Understanding Privacy Risk Perceptions of Consumer Health Wearables - An Empirical Taxonomy

Perceived privacy risks are an important factor for the adoption of consumer health wearables. However, little is known about the nature of those risk perceptions. We develop a perceived privacy risk taxonomy, which is derived based on an established numerical approach in biology. We apply a four-step mixed-method taxonomy development process to empirically explore privacy perceptions of consumer health wearables. 60 hours of interviews with two different samples, multidimensional scaling, property fitting, and qualitative data analysis enable us to uncover the structure of the mental perception of respondents’ privacy risk perceptions. Our taxonomy reveals that the most relevant dimensions to distinguish consumer health wearables according to the respondents’ perceptions of privacy risks refer to the perceived data sensitivity, perceived data variety, and perceived tracking activity. The developed taxonomy helps researchers to enhance the understanding of privacy perceptions of consumer health wearables and provides practitioners with a comprehensive nomenclature.