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
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19520%2F25%3AA0000556" target="_blank" >RIV/47813059:19520/25:A0000556 - isvavai.cz</a>
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/10.1002/bse.70384" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/bse.70384</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/bse.70384" target="_blank" >10.1002/bse.70384</a>
Alternative languages
Result language
angličtina
Original language name
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
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50204 - Business and management
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
BUSINESS STRATEGY AND THE ENVIRONMENT
ISSN
0964-4733
e-ISSN
1099-0836
Volume of the periodical
2025
Issue of the periodical within the volume
December
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
1-17
UT code for WoS article
001631553000001
EID of the result in the Scopus database
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