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Building AI-enabled capabilities for improved environmental and manufacturing performance: evidence from the US and the UK.

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Title: Building AI-enabled capabilities for improved environmental and manufacturing performance: evidence from the US and the UK.
Authors: Fosso-Wamba, Samuel1 (AUTHOR) s.fosso-wamba@tbs-education.fr, Guthrie, Cameron1 (AUTHOR), Queiroz, Maciel M.2 (AUTHOR), Oyedijo, Adegboyega3 (AUTHOR)
Source: International Journal of Production Research. Jan2026, Vol. 64 Issue 2, p545-564. 20p.
Subjects: Artificial intelligence, Sustainability, Manufacturing industries, Structural equation modeling, Experimental design
Geographic Terms: United Kingdom, United States
Abstract: Drawing on the emerging literature on the environmental and manufacturing roles of artificial intelligence (AI), this study proposes a research model that connects AI-enabled capabilities to environmental and manufacturing performance. We collected data from 128 managers from manufacturing companies in the United States (US) and the United Kingdom (UK) and tested the proposed model using partial least squares structural equation modelling. The findings lend strong empirical support to the research model and confirm the influence of AI-enabled capabilities on environmental and manufacturing performance. We also discovered significant differences in AI use and impact between US and UK respondents, highlighting the importance of context in AI research. These findings advance practice and theory while also contributing to the emerging literature on artificial intelligence for sustainability. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Building AI-enabled capabilities for improved environmental and manufacturing performance: evidence from the US and the UK.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainability%22">Sustainability</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+industries%22">Manufacturing industries</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+equation+modeling%22">Structural equation modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Experimental+design%22">Experimental design</searchLink>
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  Data: Drawing on the emerging literature on the environmental and manufacturing roles of artificial intelligence (AI), this study proposes a research model that connects AI-enabled capabilities to environmental and manufacturing performance. We collected data from 128 managers from manufacturing companies in the United States (US) and the United Kingdom (UK) and tested the proposed model using partial least squares structural equation modelling. The findings lend strong empirical support to the research model and confirm the influence of AI-enabled capabilities on environmental and manufacturing performance. We also discovered significant differences in AI use and impact between US and UK respondents, highlighting the importance of context in AI research. These findings advance practice and theory while also contributing to the emerging literature on artificial intelligence for sustainability. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/00207543.2024.2428427
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      – Code: eng
        Text: English
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        PageCount: 20
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      – SubjectFull: Manufacturing industries
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      – SubjectFull: Structural equation modeling
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      – SubjectFull: Experimental design
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      – SubjectFull: United Kingdom
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      – SubjectFull: United States
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              M: 01
              Text: Jan2026
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