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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 00207543 |
| DOI: | 10.1080/00207543.2024.2428427 |
