Linking technology readiness and the job demands–resources framework: how generative AI shapes work in small and medium enterprises?

Purpose This study aims to examine how individual technology readiness shapes employees’ perceptions of generative artificial intelligence (GenAI) as a job resource and/or a job demand, and how these appraisals influence engagement, exhaustion and performance in small and medium enterprises (SMEs). Design/methodology/approach Survey data were collected from 465 UK SME employees who regularly use GenAI. The study integrates the technology readiness index (TRI) with the job demands–resources (JD–R) framework and tests the model using covariance-based structural equation modeling and multigroup analysis. Findings Optimism increases artificial intelligence (AI)-related job resources, while innovativeness, insecurity and discomfort primarily increase job demands. Resources enhance engagement, whereas demands increase exhaustion, which in turn reduces engagement. Engagement is the main predictor of employee performance. Effects are stronger in smaller firms. Research limitations/implications The cross-sectional design and self-reported data limit causal inference and generalizability. The study advances theory by showing that GenAI operates simultaneously as a job resource and a demand, with asymmetric effects shaped by technology readiness. Practical implications SMEs should actively reduce AI-related demands (e.g. cognitive load, validation effort) while strengthening resources such as training and workflow integration, prioritizing engagement to improve performance. Social implications The impact of GenAI depends on employee experience: it can enhance well-being when perceived as a resource but increase strain when experienced as a demand. Originality/value The study integrates TRI and JD–R to explain how individual technological predispositions shape AI-related work experiences and performance in SMEs.