There are numerous risks associated with the interconnection of healthcare provision and the Internet of Things (IoT), with its sensory capabilities shown to reduce confidence in novel technology due to fears of a loss of privacy. There exists a clear omission in the extant literature—consideration of gender differences in Frontline Healthcare Providers’ (FHP) behavioural intentions—which this work aims to address through the analysis of IoT-enabled healthcare applications’ (HAs) behavioural intentions in multicultural and bi-generational (Gen X, Y) context. Essentially, analysing gender and generational differences in relation to the variables (privacy, security and trust that influence risk perception; the latter alongside attitude and perceived behavioural control potentially affect the intention) affecting FHPs’ BI towards IoT enabled HAs. A novel model is presented herein, which combines Planned Behaviour (TPB), Privacy Calculus (PCT), and the trust-risk framework. Questionnaire methodology (n = 401) was applied to both generations under consideration, data was assessed using Partial Least Squares Multi-Group Analysis (PLS-MGA), which showed gender differences in Gen Y, but there was little evidence to suggest that risk perception affects any of the cohort's behavioural intention towards the use of IoT-enabled HA, which in turn should help guide both future institutional policy and application development .
Advances in network technologies, combined with mass production of smart devices equipped with sensors with continuous, bidirectional transmission, and the advent of cloud computing have been the primary drivers behind IoT development and implementation in big data driven infrastructural control (Hassan et al., 2018; Li et al., 2020; Rafique et al., 2020; Razzak et al., 2020). However, the security problems inherent in the wider internet itself remain prevalent in IoT. In truth, each element in IoT's tri-layer structure—perception, transport, and application—requires individual consideration in that respect (Tewari and Gupta, 2020). IoT is now commonplace in modern society, often appearing in the food supply chain, logistics, mining, computing, and healthcare sectors (Pang et al., 2015; Yildirim and Ali-Eldin, 2019). Particularly in the case of the latter, the drive for service improvement has resulted in a broad body of literature considering this advance (Asif-Ur-Rahman et al., 2019; Syed et al., 2019).
The interconnected nature of the IoT enabled healthcare model (Tewari and Gupta, 2020) contains a multitude of risks relating to privacy, security and loss of trust. Limitations on the computational ability of this model means that conventional measures used to tackle security and privacy concerns often cannot be applied (Li et al., 2020), leading to the development of a concept known as the Internet of Medical Things, wherein patient data confidentiality without loss of functionality is held paramount (Li et al., 2020).