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Generative artificial intelligence, knowledge search patterns, and circular supply chain dependence reconstruction: A dual path model for performance improvement

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  • Liu, Yutian
  • Tian, Hong

Abstract

Unhealthy interdependence often constrains smart and resilient circular supply chains. Drawing on resource dependence theory and knowledge management theory, this study explores how generative artificial intelligence (GenAI) can reshape interdependence and empower circular supply chains. Using survey data from 409 Chinese firms and partial least squares structural equation modeling, we find that GenAI can reduce dependence asymmetry and strengthen joint dependence in the circular supply chain by facilitating interactive and non-interactive knowledge search, thereby further improving smart and resilient circular supply chain performance. Moreover, transactive memory systems and digital organizational culture can strengthen the promoting effect of GenAI on interactive and non-interactive knowledge search. Sensitivity analysis indicates that dependence asymmetry is the strongest predictor of smart and resilient circular supply chain performance. This study fills a theoretical gap in research on how GenAI influences supply chain interdependence and provides actionable guidance for managers deploying GenAI within circular economy frameworks.

Suggested Citation

  • Liu, Yutian & Tian, Hong, 2026. "Generative artificial intelligence, knowledge search patterns, and circular supply chain dependence reconstruction: A dual path model for performance improvement," Technology in Society, Elsevier, vol. 86(C).
    • Handle: RePEc:eee:teinso:v:86:y:2026:i:c:s0160791x26000977
    DOI: 10.1016/j.techsoc.2026.103308
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