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AI and emerging technology adoption: a research agenda for operations management.

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Title: AI and emerging technology adoption: a research agenda for operations management.
Authors: Venkatesh, Viswanath1 (AUTHOR) vvenkatesh@vvenkatesh.us, Raman, Raji1 (AUTHOR), Cruz-Jesus, Frederico2 (AUTHOR)
Source: International Journal of Production Research. Aug2024, Vol. 62 Issue 15, p5367-5377. 11p.
Subjects: Technological innovations, Innovation adoption, Supply chain management, Artificial intelligence, Research personnel
Abstract: Artificial intelligence (AI) is becoming a critical engine that powers a range of technology solutions. It is rapidly playing a vital role in supply chain and operations management. In this paper, we present a research agenda of how researchers and practitioners alike can study the potential adoption of AI-powered tools for benefits in the supply chain. We draw on the unified theory of acceptance and use of technology and suggest directions for research that leverage a mixed-methods research approach. Because these technologies are in a nascent stage and evolving rapidly, a mixed-methods approach will allow for a careful examination of potential features and how their adoption can be fostered, with specific benefits in mind. We present three-specific research directions rooted in the developmental, completeness, and expansion purposes of mixed-methods research. [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: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Aug2024, Vol. 62 Issue 15, p5367-5377. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Technological+innovations%22">Technological innovations</searchLink><br /><searchLink fieldCode="DE" term="%22Innovation+adoption%22">Innovation adoption</searchLink><br /><searchLink fieldCode="DE" term="%22Supply+chain+management%22">Supply chain management</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Research+personnel%22">Research personnel</searchLink>
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  Data: Artificial intelligence (AI) is becoming a critical engine that powers a range of technology solutions. It is rapidly playing a vital role in supply chain and operations management. In this paper, we present a research agenda of how researchers and practitioners alike can study the potential adoption of AI-powered tools for benefits in the supply chain. We draw on the unified theory of acceptance and use of technology and suggest directions for research that leverage a mixed-methods research approach. Because these technologies are in a nascent stage and evolving rapidly, a mixed-methods approach will allow for a careful examination of potential features and how their adoption can be fostered, with specific benefits in mind. We present three-specific research directions rooted in the developmental, completeness, and expansion purposes of mixed-methods research. [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: 10.1080/00207543.2023.2192309
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        Text: English
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      – SubjectFull: Innovation adoption
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      – SubjectFull: Supply chain management
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      – SubjectFull: Artificial intelligence
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              Text: Aug2024
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