This study presents a research model specifically contextualised to explain and substantiate the enablers and inhibitors of automotive blue-collar workers’ AI usage intentions. The identified factors therein stem from a multiple case study approach including interviews with 24 workers from three different German car manufacturers at four different production sites. The model includes 15 different behaviour-affecting expectations, three of which have never been mentioned in the literature before and can thus be regarded as completely new in that context. The proposed model addresses research gaps in both the individual-level technology adoption research literature and the literature on the application of AI in the automotive environment. It helps to pave the way towards a lean and user-centric development of AI-based systems in the future which do not fail at the threshold of individual-level user acceptance.
Demlehner, Quirin, "Designing AI-based systems to last: Identifying the enablers and inhibitors for the AI usage intentions of automotive blue-collar workers" (2021). ECIS 2021 Research Papers. 22.
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