Integrating Big Data and Artificial Intelligence Technology to Build Renewable Energy Supply Chain Resilience: An Empirical Study
ABSTRACT This study examines how big data and AI technology integration influence green innovation and supply chain resilience in the renewable energy sector. The study also examines the mediating role of big data and AI‐powered green innovation in enhancing resilience, as well as the moderating effect of government effectiveness. Based on the dynamic capability view, the hypotheses are formulated and further tested using primary data collected through surveys. By explaining the mechanisms through which resilience can be cultivated, the study contributes to the resilience literature and provides real‐world suggestions for managers seeking to prepare their organizations for unforeseen challenges. Therefore, this research not only contributes to the theoretical understanding of resilience but also provides actionable insights for industry practitioners. Organizations can leverage big data and AI technologies to drive innovation and develop better supply chain resilience strategies under varying levels of government effectiveness to effectively manage the complexity of renewable energy use, drive, and contribute to a green future.
Authors
- Sachin Kumar Mangla (ORCID: https://orcid.org/0000-0001-7166-5315)
- Muhammad Sabbir Rahman (ORCID: https://orcid.org/0000-0003-1613-7944)
- Surajit Bag (ORCID: https://orcid.org/0000-0002-2344-9551)
- David Roubaud (ORCID: https://orcid.org/0000-0003-3827-6187)
- Susmi Routray (ORCID: https://orcid.org/0000-0002-9062-599X)
Institutions
- North South University (BD)
- Institute of Management Technology (IN)
- Gulf University for Science & Technology (KW)
- O. P. Jindal Global University (IN)
- Montpellier Business School (FR)
- Excelia Business School (FR)
- University of Plymouth (GB)
Publication Details
- Journal
- Business Strategy and the Environment
- Published
- 2025-06-28
- DOI
- https://doi.org/10.1002/bse.70052
- Citations
- 6
- Primary Topic
- Supply Chain Resilience and Risk Management
- Type
- article
- Field-Weighted Citation Impact
- 13.36