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The role of artificial intelligence-enabled dynamic capability on environmental performance: The mediation effect of a data-driven culture in France and the USA

Samuel Fosso Wamba, Maciel M. Queiroz and Laura Trinchera

International Journal of Production Economics, 2024, vol. 268, issue C

Abstract: This study develops a model supported by the dynamic capability theory to analyze the role of artificial intelligence-enabled dynamic capability in environmental performance and the mediation of a data-driven culture. We collected data from supply chain managers in France and the USA. The data was analyzed and validated through classical structural equation modeling using the lavaan R package. The results suggest that artificial intelligence-enabled dynamic capability positively affects the two countries' data-driven culture and environmental performance. Contrary to our expectations, we found the effect of data-driven culture on environmental performance to be not significantly different from zero for the USA sample. Moreover, while the data-driven culture for the French sample partially mediates the relationship between artificial intelligence-enabled dynamic capability and environmental performance, only a direct link between artificial intelligence-enabled dynamic capability and environmental performance could be verified for the USA Case. Our study advances the emerging literature on artificial intelligence-enabled dynamic capability, bringing originality by showing that in an artificial intelligence-enabled dynamic capability context, data-driven can vary according to both the country's national culture hosting the firms/supply chains and the dynamics and changes of such environment. Therefore, scholars should rely on such findings to bring more insights to practitioners and policymakers.

Keywords: AI dynamic capability; Data-driven culture; Environmental performance; SDG 9; SDG 11 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:268:y:2024:i:c:s0925527323003638

DOI: 10.1016/j.ijpe.2023.109131

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