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AuthorSheshadri, Chatterjee
AuthorRana, Nripendra P.
AuthorDwivedi, Yogesh K.
AuthorBaabdullah, Abdullah M.
Available date2023-07-16T08:42:58Z
Publication Date2021-05-24
Publication NameTechnological Forecasting and Social Change
Identifierhttp://dx.doi.org/10.1016/j.techfore.2021.120880
ISSN0040-1625
URIhttps://www.sciencedirect.com/science/article/pii/S0040162521003127
URIhttp://hdl.handle.net/10576/45655
AbstractThis study aims to identify how environmental, technological, and social factors influence the adoption of Industry 4.0 in the context of digital manufacturing. The Industry 4.0 era has brought a breakthrough in advanced technologies in fields such as nanotechnology, quantum computing, biotechnology, artificial intelligence, robotics, the Internet of Things, fifth-generation wireless technology, fully autonomous vehicles, 3D printing and so on. In this study, we attempted to identify the socioenvironmental and technological factors that influence the adoption of artificial intelligence embedded technology by digital manufacturing and production organizations. In doing so, the extended technology-organization-environment (TOE) framework is used to explore the applicability of Industry 4.0. A conceptual model was proposed that used an integrated technology acceptance model (TAM)-TOE model and was tested using survey-based data collected from 340 employees of small, medium and large organizations. The results highlight that all the relationships, except organizational readiness, organizational compatibility and partner support on perceived ease of use, were found to be significant in the context of digital manufacturing and production organizations. The results further indicated that leadership support acts as a countable factor to moderate such an adoption.
Languageen
SubjectArtificial Intelligence
Industry 4.0
Manufacturing and production firms
TOE framework
TAM
Leadership support
TitleUnderstanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model
TypeArticle
Volume Number170
ESSN1873-5509
dc.accessType Abstract Only


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