From threat to strength: how AI usage is paradoxically associated with resilience among accounting and finance employees with perceived work meaningfulness as a mediating mechanism

Abstract

Introduction Artificial intelligence is increasingly reshaping accounting and finance work, yet its psychological implications for employees remain insufficiently understood. While existing studies have mainly emphasized the productivity and efficiency outcomes of AI, less is known about how AI usage relates to employee resilience in accounting and finance roles. To address this gap, this study examines the relationship between AI usage and employee resilience and explores the psychological mechanism and boundary condition underlying this relationship. Methods Based on the Job Demands-Resources (JD-R) theory, this study develops a moderated mediation model in which perceived work meaningfulness acts as the mediator and job complexity serves as the moderator. Data were obtained from employees in accounting and finance-related positions through a three-wave time-lagged survey. After matching responses across the three waves and screening invalid cases, 332 valid questionnaires were used for analysis. Results The results showed that AI usage positively predicted employee resilience, and perceived work meaningfulness mediated this relationship. Job complexity further moderated the relationship between AI usage and perceived work meaningfulness, such that the positive relationship was stronger under conditions of low job complexity. Discussion This study extends the JD-R framework to AI-enabled work contexts by conceptualizing AI usage as a work-related resource associated with employee resilience. The findings identify perceived work meaningfulness as a key psychological mechanism and job complexity as an important boundary condition, providing theoretical and practical implications for AI implementation in accounting and finance settings.