Maintenance decision support systems can be equipped with artificial intelligence capabilities. Artificial intelligence algorithms allow for automatically extracting patterns and hidden relationships from data gathered from machinery to support maintenance-related tasks such as diagnosis and prognosis. However, the adoption of such systems in industrial maintenance remains rather hesitant. Currently, research lacks independent, rigorous studies investigating barriers causing this absence of adoption. We modified the unified theory of acceptance and use of technology (UTAUT) for our research to provide a better explanation for this observation. In particular, we extended the model with the constructs of trust and system transparency. We assume that trust is a central factor for artificial intelligence technology acceptance due to the black-box character of such algorithms, which in turn is highly affected by system transparency. Our model can serve as the foundation for better understanding actions that can enable user adoption of such systems.
Wanner, Jonas; Popp, Laurell; Fuchs, Kevin; Heinrich, Kai; Herm, Lukas-Valentin; and Janiesch, Christian, "ADOPTION BARRIERS OF AI: A CONTEXT-SPECIFIC ACCEPTANCE MODEL FOR INDUSTRIAL MAINTENANCE" (2021). ECIS 2021 Research-in-Progress Papers. 40.
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