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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integrating Big Data and Artificial Intelligence Technology to Build Renewable Energy Supply Chain Resilience: An Empirical Study

Sachin Kumar Mangla, Muhammad Sabbir Rahman, Surajit Bag, David Roubaud et al.
6 citations
Business Strategy and the Environment
Supply Chain Resilience and Risk Management
13.36
article

Integrating Big Data and Artificial Intelligence Technology to Build Renewable Energy Supply Chain Resilience: An Empirical Study

Sachin Kumar Mangla, Muhammad Sabbir Rahman, Surajit Bag, David Roubaud, Susmi Routray
article en
6 citations

Abstract

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.

Business Strategy and the EnvironmentVol. 34(7)
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)
Openalex Percentile: Top 2%
Supply Chain Resilience and Risk Management
13.36
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.