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The adoption of technologies in the health sector has offered new possibilities to track physiological parameters and daily activities. In particular, self-tracking technologies (STTs) that are new Internet of Things (IoT) smart technologies (Joseph et al., 2017) provide plenty of opportunities for the empowerment of patients (Nelson et al. 2016) and for the healthcare sector, especially by generating data points to be collected and analyzed. For instance, through mobile applications, STTs help manage different diseases, such as diabetes, obesity, and asthma. Having citizens adopt STTs is an essential issue for the healthcare sector regarding the reduction of medical expenses (Rich & Miah, 2017). A study conducted by Strategy Analytics indicates that smartwatch sales grew 20 percent in Q1 2020 (Waltzer, 2020). Indeed, since COVID 19, an increasing number of persons appreciates having a ST device capable of measuring their vital signs, such as oxygen levels.
While STT adoption is growing, and more academic research is being conducted on this topic (Ruckenstein & Schüll, 2017), research has not paid enough attention to the multiple facets of STT adoption. Most of the research models in information systems (IS) that deal with STT are based on the Technology Acceptance Model (TAM) (Choi & Kim, 2016; Chuah et al., 2016; K. J. Kim & Shin, 2015; Wu et al., 2011) and thus acknowledge mainly technical factors (del Río Carral et al., 2017). In their literature review, del Río Carral et al. (2017) also point out that a purely technical approach does not reflect the complexity of self-tracking (ST) practices. They identify two trends in their review: 1) a set of enthusiastic articles that promote STT as a new hope for health management and 2) more nuanced and critical articles that discuss this practice in light of a neoliberal surveillance society (e.g. De Moya and Pallud 2020). Examining STT requires a multidisciplinary approach (De Moya & Pallud, 2017; Tuzovic, 2015) to involve health dimensions (with the promise of improved health), human and social dimensions (especially concerning privacy, human integrity, and normalization of society), and technical dimensions (regarding measurement reliability, a user-friendly interface design, and the methods used to analyze the collected data) (Lupton, 2016; Price et al., 2017). Becker et al. (2017) also suggest taking into account perceived benefits and costs that can influence the continued use of fitness trackers.
A stream of research in sociology deals with risks in society and individuals’ avoidance strategies (Beck, 1992). Some risks are external, such as the risk of a nuclear accident (Beck, 1992). Others correspond to personal risks, such as smoking or eating too much fat or sugar. It is then up to the individuals to manage their own risks based on a benefit-cost calculation. To date, very few studies have focused on benefit-cost models for STT adoption. Therefore, this study is an initial attempt to bridge the existing knowledge gap in the literature. More precisely, the paper aims to address the following research questions:
• What are the perceived benefits and risks associated with STT?
• How do technological, social, and health determinants influence STT adoption?