IDEAS home Printed from https://ideas.repec.org/a/bla/bstrat/v35y2026i4p5268-5284.html

Leveraging Generative Artificial Intelligence to Enhance Carbon Performance in Supply Chains Through Green Product Innovation and End‐of‐Life Product Management: AI‐Driven Carbon Performance

Author

Listed:
  • Syed Muhammad Shariq
  • Roman Sperka
  • Saqib Shamim
  • Hassan Ali

Abstract

This study illustrates how organizations reconcile their information processing capabilities with uncertainty within the supply chain (SC) through generative artificial intelligence (GAI) to achieve carbon performance (CP). A quantitative research methodology is applied, and 155 responses from manufacturing firms are analyzed through structural equation modeling (SEM) for hypothesis testing. The findings suggest that GAI for process automation and cognitive engagement has a positive influence on business intelligence (BI), whereas end‐of‐life (EOL) product management mediates the relationship between green product innovation (GPI) and CP. This study contributes to the SC context, focusing on GAI and BI in mitigating uncertainties within SCs to foster GPI and improve CP. This study highlights actionable frameworks for leveraging digital technologies in sustainable SCs by addressing technological challenges and integrating green innovation practices.

Suggested Citation

  • Syed Muhammad Shariq & Roman Sperka & Saqib Shamim & Hassan Ali, 2026. "Leveraging Generative Artificial Intelligence to Enhance Carbon Performance in Supply Chains Through Green Product Innovation and End‐of‐Life Product Management: AI‐Driven Carbon Performance," Business Strategy and the Environment, Wiley Blackwell, vol. 35(4), pages 5268-5284, May.
    • Handle: RePEc:bla:bstrat:v:35:y:2026:i:4:p:5268-5284
    DOI: 10.1002/bse.70384
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/bse.70384
    Download Restriction: no
    File URL: https://libkey.io/10.1002/bse.70384?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:bstrat:v:35:y:2026:i:4:p:5268-5284. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-0836 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.