Smart technology for healthcare: Exploring the antecedents of adoption intention of healthcare wearable technology
Technological advancement and personalized health informa tion has led to an increase in people using and responding to wear able technology in the last decade. These changes are often per ceived to be beneficial, providing greater information and insights about health for users, organizations and healthcare and govern ment. However, to date, understanding the antecedents of its adop tion is limited. Seeking to address this gap, this cross-sectional study examined what factors influence users’ adoption intention of healthcare wearable technology. We used self-administrated online survey to explore adoption intentions of healthcare wear able devices in 171 adults residing in Hong Kong. We analyzed the data by Partial least squares – structural equation modelling (PLS-SEM). The results reveal that perceived convenience and perceived irreplaceability are key predictors of perceived usefulness, which in turn strengthens users’ adoption intention. Additionally, the results also reveal that health belief is one of the key predictors of adoption intention. This paper contributes to the extant literature by providing understanding of how to strengthen users’ intention to adopt healthcare wearable technology. This includes the strengthening of perceived convenience and per ceived irreplaceability to enhance the perceived usefulness, incor porating the extensive communication in the area of healthcare messages, which is useful in strengthening consumers’ adoption intention in healthcare wearable technology.
1. Ahlan, A.R., & Ahmad, B.I.E. (2014). User acceptance of health information technology (HIT) in developing countries: A conceptual model. Procedia Technology, 16, 1287-1296.
2. Ariff, M.S.M., Yeow, S.M., Zakuan, N., & Bahari, A.Z. (2013). Acceptance of Internet Banking Systems among Young Managers. In IOP Conference Series: Materials Science and Engineering (Vol. 46, No. 1, p. 012024). IOP Publishing.
3. Ayeh, J.K. (2015). Travellers’ acceptance of consumer-generated media: An integrated model of technology acceptance and source credibility theories. Computers in Human Behavior, 48, 173-180.
4. Bagozzi, R.P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the association for information systems, 8(4), 3.
5. Benbasat, I., & Barki, H. (2007). Quo vadis TAM?. Journal of the association for information systems, 8(4), 7.
6. Berry, L.L., Seiders, K., & Grewal, D. (2002). Understanding service convenience. Journal of marketing, 66(3), 1-17.
7. Champion, V.L., & Skinner, C.S. (2008). The health belief model. Health behavior and health education: Theory, research, and practice, 4, 45-65.
8. Chan, M., Estève, D., Fourniols, J.Y., Escriba, C., & Campo, E. (2012). Smart wearable systems: Current status and future challenges. Artificial intelligence in medicine, 56(3), 137-156.
9. Cheung, M.L., Pires, G.D. & Rosenberger III, P.J. (2019). Developing a conceptual Model for examining social media marketing effects on brand awareness and brand image. International Journal of Economics and Business Research, 17(3), 243-261.
10. Chin, W. (1998). The partial least squares approach for structural equation modelling. In G. Marcoulides (Ed.), Modern Methods for Business Research (pp. 295–336). Mahwah, NJ: Laurence Erlbaum Associates.
11. Choi, G., & Chung, H. (2013). Applying the technology acceptance model to social networking sites (SNS): Impact of subjective norm and social capital on the acceptance of SNS. International Journal of Human-Computer Interaction, 29(10), 619-628.
12. Choi, J., & Kim, S. (2016). Is the smartwatch an IT product or a fashion product? A study on factors affecting the intention to use smartwatches. Computers in Human Behavior, 63, 777-786.
13. Chuah, S.H.W., Rauschnabel, P.A., Krey, N., Nguyen, B., Ramayah, T., & Lade, S. (2016). Wearable technologies: The role of usefulness and visibility in smartwatch adoption. Computers in Human Behavior, 65, 276-284.
14. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
15. Due, B.L. (2014). The future of smart glasses: An essay about challenges and possibilities with smart glasses. Working papers on interaction and communication, 1(2), 1-21.
