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Internet technologies (Zhang, Cai, Le, Fei, & Ma, 2019) and smart technologies (Maestre-Gongora & Bernal, 2019), such as the Internet of Things (IoT), are now considered to be part of individuals’ day-to-day experience, revolutionizing the ways in which users interact in their personal and professional lives. IOT analytics (2014) splits this market into two categories: business-facing and consumer-facing. For the purposes of this study, the authors focus on the consumer-facing category, segmented into four sub-categories1: home, lifestyle (including wearables), health, and mobility.
With the expansion of the digital era, the use of wearables has grown in popularity in various fields and contexts. Wearables can be defined as smart objects that consumers can wear, such as smartglasses (Rauschnabel, He, & Ro, 2018), fashionable wearables (Wang, Yu, & Ma, 2018), or smartwatch devices (Jung, Kim, & Choi, 2016). As noted by Hsiao and Chen (2018), as wearable computers, smartwatches can facilitate everyday tasks, increase the efficiency of their users, and monitor parameters such as health or wellbeing. Several researchers have analyzed the factors influencing the acceptance of wearable devices (e.g. Kalantari, 2017; Lunney, Cunningham, & Eastin, 2016). One of the key enablers of smartwatch diffusion relates to the capacity of activity trackers, which offer high potential in the healthcare field (Zhang et al., 2017). As the smartwatch industry produces a greater number of low-cost products, the number of users has inevitably increased. Hence, the economic model for smartwatches is enabling a growth in their adoption around the world. According to Allied Market Research (2019)2, in 2017, the smartwatch industry was valued at US$ 9,264.9 million, and it is forecast to grow to US$ 31,070.6 million by 2025. These products are gaining in popularity, not only because they are economically affordable, but also because of their range of functionalities. As a result, the dynamics of the smartwatch industry have captured the attention of both industry actors and academia. For the purposes of this study, the authors mobilized the technology acceptance model (TAM) (Davis, 1989) as it is a useful approach for understanding technology adoption behaviour in the first wave (early adopters). Perceived-usefulness (PU) and perceived-ease-of-use (PEU), which are the foundations of other robust acceptance models (Venkatesh & Davis, 2000; Venkatesh, Morris, Davis, & Davis, 2003; Venkatesh, Thong, & Xu, 2012), were also deployed, as they have a high capacity for predicting behaviour intention toward determined technology (Davis, 1989). However, while earlier research has extensively used the TAM and its modifications (Choi & Kim, 2016; Chuah, et al., 2016; Dutot, Bhatiasevi, & Bellallahom, 2019; Wu, Wu, & Chang, 2016), the relationship between the TAM variables and perceived-playfulness (PP) and perceived-connectivity (PC) are still to be investigated, as are the moderating impacts of age and gender, as well as the impact of culture, on the use of smartwatches.
The constructs used in this study were chosen on the basis that: (i) the TAM is a popular model frequently used to measure and explain the acceptance of innovative technology (Legris, Ingham, & Collerette (2003). (ii) TAM is the foundation of other models such as the Unified Theory of Acceptance And Use of Technology (Venkatesh et al. 2003). (iii) the TAM is more accurate if including in a broader model considering human aspects (Legris et al., 2003) such as the perceived connectivity and perceived playfulness. Finally, (iv) TAM is recommended to analyse the early stage of adoption (Davis, 1989).
Thus, the aim of this study is to answer the following research questions: (1) What are the variables impacting (or not) attitude-towards-using a smartwatch? (2) What is the influence of PEU on PU and PP? (3) Does PC influence PU and PEU? (4) Does gender, age, or culture moderate the relationships between the variables explaining ATU?