Abstract
Modeling how patients adopt personal health technology is a challenging problem: Decision-making processes are largely unknown, occur in complex, multi-stakeholder settings, and may play out differently for different products and users. To address this problem, this chapter develops a soft analytics approach, based on Fuzzy Cognitive Maps (FCM) that leads to adoption models that are specific for a particular product and group of adopters. Its empirical grounding is provided by a case study, in which a group of women decides whether to adopt a wearable remote healthcare monitoring device. The adoption model can simulate different product configurations and levels of support and provide insight as to what scenarios will most likely lead to successful adoption. The model can be used by product developers and rollout managers to support technology planning decisions.
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Detail research material can be found on OSF website (Springer Chapter book: Rahimi et al.): https://osf.io/hp7r2/
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Rahimi, N., Jetter, A.J., Weber, C.M., Wild, K. (2018). Soft Data Analytics with Fuzzy Cognitive Maps: Modeling Health Technology Adoption by Elderly Women. In: Giabbanelli, P., Mago, V., Papageorgiou, E. (eds) Advanced Data Analytics in Health. Smart Innovation, Systems and Technologies, vol 93. Springer, Cham. https://doi.org/10.1007/978-3-319-77911-9_4
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