Enhancing employees’ workplace well-being through workplace digitization: exploring the mediating roles of job demands and digital anxiety

Abstract

Introduction: With the arrival of the digital era, workplaces are undergoing radical changes, and digitalization not only enhances work efficiency but also significantly influences employees’ workplace well-being. In this context, it is crucial for enterprises to actively promote digital transformation to gain a competitive edge. A significant challenge lies in liberating employees from monotonous tasks, thereby facilitating their engagement in more creative and analytical endeavors, which in turn enhances their workplace well-being. Consequently, investigating the mechanisms for improving workplace well-being during this transition has emerged as a critical area of inquiry in both scholarly and practical realms. Methods: Based on the Job Demands-Resources (JD-R) model, job demands and digital anxiety are introduced as mediating variables, and a dual-mediation model is constructed to explore the specific mechanism through which workplace digitization affects workplace well-being. A three-stage follow-up survey was conducted, resulting in 382 valid samples for rigorous statistical analysis. Results: The results show that workplace digitization positively impacts workplace well-being. Job demands and digital anxiety mediate the association between workplace digitization and workplace well-being. Additionally, workplace digitization reduces job demands and digital anxiety, which in turn enhances employees’ workplace well-being. These findings provide theoretical guidance on how management practices can enhance employees’ workplace well-being. Discussion: Based on the JD-R model, this study expands the research scope of workplace digitization outcome variables and enriches the antecedents of workplace well-being. By applying the JD-R model, this research positions workplace digitization as a macro-level organizational intervention that influences workplace well-being through both loss and gain pathways, thereby contributing valuable insights into its antecedents.