Out of Control – Privacy Calculus and the Effect of Perceived Control and Moral Considerations on the Usage of IoT Healthcare Devices

Abstract

People are increasingly applying Internet of Things (IoT) devices that help them improve their fitness and provide information about their state of health. Although the acceptance of healthcare devices is increasing throughout the general population, IoT gadgets are reliant on sensitive user data in order to provide full functioning and customized operation. More than in other areas of IoT, healthcare applications pose a challenge to individual privacy. In this study, we examine whether actual and perceived control of collected data affects the willingness to use an IoT healthcare device. We further measure actual behavior as a result of a risk-benefit trade-off within the framework of privacy calculus theory. Our experiment with N = 209 participants demonstrates that while actual control does not affect the willingness to use IoT in healthcare, people have a higher intention to use an IoT healthcare device when they perceive to be in control of their data. Furthermore, we found that, prior to their decision, individuals weigh perceived risks and anticipated benefits of information disclosure, which demonstrates the potential to apply the privacy calculus in the context of IoT healthcare technology. Finally, users’ moral considerations of IoT in healthcare are discussed.

Investigating factors that affect the adoption of COVID-19 contact-tracing apps: A privacy calculus perspective.

“…The privacy calculus (Culnan & Armstrong, 1999) assumes that people weigh the anticipated privacy costs and benefits before they decide to disclose personal information (Dienlin & Metzger, 2016), install an app (Eling et al, 2013), or adopt new technology (Princi & Krämer, 2020). For instance, perceived privacy costs (e.g., privacy threats) have been found to negatively affect online self-disclosure (Bol et al, 2018) or the likelihood to install an app (Eling et al, 2013).…”

“…Several studies found that the anticipation of social benefits increases the likelihood of information disclosure on social networking sites (SNSs; Krasnova et al, 2010) or that the perception of convenience increases likelihood of smart technology adoption (Zheng et al, 2018). Frequently, benefit perceptions are found to be the driving factor of self-disclosure or technology adoption (e.g., Dienlin & Metzger, 2016;Krasnova et al, 2014;Princi & Krämer, 2020). One explanation for this observation can be found in the gratification hypothesis that assumes the expected benefits to outweigh the perceived risks, for instance, because people overrate the gratifications or because they are unaware of the privacy threats (Trepte et al, 2015).…”

Sağlık Hizmetlerinde Nesnelerin İnterneti Uygulamalarının Bibliyometrik Analizi

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What Motivates People to Pay for Online Sports Streaming? An Empirical Evaluation of the Revised Technology Acceptance Model

“…The technology acceptance model (TAM) is the most frequently adopted theory in explaining technology usage (Princi and Krämer, 2020). Due to the simple construction of TAM, it has been widely adopted to investigate users' acceptance of different kinds of technologies.…”