AI capability and green innovation impact on sustainable performance: Moderating role of big data and knowledge management

Hussam Al Halbusi, Khalid Ibrahim Al-Sulaiti, Ali Abdallah Alalwan and Adil S. Al-Busaidi

Technological Forecasting and Social Change, 2025, vol. 210, issue C

Abstract: This study addresses the environmental impact of industries by focusing on increased resource consumption and waste generation that lead to ecosystem degradation. It advocates sustainable practices and a circular economy (CE) as strategies to mitigate these effects. Thus, the study examines how Artificial Intelligence (AI) capabilities directly affect green innovations and their subsequent influence on sustainable performance and CE. In addition, it introduces two key moderating factors—big data analytics and knowledge management systems—in the relationship between AI capabilities and green innovation. We validate the model using multi-sectoral population data from various Qatari industries and employ structural equation modeling (SEM) and artificial neural networks (ANN) as analytical approaches. The results indicate the significant impact of AI capability on green innovation, with these innovations critically linked to sustainable performance and CE. Remarkably, interactions with big data analytics and knowledge management systems enhance the positive impact of AI capabilities. Hence, this study emphasizes AI's noteworthy implications for green innovation, shaping sustainable performance, and CE. Identifying big data analytics and knowledge management systems as vital moderators adds complexity. The findings guide industries to integrate AI, big data analytics, and knowledge management systems for practical applications, stressing a holistic approach to promoting environmentally responsible practices across sectors.

Keywords: AI capability; Green innovation; Big data analytics; Knowledge management systems; Environmental sustainability; Circular economy (CE) (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:210:y:2025:i:c:s0040162524006954

DOI: 10.1016/j.techfore.2024.123897

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