Antecedents of Consumers’ Intention to Adopt Wearable Healthcare Devices
Wearable technologies are considered as the possibility of enhancing healthcare productivity and decrease healthcare charge. Regardless of the significance of this technology, inadequate studies have focused on the antecedents of factors influencing consumers’ intention for the adoption of wearable devices. This study aimed to determine the significant factors which have an influence on consumers’ intention for the adoption of wearable healthcare devices. The current study adopts a Technology Acceptance Model (TAM) to explore an individual’s intention for wearable health technology adoption. Data for this study was obtained from 176 Malaysian researchers. The Structural Equation Model (SEM) was performed for testing the proposed research model. The obtained results from SEM indicated that perceived usefulness, perceived ease of use, initial trust and functionality have a statistically significant influence on consumers ‘intention for adoption of wearable healthcare devices. The results of this study will aid the manufacturers and providers to how increase the use of wearable healthcare devices in the healthcare.
Adukaite, A., Reimann, A.M., Marchiori, E. and Cantoni, L. (2013). Hotel mobile apps. The case of 4 and 5 star hotels in European German-speaking countries. In Information and communication technologies in tourism 2014. Springer, pp.45–57.
Ahani, A., Rahim, N. Z. A., & Nilashi, M. (2017). Forecasting social CRM adoption in SMEs: A combined SEM-neural network method. Computers in Human Behavior, 75, 560-578.
Asadi, S., Abdullah, R., Safaei, M. and Nazir, S. (2019). An Integrated SEM-Neural Network Approach for Predicting Determinants of Adoption of Wearable Healthcare Devices. Mobile Information Systems.
Asadi, S., Hussin, A.R.C. and Dahlan, H.M. (2018). Toward Green IT adoption: from managerial perspective. International Journal of Business Information Systems. 29(1), 106–125.
Asadi, S., Hussin, A.R.C., Dahlan, H.M. and Yadegaridehkordi, E. (2015). Theoretical model for Green Information Technology adoption. ARPN Journal of Engineering and Applied Sciences. 10(23).
Asadi, S., Nilashi, M., Husin, A.R.C. and Yadegaridehkordi, E. (2017)(a). Customers perspectives on adoption of cloud computing in banking sector. Information Technology and Management. 18(4), 305–330.
Asadi, S., Nilashi, M., Husin, A.R.C. and Yadegaridehkordi, E. (2017)(b). Customers perspectives on adoption of cloud computing in banking sector. Information Technology and Management. 18(4), 305–330.
Barnes, K., Kauffman, V. and Connolly, C. (2014). Health wearables: Early days. PwC Health Research Institute Report.
Basoglu, N., Ok, A.E. and Daim, T.U. (2017). What will it take to adopt smart glasses: A consumer choice based review? Technology in Society. 50, 50–56. Available at: http://dx.doi.org/10.1016/j.techsoc.2017.04.005.
Bertrand, M. and Bouchard, S. (2008). Applying the technology acceptance model to vr with people who are favorable to its use. Journal of Cyber Therapy and Rehabilitation. 1(2), 200–207.
Canhoto, A.I. and Arp, S. (2017). Exploring the factors that support adoption and sustained use of health and fitness wearables. Journal of Marketing Management. 33(1–2), 32–60.
Chae, J.M. (2009). Clothing & Textiles: Consumer acceptance model of smart clothing according to innovation. International Journal of Human Ecology. 10(1)(June 2009), 23–33.
Davis, F.D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of. MIS Quarterly. 13(3), 319–340.
Dehghani, M. (2018). Exploring the motivational factors on continuous usage intention of smartwatches among actual users. Behaviour and Information Technology. 37(2), 145–158.
Fotiadis, D.I., Glaros, C. and Likas, A. (2006). Wearable Medical Devices. Wiley Encyclopedia of Biomedical Engineering. (3).
Goudarzi, S., Ahmad, M.N., Zakaria, N.H., Soleymani, S.A., Asadi, S. and Mohammadhosseini, N. (2013). Development of an instrument for assessing the Impact of trust on Internet Banking Adoption. J Basic Appl Sci Res. 3(5), 1022–1029.
Gu, Z., Wei, J. and Xu, F. (2016). An Empirical Study on Factors Influencing Consumers’ Initial Trust in Wearable Commerce. Journal of Computer Information Systems. 56(1), 79–85.
Haghi, M., Thurow, K. and Stoll, R. (2017). Wearable devices in medical internet of things: Scientific research and commercially available devices. Healthcare Informatics Research. 23(1), 4–15.
Hair Jr, J.F., Hult, G.T.M., Ringle, C. and Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM), Sage Publications.
