Rethinking Continuance Intentions in the AI-Augmented Digital Workplace

As digital technologies become increasingly embedded in organizational processes, understanding the determinants of continued technology usage, particularly in AI-augmented digital workplaces, is essential for optimizing employee engagement and performance. This study evaluates continuance intentions in AI-augmented remote work systems by extending the Expectation Confirmation Model (ECM) with constructs such as perceived enjoyment, trust in technology, self-efficacy, and user satisfaction. Using Structural Equation Modeling (SEM) on data from 348 experienced users of digital work platforms, our analysis reveals that trust and perceived enjoyment significantly influence satisfaction, which in turn drives continued usage intentions. Notably, the dynamics of remote work technology adoption diverge from prior information systems continuance models, with enjoyment and psychological assurance playing more prominent roles than traditionally modeled constructs like utility alone. These findings highlight the evolving expectations of knowledge workers in AI-enabled work environments, where user-centric design, intelligent automation, and trustworthy system behaviors are paramount. The study offers actionable insights for system designers and digital transformation leaders seeking to sustain user engagement in an increasingly hybrid and AI-integrated workplace. The results provide important implications for organizations designing or deploying digital collaboration tools.