Examining individuals’ adoption of healthcare wearable devices: An empirical study from privacy calculus perspective

• Published in: International journal of medical informatics (Shannon, Ireland) Vol. 88; pp. 8 - 17
• Main Authors: Li, He, Wu, Jing, Gao, Yiwen, Shi, Yao
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier Ireland Ltd 01.04.2016
• Subjects: Adaptation, Psychological; Adoption; Adult; Confidentiality - psychology; Female; Healthcare; Humans; Internal Medicine; Male; Middle Aged; Models, Theoretical; Other; Perception; Privacy - psychology; Privacy calculus; Telemedicine - instrumentation; Wearable devices; Privacy calculus; Healthcare; Wearable devices; Adoption
• ISSN: 1386-5056; 1872-8243; 1872-8243
• DOI: 10.1016/j.ijmedinf.2015.12.010

•An adoption model of healthcare wearable technology was proposed and tested.•Individual’s wearable device adoption decision is determined by risk–benefit assessment.•Antecedents of individual’s privacy calculus for wearable device are identified and validated.•Theoretical contributions and practical suggestions were also discussed. Wearable technology has shown the potential of improving healthcare efficiency and reducing healthcare cost. Different from pioneering studies on healthcare wearable devices from technical perspective, this paper explores the predictors of individuals’ adoption of healthcare wearable devices. Considering the importance of individuals’ privacy perceptions in healthcare wearable devices adoption, this study proposes a model based on the privacy calculus theory to investigate how individuals adopt healthcare wearable devices. The proposed conceptual model was empirically tested by using data collected from a survey. The sample covers 333 actual users of healthcare wearable devices. Structural equation modeling (SEM) method was employed to estimate the significance of the path coefficients. This study reveals several main findings: (1) individuals’ decisions to adopt healthcare wearable devices are determined by their risk–benefit analyses (refer to privacy calculus). In short, if an individual’s perceived benefit is higher than perceived privacy risk, s/he is more likely to adopt the device. Otherwise, the device would not be adopted; (2) individuals’ perceived privacy risk is formed by health information sensitivity, personal innovativeness, legislative protection, and perceived prestige; and (3) individuals’ perceived benefit is determined by perceived informativeness and functional congruence. The theoretical and practical implications, limitations, and future research directions are then discussed.