The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance

Smaïl Benzidia (), Naouel Makaoui and Omar Bentahar ()
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Smaïl Benzidia: CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine
Naouel Makaoui: ICD International Business School Paris
Omar Bentahar: CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine

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Abstract: Big data analytics and artificial intelligence (BDA-AI) technologies have attracted increasing interest in recent years from academics and practitioners. However, few empirical studies have investigated the benefits of BDA-AI in the supply chain integration process and its impact on environmental performance. To fill this gap, we extended the organizational information processing theory by integrating BDA-AI and positioning digital learning as a moderator of the green supply chain process. We developed a conceptual model to test a sample of data from 168 French hospitals using a partial least squares regression-based structural equation modeling method. The findings showed that the use of BDA-AI technologies has a significant effect on environmental process integration and green supply chain collaboration. The study also underlined that both environmental process integration and green supply chain collaboration have a significant impact on environmental performance. The results highlight the moderating role of green digital learning in the relationships between BDA-AI and green supply chain collaboration, a major finding that has not been highlighted in the extant literature. This article provides valuable insight for logistics/supply chain managers, helping them in mobilizing BDA-AI technologies for supporting green supply processes and enhancing environmental performance.

Date: 2021
Note: View the original document on HAL open archive server: https://hal.science/hal-03028127
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Citations: View citations in EconPapers (39)

Published in Technological Forecasting and Social Change, 2021, 165, pp.120557. ⟨10.1016/j.techfore.2020.120557⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03028127

DOI: 10.1016/j.techfore.2020.120557

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