existing theories by clarifying how task-specific automation perceptions distinctly affect training motivations. Methods: The study uses quantitative survey data collected from over 6,000 respondents across six European Union countries (Austria, Czechia, Germany, Hungary, Poland, and Slovakia). Multivariate logistic regression analysis is employed to assess the relationships between workers' training motivations and factors such as automation exposure, general job loss fear, specific technological unemployment fear, prior training participation, and education. Findings & value added: The study provides empirical evidence enriching workforce adaptation and lifelong learning theories by highlighting how nuanced perceptions of automation distinctly shape training motivations. Results indicate that workers previously engaged in training, those experiencing complementarity or partial substitution of tasks due to automation, and individuals expressing general fear of job loss show higher motivation for training. Conversely, extensive substitution of tasks and specific fears of technological unemployment decrease training willingness. Formal education levels overall do not significantly influence training participation, but notably workers with vocational education exhibit lower training motivation. These findings offer a detailed theoretical understanding of motivational factors and present critical implications for policymakers and organizational leaders. To effectively support lifelong learning in the digital economy, fostering positive training experiences and proactively addressing structural and perceptual barriers are essential.