Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Business Strategy and the Environment

DOI: 10.1002/bse.70384

Authors: Syed Muhammad Shariq, Roman Sperka, Saqib Shamim, Hassan Ali

Institutions: University of Ostrava, Silesian University in Opava, Queen Mary University of London, University of Vaasa, Tomas Bata University in Zlín

Language: English

Topic: Sustainable Supply Chain Management

Subfield: Strategy and Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Responsible consumption and production, Climate action

Open Access status: hybrid

Estimated APC paid: $4,480