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

et al., 2012). The variance explained in BI and UB is 74% and 56% respectively in the UTAUT2, considered as substantial (Venkatesh et al., 2012). However, previous research for investigating WFT adoption and use in the different contexts of the world used UTAUT2. For instance, Owen, Archibald, & Wickramanayake (2019) studied WFT adoption using UTAUT2 in Jamaica. Talukder et al. (2019) also used UTAUT2 in the Chinese context for investigating the key predicting variables of WFT adoption and use. Owen et al., (2019) extended the UTAUT2 by integrating ‘perceived privacy and security risk’ in the model as external variables. Further, Talukder et al. (2019) also extended the UTAUT2 model incorporating ‘compatibility’ and ‘innovativeness’ in the model. Further, Sergueeva, Shaw, & Lee (2020) suggest that the UTAUT2 model should further develop by incorporating other antecedents. Considering the previous studies as well as better predictability of UTAUT2 for individual acceptance and use of technology, the current study also employed the UTAUT2 model. In addition, the model is further extended by encompassing ‘health consciousness’ of consumer as an additional variable to predict the WFT adoption and use in Bangladesh. The reason for incorporating health consciousness in the model is that, according to Hong (2011), it can influence the health behavior. Further, Cho, Park, & Lee (2014), regarding health technology use, noted that there is a substantial rapport between health consciousness and behavioral intention. The current study also considered the role of age as moderating variable to investigate the differential effect of age on the relationship between exogenous and endogenous variables. The baseline UTAUT model and the extended UTAUT2 model also considered the age as moderating variable and found significant differences on the relationship between exogenous and endogenous variables. Researchers also stressed to investigate how age moderate the relationship among major accountable predictors of technology embrace- ment (Venkatesh et al., 2003). Though age plays a critical role as moderator on the technology adoption and use (Venkatesh et al., 2003; Venkatesh et al., 2012), the variable did receive scant attention in the WFT adoption and usage in both developing and developed countries. However, the proposed model is presented in the Fig 1. III. H ypotheses D evelopment a) Performance Expectancy (PE) PE, the premier construct of UTAUT2 model, is explicated by Venkatesh, Thong, and Xu (2012) as it refers to the magnitude to which one perceives that a certain job will be successfully carried out by using innovative technologies. In regard to WFTs, this predictor variable sets point to which an individual believes that the device has potential working power to track and monitor his/her physiological condition in a daily basis which finally do well his/her physical and mental health by reducing health related hazards. Reyes-Mercado and Technology (2018) reverberated that PE plays a pivotal role to augment the behavioral intention of users to adopt fitness wearable. Other studies reveal that older adult users residing in the community form certain specific expectations from technologies (Choudrie, Alfalah, & Spencer, 2017), which are not consistent with those expected by other segments of the marketplace. The above discussion wielded this research work to posit the following hypothesis: H 1 : PE is positively associated with the elderly's intention to use WFT. b) Effort Expectancy (EE) Another cabbalistic construct of extended UTAUT model, EE point to the level of simplicity related with the use of a system (Venkatesh, Morris, Davis, & Davis, 2003). Technologies which are adroit at hassle avoidance and simple to use, users generally feel free to adapt these innovations swiftly to their own lives (Alalwan, Dwivedi, & Rana, 2017). In respect of WFTs, it is important to design these devices as unobtrusive technology which are easy to learn and get-at-able to operate for which consumers will hold firm determination to motivate users to adopt the technology (Dwivedi et al., 2016; Oliveira et al., 2014). In previous studies, it is explored that customers’ chance of attaining comfort depends on the types of click and the age of customers (Venkatesh et al., 2003). Many of prior findings confirmed that perceived and real ease-of-use should take into consideration for making older adults highly inclined to adopt the technology (Cimperman et al., 2016). Therefore, we postulated the following hypothesis: H 2 : EE is positively associated with the elderly's intention to adopt WFT. c) Social Influence (SI) SI, another important constructs which have profound effect on consumer behavior to adopt a technology, is demarcated as the extent to which a person discerns that other people, who are placed by that person as reference group to which she/he belongs or hope to belong, envisage that a particular innovation should be adopted by him or her (Davis, 1989; Venkatesh et al, 2012). Although Venkatesh et al. (2003) did not notice a considerable effect on users’ intention to adopt a technology in an organizational context in their study, Venkatesh et al. (2012) observe a remarkable influence of SI. A number of extant literature revealed that consumers are likely to conform to others’ Understanding the Age Differences in Adopting WFTs: An Extension of the UTAUT2 Model 3 Global Journal of Management and Business Research Volume XXIII Issue I Version I Year 2023 ( )E © 2023 Global Journals