Global Journal of Management and Business Research, E: Marketing, Volume 23 Issue 1

health (Lee & Lee, 2017). In addition, HC is an indication of the quality of people’s life that drive him to undertake health actions (Kraft & Goodell, 1993). Prior studies on wearable technology devices have supported that there is a significant relationship between HC and BI (Lee & Lee, 2017; Wen et al., 2017). Patel, Asch (2015) proposed that wearable devices motivate users to increase physical activities, which in turn, improve healthy behavior. However, they admonish that one cannot improve his/her health by simply wearing these devices alone and to gain proper health, one have to engage himself/herself in positive health behavior practices. Hence, we deem that if an individual possess more health interest, she/he shows more intention-to- use the WFT. Based on these literatures, therefore, we posited the following hypothesis: H 8 : HC affects an individual’s intention to adopt WFT. i) Behavioral Intention (BI) The relationship between the behavioral intention (BI) and actual use behavior (AUB) is well documented in many research fields and that indicates BI is the extent to which one intentionally determined to execute a given action (Islam et al., 2013). It has been experimentally proven that BI is positively related with the actual usage behavior of customers in different context (Taylor & Todd, 1995, Alam et al., 2020) BI was repeatedly used to measure as the attitudinal and behavioral loyalty. Furthermore, extant literature revealed that BI has significant impact on actual usage behavior (Goulão, 2014; Cimperman et al., 2016) Therefore, causal link between BI and the wearable use can be hypothesized as: H 9 : BI has a positive impact on the actual use of a WFT. j) Age as Moderator Technology acceptance and use decision is significantly influenced by individual differences (Arning & Ziefle, 2009). Age differences of the users plays a critical role in the technology adoption intention (Zhang et al., 2014). Technology adoption literature attracted the researchers to consider age as a moderator between endogenous and exogenous variables (Tavares and Oliveira, 2016). Morris & Venkatesh (2000) noted that technology usage decision is significantly differ for younger and older users. An empirical study by Alsswey and Al-Samarraie (2019) revealed that the relationship between ease of use and BI and usefulness and BI are significantly and positively influenced by age differences of the respondents (Alsswey and Al- Samarraie, 2019). Further, Zhu et al. (2018) noted that young people has shown strong association in adopting technology than middle-aged and older people. The above evidences helped the author to propose the following hypotheses: H 10a : PE and WFT adoption intention is significantly moderated by age of the respondents. H 10b : EE and WFT adoption intention is significantly moderated by age of the respondents. H 10c : SI and WFT adoption intention is significantly moderated by age of the respondents. H 10d : FC and WFT adoption intention is significantly moderated by age of the respondents. H 10e : FC and WFT use behavior is significantly moderated by age of the respondents. H 10f : HM and WFT adoption intention is significantly moderated by age of the respondents. H 10g : PV and WFT adoption intention is significantly moderated by age of the respondents. H 10h : HT and WFT adoption intention is significantly moderated by age of the respondents. H 10i : HC and WFT adoption intention is significantly moderated by age of the respondents. H 10j : BI and AUB adoption intention is significantly moderated by age of the respondents. Fig. 1: UTAUT2 Model with Extended Construct Source: Venkatesh et al. (2012) Understanding the Age Differences in Adopting WFTs: An Extension of the UTAUT2 Model 5 Global Journal of Management and Business Research Volume XXIII Issue I Version I Year 2023 ( )E © 2023 Global Journals