16. Featherman, M.S., Miyazaki, A.D., & Sprott, D.E. (2010). Reducing online privacy risk to facilitate e-service adoption: the influence of perceived ease of use and corporate credibility. Journal of Services Marketing, 24(3), 219-229.
17. Gao, Y., Li, H., & Luo, Y. (2015). An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 115(9), 1704-1723.
18. Gong, X., Zhang, K.Z., Cheung, C.M., Chen, C., & Lee, M.K. (2019). Alone or Together? Exploring the Role of Desire for Online Group Gaming in Players’ Social Game Addiction. Information & Management.
19. HealthWorks Collective (2018). Wearable Tech Market Set for Significant Growth. Retrieved December 26, 2018, from https://www.medtechimpact.com/wearable-tech-market-set-for-significant-growth/
20. Holden, R.J., & Karsh, B.T. (2010). The technology acceptance model: its past and its future in health care. Journal of biomedical informatics, 43(1), 159-172.
21. Hur, Y., Ko, Y.J., & Claussen, C.L. (2012). Determinants of using sports web portals: An empirical examination of the sport web-site acceptance model. International Journal of Sports Marketing and Sponsorship, 13(3), 6-25.
22. Indu, R., & Jagathy Raj, V.P. (2012). Developing a theoretical framework for a study on the impact of advertising credibility of consumer healthcare products. European Journal of Commerce and Management Research, 1(1), 14-24.
23. Jeong, S.C., Kim, S.H., Park, J.Y., & Choi, B. (2017). Domain-specific innovativeness and new product adoption: A case of wearable devices. Telematics and Informatics, 34(5), 399-412.
24. Kalantari, M. (2017). Consumers’ adoption of wearable technologies: literature review, synthesis, and future research agenda. International Journal of Technology Marketing, 12(3), 274-307.
25. Kawakami, T., & Parry, M.E. (2013). The impact of word of mouth sources on the perceived usefulness of an innovation. Journal of Product Innovation Management, 30(6), 1112-1127.
26. Kim, H.W., Chan, H.C., & Gupta, S. (2007). Value-based adoption of mobile internet: an empirical investigation. Decision support systems, 43(1), 111-126.
27. Kim, K.J., & Shin, D.H. (2015). An acceptance model for smart watches: Implications for the adoption of future wearable technology. Internet Research, 25(4), 527-541.
28. Knight, H.M., Gajendragadkar, P.R., & Bokhari, A. (2015). Wearable technology: using Google Glass as a teaching tool. BMJ case reports, 2015, bcr2014208768.
29. Korenkova, M., & Hägerfors, A. (2011, January). Quality criteria for digital information in long-term digital preservation. In Archiving Conference (Vol. 2011, No. 1, pp. 34-39). Society for Imaging Science and Technology.
30. Lee, J., Kim, D., Ryoo, H.Y., & Shin, B.S. (2016). Sustainable wearables: wearable technology for enhancing the quality of human life. Sustainability, 8(5), 466.
31. Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & management, 40(3), 191-204.
32. Li, H., Wu, J., Gao, Y., & Shi, Y. (2016). Examining individuals’ adoption of healthcare wearable devices: An empirical study from privacy calculus perspective. International journal of medical informatics, 88, 8-17.
33. Marakhimov, A., & Joo, J. (2017). Consumer adaptation and infusion of wearable devices for healthcare. Computers in Human Behavior, 76, 135-148.
34. MedTech Impact of Wellness (2018). Forces Driving The Growth Of Wearable Medical Device Market. Retrieved December 26, 2018, from: https://www.healthworkscollective.com/forces-driving-the-growth-of-wearable-medical-device-market/
35. Metzger, M.J., Flanagin, A.J., & Zwarun, L. (2003). College student Web use, perceptions of information credibility, and verification behavior. Computers & Education, 41(3), 271-290.
36. Nasir, S., & Yurder, Y. (2015). Consumers’ and physicians’ perceptions about high tech wearable health products. Procedia-Social and Behavioral Sciences, 195, 1261-1267.
