Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model

Sheshadri Chatterjee, Nripendra P. Rana, Yogesh K. Dwivedi and Abdullah M. Baabdullah

Technological Forecasting and Social Change, 2021, vol. 170, issue C

Abstract: This 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.

Keywords: Artificial Intelligence; Industry 4.0; Manufacturing and production firms; TOE framework; TAM; Leadership support (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (41)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:170:y:2021:i:c:s0040162521003127

DOI: 10.1016/j.techfore.2021.120880

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