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Adoption of AI-powered Industrial Robots in Auto Component Manufacturing Companies
Pillai, R. ; Sivathanu, B. ; Mariani, M. ; Rana, Nripendra P. ; Yang, B. ; Dwivedi, Y.K.
Pillai, R.
Sivathanu, B.
Mariani, M.
Rana, Nripendra P.
Yang, B.
Dwivedi, Y.K.
Publication Date
2022
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© 2022 Taylor & Francis. The Version of Record of this manuscript has been published and is available in Production Planning and Control at https://doi.org/10.1080/09537287.2021.1882689.
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Yes
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Accepted for publication
2020-10-08
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Abstract
The usage of AI-empowered Industrial Robots (InRos) is booming in the Auto Component Manufacturing Companies (ACMCs) across the globe. Based on a model leveraging the Technology, Organisation, and Environment (TOE) framework, this work examines the adoption of InRos in ACMCs in the context of an emerging economy. This research scrutinizes the adoption intention and potential use of InRos in ACMCs through a survey of 460 senior managers and owners of ACMCs in India. The findings indicate that perceived compatibility, external pressure, perceived benefits and support from vendors are critical predictors of InRos adoption intention. Interestingly, the study also reveals that IT infrastructure and government support do not influence InRos adoption intention. Furthermore, the analysis suggests that perceived cost issues negatively moderate the relationship between the adoption intention and potential use of InRos in ACMCs. This study offers a theoretical contribution as it deploys the traditional TOE framework and discovers counter-intuitively that IT resources are not a major driver of technology adoption: as such, it suggests that a more comprehensive framework than the traditional RBV should be adopted. The work provides managerial recommendations for managers, shedding light on the antecedents of adoption intention and potential use of InRos at ACMCs in a country where the adoption of InRos is in a nascent stage.
Version
Accepted manuscript
Citation
Pillai R, Sivathanu B, Mariani M et al (2022) Adoption of AI-powered Industrial Robots in Auto Component Manufacturing Companies. Production Planning and Control. 33(16): 1517-1533.
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Article