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• W4311762957 abstract "Predictive Maintenance (PdM) solutions assist decision-makers by predicting equipment health and scheduling maintenance actions, but their implementation in industry remains problematic. Specifically, prior research repeatedly indicates that decision-makers often refuse to adopt the data-driven, system-generated advice in their working procedures. In this paper, we address these acceptance issues by studying how PdM implementation changes the nature of decision-makers’ work and how these changes affect their acceptance of PdM systems. We build on the human-centric Smith-Carayon Work System model to synthesise literature from research areas where system acceptance has been explored in more detail. Consequently, we expand the maintenance literature by investigating the human-, task-, and organisational characteristics of PdM implementation. Following the literature review, we distil ten propositions regarding decision-making behaviour in PdM settings. Next, we verify each proposition’s relevance through in-depth interviews with experts from both academia and industry. Based on the propositions and interviews, we identify four factors that facilitate PdM adoption: trust between decision-maker and model (maker), control in the decision-making process, availability of sufficient cognitive resources, and proper organisational allocation of decision-making. Our results contribute to a fundamental understanding of acceptance behaviour in a PdM context and provide recommendations to increase the effectiveness of PdM implementations." @default.
• W4311762957 created "2022-12-28" @default.
• W4311762957 creator A5002891288 @default.
• W4311762957 creator A5027950123 @default.
• W4311762957 creator A5041186790 @default.
• W4311762957 creator A5088927222 @default.
• W4311762957 date "2022-12-15" @default.
• W4311762957 modified "2023-10-05" @default.
• W4311762957 title "Predictive maintenance for industry 5.0: behavioural inquiries from a work system perspective" @default.
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