Abstract/Summary

Smart devices, particularly wearable fitness trackers, have gained considerable attention as part of the Internet of Things (IoT) in various industries. These IoT-enabled wearable health devices have the potential to improve quality of life, promote healthy habits, and reduce medical expenses. However, customer concerns about the privacy of their healthcare information pose a challenge to the widespread adoption of these devices. Additionally, many users discontinue their use of wearable devices over time, highlighting the need to understand the factors driving or discouraging their usage. This research addresses the gap in academic literature regarding the factors influencing the initial and continued adoption of wearable technology. Two conceptual models were developed to investigate these factors, focusing on perceived privacy, perceived risk, user satisfaction, and the impact of underlying health conditions. The first model combines the UTAUT2 and DeLone and McLean models to capture the initial adoption of IoT health wearables, while the second model incorporates UTAUT2, DeLone and McLean, and ECM to analyze the continuance adoption of these devices. Quantitative deductive methods were employed, utilizing two separate online surveys—one for pre-users and another for post-users of IoT wearables—to validate the conceptual models. The data collected from 266 pre-users and 365 post-users were analyzed using partial least squares structural equation modeling (SmartPLS). The findings indicate that performance expectancy, social influence, hedonic motivation, system quality, service quality, and expected satisfaction influence potential consumers' intentions to adopt IoT health wearables. Similarly, experienced users are influenced by performance expectancy, price value, hedonic motivation, perceived privacy, information quality, and satisfaction. Information, system, and service quality significantly impact satisfaction in both models. Additionally, the study examines the moderating effect of underlying health conditions on the relationships between perceived privacy and use, perceived risk and use, and satisfaction and data sharing. The results indicate a positive moderating effect between satisfaction and data sharing, but only in the 'continuance use' model. This research provides valuable insights into the factors affecting the adoption and continued use of IoT health wearables, contributing to a comprehensive understanding of the phenomenon. The findings have implications for individuals, healthcare providers, and industries involved in the development and marketing of wearable technology.