Artículos de Revista en Ciencias Económicas · 2026
The interplay of data-driven insights and AI anxiety in shaping the impact of AI capabilities on circular economy capability
In a world facing pressing environmental challenges like climate change and resource scarcity, Artificial Intelligence (AI) is widely regarded as a powerful tool to enhance and support sustainability goals via Circular Economy Capability (CEC). The organizational capacity to leverage this technology, Artificial Intelligence Capability (AIC), is conceptualized through the lens of the Resource-Based Theory (RBT) as the capacity to effectively implement and utilize AI to generate strategic value. However, the direct relationship between AIC and CEC is not straightforward. The purpose of this research is to investigate this nuanced relationship by examining how socio-technical factors such as Data-Driven Insights (DDI)—actionable inferences derived from analytics over data—and AI Anxiety—stemming from employees’ fear of job loss—shape the relationship between AIC and CEC. Using a moderated mediation model and Partial Least Squares Structural Equation Modeling (PLS-SEM), we analyzed data from firms with moderate to high technology maturity. While the study’s results are primarily based on context-specific evidence, which invites further investigation into generalizability to other settings, our findings suggest that the direct effect of AIC on CEC is not significant. Instead, DDI significantly mediate the relationship, confirming that AIC must be bundled with actionable insights to create value. Crucially, AI anxiety negatively moderates the effect of DDI on CEC. This means that while organizations may generate valuable insights, employee resistance and fear hinder their effective translation into sustainability practices. This study highlights the critical socio-technical barriers to AI adoption and their impact on achieving sustainability goals.