37. Ooi, K.B., & Tan, G.W.H. (2016). Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Expert Systems with Applications, 59, 33-46.
38. Özkan, T., Lajunen, T., Doğruyol, B., Yıldırım, Z., & Çoymak, A. (2012). Motorcycle accidents, rider behaviour, and psychological models. Accident Analysis & Prevention, 49, 124-132.
39. Pai, F.Y., & Huang, K.I. (2011). Applying the technology acceptance model to the introduction of healthcare information systems. Technological Forecasting and Social Change, 78(4), 650-660.
40. Pedeliento, G., Andreini, D., Bergamaschi, M., & Salo, J. (2016). Brand and product attachment in an industrial context: The effects on brand loyalty. Industrial Marketing Management, 53, 194-206.
41. Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: an extension of the technology acceptance model. Internet research, 14(3), 224-235.
42. Pradhan, D., Duraipandian, I., & Sethi, D. (2016). Celebrity endorsement: How celebrity–brand–user personality congruence affects brand attitude and purchase intention. Journal of Marketing Communications, 22(5), 456-473.
43. Read, W., Robertson, N., & McQuilken, L. (2011). A novel romance: the technology acceptance model with emotional attachment. Australasian Marketing Journal (AMJ), 19(4), 223-229.
44. Ross, T.P., Ross, L.T., Rahman, A., & Cataldo, S. (2010). The bicycle helmet attitudes scale: using the health belief model to predict helmet use among undergraduates. Journal of American College Health, 59(1), 29-36.
45. Schifferstein, H.N., & Zwartkruis-Pelgrim, E.P. (2008). Consumer-product attachment: Measurement and design implications. International journal of design, 2(3).
46. Shaw, N. (2014). The mediating influence of trust in the adoption of the mobile wallet. Journal of Retailing and Consumer Services, 21(4), 449-459.
47. Shin, D.H. (2009). Towards an understanding of the consumer acceptance of mobile wallet. Computers in Human Behavior, 25(6), 1343-1354.
48. Shin, D.H., Lee, S., & Hwang, Y. (2017). How do credibility and utility play in the user experience of health informatics services?. Computers in Human Behavior, 67, 292-302.
49. Signorini, M.G., Fanelli, A., & Magenes, G. (2014). Monitoring fetal heart rate during pregnancy: contributions from advanced signal processing and wearable technology. Computational and mathematical methods in medicine, 2014.
50. Tehrani, K., & Michael, A. (2014). Wearable technology and wearable devices: Everything you need to know. Wearable Devices Magazine.
51. Ukpabi, D.C., & Karjaluoto, H. (2017). Consumers’ acceptance of information and communications technology in tourism: A review. Telematics and Informatics, 34(5), 618-644.
52. Wang, B.R., Park, J.Y., Chung, K., & Choi, I.Y. (2014). Influential factors of smart health users according to usage experience and intention to use. Wireless personal communications, 79(4), 2671-2683.
53. Wang, C., Lee, M.K., & Hua, Z. (2015). A theory of social media dependence: Evidence from microblog users. Decision Support Systems, 69, 40-49.
54. Wang, Y.S., Wang, Y.M., Lin, H.H., & Tang, T.I. (2003). Determinants of user acceptance of Internet banking: an empirical study. International journal of service industry management, 14(5), 501-519.
55. Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232.
56. Wu, J.H., & Wang, S.C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & management, 42(5), 719-729.
57. Wu, J., Li, H., Cheng, S., & Lin, Z. (2016). The promising future of healthcare services: When big data analytics meets wearable technology. Information & Management, 53(8), 1020-1033.
58. Yoon, C., & Kim, S. (2007). Convenience and TAM in a ubiquitous computing environment: The case of wireless LAN. Electronic Commerce Research and Applications, 6(1), 102-112.
59. Zhang, M., Luo, M., Nie, R., & 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, 97-109.
60. Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in human behavior, 26(4), 760-767.