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

Understanding the Age Differences in Adopting WFTs: An Extension of the UTAUT2 Model 17 Global Journal of Management and Business Research Volume XXIII Issue I Version I Year 2023 ( )E © 2023 Global Journals 80. Shih, P. C., Han, K., Poole, E. S., Rosson, M. B., & Carroll, J. M. (2015). Use and adoption challenges of wearable activity trackers. Proceedings from iConference ’15: Create, Collaborate, Celebrate. CA, Newport Beach. 81. Sigal, R. J., Kenny G. P., Wasserman D. H., Castaneda-Sceppa C. Physical activity/exercise and type 2 diabetes. Diabetes Care2004; 27 (10): 2518- 39. 82. Sinkovics, R. R., Stcottinger, B., Schlegelmilch, B. B. and Ram, S. (2002), “Reluctance to use technologyrelated products: development of a technophobia scale”, Thunderbird International Business Review, Vol. 44 No. 4, pp. 477-494. 83. Slattery, M. L., Edwards, S., Curtin, K., Ma, K., Edwards, R., Holubkov, R. Physical activity and colorectal cancer. Am JEpidemiol 2003; 158 (3): 214-24. 84. Strath, S. J., Swartz, A. M., Parker, S. J., Miller, N. E., Grimm, E. K., Cashin, S. E., 2011. A pilot randomized controlled trial evaluating motivationally matched pedometer feedback to increase physical activity behavior in older adults. J. Phys. Activ. Health 8 (0 2), S267. 85. Sultan, F., Rohm, A. J. and Gao, T. T. (2009), “Factors influencing consumer acceptance of mobile marketing: a two-country study of youth markets”, Journal of Interactive Marketing, Vol. 23 No. 4, pp. 308-320. 86. Sun, A., Ji, T., Wang, J. and Liu, H. (2016) ‘Wearable mobile internet devices involved in big data solution for education’, International Journal of Embedded Systems, Vol. 8, No. 4, pp.293–299. 87. Talukder, M. S., Chiong, R., Bao, Y., & Hayat Malik, B. (2019). Acceptance and use predictors of fitness wearable technology and intention to recommend: An empirical study. Industrial Management & Data Systems, 119 (1), 170–188. 88. Tavares, J., & Oliveira, T., 2016. Electronic health record portals definition and usage. In Encyclopedia of E-Health and Telemedicine (pp. 555-562). IGI Global. 89. Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19 (4), 561–570. 90. United Nations (2021) Leaving no one behind. https://www.un.org/fr/esa/leaving-no-one-behind Accessed 2 July 2021. 91. Venkatesh, V. (2010). Technology acceptance, summary of technology acceptance models. http://www.vvenkatesh.com/IT/organizations/Theore tical_Models.asp. Accessed 10 Nov 2020. 92. Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different concept- ualizations of system use: The competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Quarterly, 32 (3), 483– 502. 93. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified. MIS Quarterly, 27 (3), 425-478. 94. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36 (1), 157-178. https://doi.org/10.2307/41410412 95. Vooris, R. & Blaszka, M. & Purrington, S. Understanding the wearable fitness tracker revolution. International Journal of the Sociology of Leisure, Springer Nature Switzerland AG 2019 https://doi.org/10.1007/s41978-018-00022-y , 96. Waxman, A. WHO's global strategy on diet, physical activity and health: response to a worldwide epidemic of noncommunicable diseases. Scand J Nutr 2004; 48 (2): 58-60. 97. Wei, J. (2014). How Wearables Intersect with the Cloud and the Internet of Things: Considerations for the developers of wearables. IEEE Consumer Electronics Magazine, 3 (3), 53-56. https://doi.org/ 10.1109/MCE.2014.2317895. 98. Wen, D., Zhang, X., and Lei, J. “Consumers’ perceived attitudes to wearable devices in health monitoring in China: a survey study,” Computer Methods and Programs in Biomedicine, vol. 140, pp. 131–137, 2017. 99. Wendel-Vos, G. C., Schuit, A. J., Feskens, E. J., Boshuizen, H. C., Verschuren, W. M., Saris, W. H. Physical activity and stroke. A meta-analysis of observational data. Int J Epidemiol 2004; 33: 787- 98. 100. WHO: Fiscal policies for diet and the prevention of noncommunicable diseases. https://www.who.int/ dietphysicalactivity/publications/fiscal-policies-diet- prevention/en/. Accessed 06 March 2021. 101. World Health Organization: Global Recommen- dations on Physical Activity for Health. WHO Press, Geneva (2010) 102. Zhang X., Guo X, Lai KH, Guo F, Li C, 2014. Understanding gender differences in mHealth adoption: A modified theory of reasoned action model. Telemedicine and eHealth 20 (1), 39-46. 103. Akter, S., D’Ambra, J., & Ray, P. J. E. M. (2010). Service quality of mHealth platforms: development and validation of a hierarchical model using PLS. 20 (3-4), 209-227. 104. Alalwan, A. A. J. I. J. o. I. M. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. 50 , 28-44. 105. Butryn, M. L., Arigo, D., Raggio, G. A., Colasanti, M., & Forman, E. M. J. J. o. h. p. (2016). Enhancing