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

Smail Benzidia, Naouel Makaoui and Omar Bentahar

Technological Forecasting and Social Change, 2021, vol. 165, issue C

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.

Keywords: Big data analytics; Artificial intelligence; Environmental performance; Green supply chain process; Green digital learning; Organizational information processing theory; Healthcare (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (64)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:165:y:2021:i:c:s0040162520313834

DOI: 10.1016/j.techfore.2020.120557

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