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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

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  • Fosso Wamba, Samuel
  • Queiroz, Maciel M.
  • Trinchera, Laura

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.

Suggested Citation

  • Fosso Wamba, Samuel & Queiroz, Maciel M. & Trinchera, Laura, 2024. "The role of artificial intelligence-enabled dynamic capability on environmental performance: The mediation effect of a data-driven culture in France and the USA," International Journal of Production Economics, Elsevier, vol. 268(C).
    • Handle: RePEc:eee:proeco:v:268:y:2024:i:c:s0925527323003638
    DOI: 10.1016/j.ijpe.2023.109131
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