Algorithmic Empowerment and Its Impact on Circular Economy Participation: An Empirical Study Based on Human–Machine Collaborative Decision-Making Mechanisms

At the intersection of the circular economy and artificial intelligence (AI), high-value secondhand trading faces a “triple decision dilemma” of cognitive overload, trust risk, and emotional attachment. To address the limits of traditional human-centered theories, this study develops and empirically tests a novel framework of Algorithmic Empowerment. Drawing on data from 1396 users of Chinese secondhand luxury platforms and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings reveal that users’ empowerment perception arises from three dimensions—Algorithmic Connectivity (AC), Human–Agent Symbiotic Trust (HAST), and Algorithmic Value Alignment (AVA). This perceived empowerment affects participation willingness through two parallel pathways: the social pathway, where algorithmic curation shapes social norms and recognition, and the cognitive pathway, where AI enhances decision fluency and reduces cognitive friction. The results confirm the dual mediating effects of these mechanisms. This study advances understanding of human–AI collaboration in sustainable consumption by conceptualizing empowerment as the bridge linking algorithmic functions to user engagement, and provides actionable implications for designing AI systems that both enhance efficiency and foster user trust and identification.