The privacy calculus in the context of novel health technology for diagnosing and tracking infectious diseases: The role of disease severity and technology's evidence base for effectiveness in adoption and voluntary health data-sharing

In the past decades, accelerated by the recent COVID pandemic, the field of healthcare has faced technological advancements, such as wearables and mobile applications, that collect personal or health data. However, such tools are ineffective if they are not adopted by a large part of the population or if relevant health data, collected by the application, are not (voluntarily) shared. This study assessed the role of disease severity and evidence base for the effectiveness of the technology in the Privacy Calculus risk-benefit trade-off to contribute or hinder technology acceptance and data sharing. A large-scale 2 x 2 x 2 online vignette experiment (n = 822) was carried out, where participants were presented with a hypothetical scenario describing a novel health technology for diagnosing and tracking of infectious diseases. The results indicated that participants' privacy concerns negatively affected their intention to use the technology and willingness to share data, and that a high severity of the disease weakened this relationship. None of the other expected effects on intentions to use, willingness to share data or privacy concerns, were significant. These findings highlight the role of privacy as a barrier to technology acceptance, and suggest disease severity plays a role in the Privacy Calculus risk-benefit trade off by weakening the negative effect of privacy concerns on adoption in contexts where disease severity is high.