Asian Journal for Public Opinion Research

Center for Asian Public Opinion Research & Collaboration Initiative (충남대학교 아시아여론조사학회)
권호 표지
DOI QR코드

DOI QR Code

Technological and Personal Factors of Determining the Acceptance of Wrist-Worn Smart Devices

  • Received : 2019.04.22
  • Accepted : 2019.07.18
  • Published : 2019.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

With much attention being paid to the rapid growth of wrist-worn smart devices, this study aimed to examine the micro-processes that determine an individual's adoption of smart bands and smartwatches. Primarily relying on the theoretical background of the extended technology acceptance model (TAM II), this study explored relationships between three groups of predictors-social, personal, and device-oriented-and the three main components of the original TAM: perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention (BI). Results from the path analysis indicated multiple factors played significant roles in increasing the PU, PEOU, and BI of wristworn smart devices: subjective norms, social image, self-efficacy, perceived service diversity, and perceived reasonable cost. The main findings from this research contribute to significantly improving the understanding of the main factors leading people to adopt wrist-worn smart devices.

Keywords

References

  1. Agarwal, R. & Prasad, J. (1998). Conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9, 204-215. doi: 10.1287/isre.9.2.204
  2. A rsand, E., Muzny, M., Bradway, M., Muzik, J., & Hartvigsen, G. (2015). Performance of the first combined smartwatch and smartphone diabetes diary application study. Journal of Diabetes Science and Technology, 9, 556-563. doi: 10.1177/1932296814567708
  3. Baek, H., Chon, B., & Lee, J. (2013). Determinants of intention to use N-screen service among college students. Korean Journal of Broadcasting, 27(1), 94-130.
  4. Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W. H. Freeman.
  5. Bruner, G. C., & Kumar, A. (2007). Attitude toward location-based advertising. Journal of Interactive Advertising, 7, 3-15. doi: 10.1080/15252019.2007.10722127
  6. Cho, B. J., & Lee, J. S. (2016). Adoption factors of smart watch: Focusing on moderate effects of innovation resistance. Journal of Broadcasting and Telecommunication Research, 93, 111-136.
  7. Choi, G., & Chung, H. (2013). Applying 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, 619-628. doi: 10.1080/10447318.2012.756333
  8. 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. doi: 10.1016/j.chb.2016.06.007
  9. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982-1003. doi: 10.1287/mnsc.35.8.982
  10. Eastin, M. S., & LaRose, R. (2000). Internet self-efficacy and the psychology of the digital divide. Journal of Computer-Mediated Communication, 6. doi: 10.1111/j.1083-6101.2000.tb00110.x
  11. Fulk, J. (1993). Social construction of communication technology. Academy of Management Journal, 36, 921-950. 10.1002/9781118955567.wbieoc190
  12. Gope, C. (2015). Use of a smartwatch for seizure/abnormal motion activity monitoring and tracking. Epilepsy & Behavior, 46, 52-53. https://doi.org/10.1016/j.yebeh.2015.02.049
  13. Haghi, M., Thurow, K., & Stoll, R. (2017). Wearable devices in medical Internet of things: Scientific research and commercially available devices. Healthcare Informatics Research, 23(1), 4-15. doi: 10.4258/hir.2017.23.1.4
  14. Hong, J-. C., & Lin, P-.H., & Hsieh, P-.C. (2017). The effect of consumer innovativeness on perceived value and continuance intention to use smartwatch. Computers in Human Behavior, 67, 264-272. doi: 10.1016/j.chb.2016.11.001
  15. Hurt, H. T., Joseph, K., & Cook, C. D. (1977). Scales for the measurement of innovativeness. Human Communications Research, 4 (1), 58-65. doi: 10.1111/j.1468-2958.1977.tb00597.x
