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

rate (Epstein, Jacobson, Bales, McDonald, & Munson, 2015; Shih, Han, Poole, Rosson, & Carroll, 2015) to provide users for getting easy access to their health- related information at any time and at any place (Akter, D’Ambra, & Ray, 2010). In current years WFT sales have risen and moreover, are going to be increased continuously by 16.5% annually by 2023 (Prescient & Strategic Intelligence, 2018). The readily available WFTs not only keep tracking and monitoring physical activity but that also motivate or remind their users to do more—e.g., walked at least 250 steps per hour—are one way to promote healthy habits among health- conscious individuals (Lidynia, Brauner & Ziefle, 2017). Conversely, younger adults or generation Y who were born between the early 1980s and early 2000s (Horovitz, 2012), consists of about 35% of the total population of Bangladesh (Statistics, 1991), accustomed to sedentary lifestyle like reading b ooks or magazines, spending time in gossiping with friends and family or watching television, entertaining oneself by engaging in video games, or spending time in mobile phone/ computer for significant time of the day instead of practicing sufficient physical activities like walking, playing, which is injurious to health and major contributor of many curable causes of m ortality (WHO). Kruk (2009), clearly mentioned some adverse effects of the physical inactivity and the consequential health problems on countries. As a result, individuals either show their absenteeism in the workplace or they cannot exhibit their optimum performance due to illness; and a substantial number of resources have to expend for ensuring better treatment of these people that could be better used otherwise. Since younger adults in Bangladesh, similar to other developed nations, have no techno phobia as compared to other group of the marketplace (Sinkovics, Stöttinger, Schlegelmilch, & Ram, 2002), the mobility of a WFT, a big part of the internet of things (Sun, Ji, Wang, & Liu, 2016), has presented a ubiquitous stage for communication and personal fitness (Vooris, Blaszka, & Purrington, 2019). Rather than considered as ‘technology’, many younger adults also perceive WFT as ‘fashion’ or ‘fashionology’ (Hein & Rauschnabel, 2016). WFT could also move beyond the traditional devices that typically monitor health parameters in performance and hence can potentially obsolete these technologies in forthcoming healthcare system. Whilst the efficacy of WFT is promising in this digital age (Butryn, Arigo, Raggio, Colasanti, & Forman, 2016; Mercer et al., 2016; Strath et al., 2011), their adoption has not met the expectations like other tablets (Chau et al., 2019). This is because there has been relatively little research on the influence of age on fitness devices (Ehmen et al., 2012), although researchers on psychology have brought forward the role of age differences on technology adoption over the II. L iterature R eview and R esearch M odel Several models have been used for measuring innovative technology adoption. Models such as the Theory-of-Reasoned-Action (TRA) (Fishbein & Ajzen, 1975), Technology-Acceptance-Model (TAM) (Davis, 1989), TAM2 (Davis, Bagozzi & Warshaw, 1989), TAM3 (Venkatesh et al. 2008), the Theory-of-Planned-Behavior (TPB) (Ajzen, 1991), the Unified-Theory-of-Acceptance- and-Use-of-Technology (UTAUT) (Venkatesh et al., 2003), UTAUT2 (Venkatesh, Thong & Xu, 2012), the Combined-TAM-and-TPB (C-TAMTPB) (Taylor & Todd, 1995), the Innovation-Diffusion-Theory (IDT) (Rogers, 1995) are employed to study the acceptance and/or use of technology. However, the research domain of technology acceptance has been ruled by the TAM and UTAUT, which are extensively used for inspecting IS adoption intention (Rahia et al., 2018). On the other hand, both of them were developed for measuring technology adoption from the organizational perspective (Venkatesh et al., 2012). Further, the explanatory power of the endogenous variables of those two models is low compared to the model UTAUT2, which was developed to measure individual consumer acceptance of technology. For instance, UTAUT model depict 56% of the variance in BI and 40% of the variance in use behavior (Venkatesh et al., 2012). Further, the UTAUT has four predicting variables (performance expectancy, effort expectancy, social influence, and facilitating conditions) whereas UTAUT2 is extended with three additional predicting constructs (habit, hedonic motivation, and price value). In addition, the explanatory power of UTAUT2 is better than the UTAUT (Venkatesh Understanding the Age Differences in Adopting WFTs: An Extension of the UTAUT2 Model 2 Global Journal of Management and Business Research Volume XXIII Issue I Version I Year 2023 ( )E © 2023 Global Journals technological innovations, e. g., smartphones and last few decades (Girard, 1993; Mc Carty & Shrum, 1993; Minton & Schneider, 1985). Additionally, WFTs are still in the early stage of commercialization, not much literature has been found on WFT in the context of Bangladesh. Therefore, this study seeks to investigate the intention of Bangladeshis to use WFTs and to comprehend the role of factors that affect this. To do this, this study could potentially present a starting point for the eventual successful introduction and implementation of the technology to address the issue of low physical activity levels among the Bangladeshi population. The results from this research may form part of the basis on which WFTs can be successfully implemented in Bangladesh to facilitate increased levels of physical activity among the population. Additionally, this research may contribute to the gap in knowledge that exists in regard to the adoption of wearable fitness trackers and the non-technical factors that affect it, as well as the gap that exists in literature about wearable fitness trackers and technology adoption in developing countries such as Bangladesh.