Kalantari, M. and Rauschnabel, P. (2018). Exploring the Early Adopters of Augmented Reality Smart Glasses: The Case of Microsoft HoloLens. , 229–245.
Kim, Y.J. and Sim, J.B. (2012). Acceptance-diffusion strategies for tablet-PCs: Focused on acceptance factors of non-users and satisfaction factors of users. ETRI Journal. 34(2), 245–255.
Lee, S.Y. and Lee, K. (2018). Factors that influence an individual’s intention to adopt a wearable healthcare device: The case of a wearable fitness tracker. Technological Forecasting and Social Change. 129(December 2017), 154–163.
Li, H., Wu, J., Gao, Y. and Shi, Y. (2016). Examining individuals’ adoption of healthcare wearable devices: An empirical study from privacy calculus perspective. International Journal of Medical Informatics. 88(555), 8–17.
Liébana-Cabanillas, F., Marinković, V. and Kalinić, Z. (2017). A SEM-neural network approach for predicting antecedents of m-commerce acceptance. International Journal of Information Management. 37(2), 14–24.
Liébana-Cabanillas, Marinković, K. (2017). A SEM-neural network approach for predicting antecedents of m-commerce acceptance-annotated.
Magrath, V. and McCormick, H. (2013). Marketing design elements of mobile fashion retail apps. Journal of Fashion Marketing and Management: An International Journal. 17(1), 115–134.
Nilashi, M., Jannach, D., bin Ibrahim, O., Esfahani, M. D., & Ahmadi, H. (2016). Recommendation quality, transparency, and website quality for trust-building in recommendation agents. Electronic Commerce Research and Applications, 19, 70-84.
Potnis, D., Demissie, D. and Deosthali, K. (2017). Students’ intention to adopt Internet-based personal safety wearable devices: Extending UTAUT with trusting belief. First Monday. 22(9).
Rauschnabel, P.A. and Ro, Y.K. (2016). Augmented reality smart glasses: an investigation of technology acceptance drivers. International Journal of Technology Marketing. 11(2), 123.
Roman, D.H., Conlee, K.D., Abbott, I., Jones, R.P., Noble, A., Rich, N., Ro, I., Kaufman, J., Weikert, R. and Costa, D. (2015). The Digital Revolution comes to US Healthcare,
Sternad Zabukovšek, S., Kalinic, Z., Bobek, S. and Tominc, P. (2018). SEM–ANN based research of factors’ impact on extended use of ERP systems. Central European Journal of Operations Research.
Tan, G.W.-H., Ooi, K.-B., Leong, L.-Y. and Lin, B. (2014). Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-Neural Networks approach. Computers in Human Behavior. 36, 198–213. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0747563214001745 [Accessed August 4, 2014].
Tarute, A., Nikou, S. and Gatautis, R. (2017). Mobile application driven consumer engagement. Telematics and Informatics. 34(4), 145–156.
Wen, D., Zhang, X. and Lei, J. (2017). Consumers’ perceived attitudes to wearable devices in health monitoring in China: A survey study. Computer Methods and Programs in Biomedicine. 140, 131–137.
Wu, J.-H. and Wang, S.-C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & management. 42(5), 719–729.
Yadegaridehkordi, E., Iahad, N. A., & Asadi, S. (2015). Cloud computing adoption behaviour: an application of the technology acceptance model. Journal of Soft Computing and Decision Support Systems, 2(2), 11-16.
Yadegaridehkordi, E., Nilashi, M., Nasir, M. H. N. B. M., & Ibrahim, O. (2018). Predicting determinants of hotel success and development using Structural Equation Modelling (SEM)-ANFIS method. Tourism Management, 66, 364-386.
Yadegaridehkordi, E., Shuib, L., Nilashi, M. and Asadi, S. (2019b). Decision to adopt online collaborative learning tools in higher education: A case of top Malaysian universities. Education and Information Technologies. 24(1), 79–102.
Yadegaridehkordi, E., Shuib, L., Nilashi, M., & Asadi, S. (2019a). Decision to adopt online collaborative learning tools in higher education: A case of top Malaysian universities. Education and Information Technologies, 24(1), 79-102.
Yang, H., Yu, J., Zo, H. and Choi, M. (2016). User acceptance of wearable devices: An extended perspective of perceived value. Telematics and Informatics. 33(2), 256–269.
Zhang, M., Luo, M., Nie, R. and Zhang, Y. (2017). Technical attributes, health attribute, consumer attributes and their roles in adoption intention of healthcare wearable technology. International Journal of Medical Informatics. 108(September), 97–109.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.