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Empirical analysis of the roles of dynamic sustainable capabilities and artificial intelligence in accelerating circular business model innovation: Insights from Chinese manufacturing firms

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  • Renfei, Chen
  • Zhongwen, Li

Abstract

Despite increasing managerial focus on Artificial Intelligence (AI) -enhanced environmental strategies, few studies have examined how dynamic sustainable capabilities (DSC) and AI-enabled circular business model innovation (CBMI) affect sustainability-oriented corporate performance (SOCP). Grounded in dynamic capabilities theory and dynamic managerial capabilities theory, this study develops a unified framework to investigate the mechanisms through which DSC fosters AI capabilities and CBMI, ultimately enhancing SOCP, while examining managerial cognition as a key moderator. This study empirically analyzes data from 289 questionnaires from 120 manufacturing companies through the PLS-SEM method. The findings reveal that DSC has a positive effect on AI capabilities, CBMI, and SOCP. AI capabilities and CBMI partially mediate the DSC-SOCP relationship. Managerial cognition positively contributes to the role of CBMI on SOCP, and AI capabilities have a positive effect on CBMI. This study advances the literature by elucidating the sequential pathways from DSC to AI-driven CBMI, highlighting micro-foundations for circular transitions. Moreover, this study extends managerial cognition to CBMI practices, revealing the synergies between managerial cognition and CBMI and its role in accelerating SOCP, contributing to clarifying the sources of performance differences in circular economy practices. This lays the foundation for future research agendas on AI integration in the circular economy.

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  • Renfei, Chen & Zhongwen, Li, 2026. "Empirical analysis of the roles of dynamic sustainable capabilities and artificial intelligence in accelerating circular business model innovation: Insights from Chinese manufacturing firms," Technology in Society, Elsevier, vol. 85(C).
    • Handle: RePEc:eee:teinso:v:85:y:2026:i:c:s0160791x25003665
    DOI: 10.1016/j.techsoc.2025.103176
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