  16. Izuagbe, R., & Popoola, S. O. (2017). Social influence and cognitive instrumental factors as facilitators of perceived usefulness of electronic resources among library personnel in private universities in South-west, Nigeria. Library Review, 66(8), 679-694. doi: 10.1108/LR-09-2016-0086
  17. Joachim, Vw., Spieth, P., & Heidenreigh, S. (2018). Active innovation resistance: An empirical study on functional and psychological barriers to innovation adoption in different contexts. Industrial Marketing Management, 71, 95-107. doi: 10.1016/j.indmarman.2017.12.011
  18. Kang, T. (2014). How users' perceived ubiquity of mobile service influences on the satisfaction and the continuance usage intention. Journalism & Communication, 18(4), 5-34.
  19. Lee, H., & Chang, E. (2011). Consumer attitudes toward online mass customization: An application of extended technology acceptance model. Journal of Computer-Mediated Communication, 16, 171-200. doi: 10.1111/j.1083-6101.2010.01530.x
  20. Lee, J., Kang, J., Ahn, I., Oh, M., & Kim, H. (2014). A study on factors influencing usage intention of wearable device adoption of the early users through TAM. In Proceedings of 2014 Conference on Korea Society of IT Services, 507-510.
  21. Lee, H. S., & Lim, J. H. (2007). Kujobangjeongsik modeling gwa AMOS 6.0 [Structural equation modeling analysis and AMOS 6.0.] Seoul, Korea: Bubmunsa Corporation.
  22. Legris, P, Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40, 191-204. doi: 10.1016/S0378-7206(01)00143-4
  23. Lim, S., Shin, J., Kim, S., & Park, J. (2015). Expansion of smartwatch touch interface from touchscreen to around device interface using infrared line image sensors. Sensors, 15, 16642-16653. doi: 10.3390/s150716642
  24. Moore, G. C., & Benbasat, I. (1991). Development of an investment to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2, 192-212. https://doi.org/10.1287/isre.2.3.192
  25. Oh, K. (2012). N Screen service jamjaejeok sooyongja ui sooyong uido younghyang yoin yungu [Determinants of intention to use toward N Screen service for potential user]. The Journal of the Korea Contents Association, 12(9), 80-92. https://doi.org/10.5392/JKCA.2012.12.09.080
  26. Ozturk, A. B., Bilgihan, A., Nusair, K., & Okumus, F. (2016). What keeps the mobile hotel booking users loyal? Investigating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience. International Journal of Information Management, 36, 1350-1359. doi: 10.1016/j.ijinfomgt.2016.04.005
  27. Park, N., Lee, K., & Cheong, P. H. (2008). University instructors' acceptance of electronic courseware: An application of the technology acceptance model. Journal of Computer-Mediated Communication, 13, 163-186. doi: 10.1111/j.1083-6101.2007.00391.x
  28. Park, S., Nam, M., & Cha, S. (2012). University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43, 592-605. doi: 10.1111/j.1467-8535.2011.01229.x
  29. Sang, S., Lee, J., & Lee, J. (2009). E-government adoption in Cambodia: A partial least squares approach. Transforming Government: People, Process and Policy, 4, 138-157. doi: 10.1108/17506161011047370
  30. Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44, 90-103. doi: 10.1016/j.im.2006.10.007
  31. Statista. (2019). Wearable device shipments worldwide from 2016 to 2022. Retrieved from https://www.statista.com/statistics/610478/wearable-deviceshipments-worldwide/
  32. Thakur, R., Angriawan, A., & Summey, J. H. (2016). Technological opinion leadership: The role of personal innovativeness, gadget love, and technological innovativeness. Journal of Business Research, 69, 2764-2773. doi: 10.1016/j.jbusres.2015.11.012
  33. Thomas, M. (2011). Deconstructing digital natives: Young people, technology, and the new literacies. New York, NY: Routledge.
  34. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. doi: 10.1287/mnsc.46.2.186.11926
  35. Wu, L-. H., Wu, L-. C., & Chang, S. C. (2016). Exploring consumers' intention to accept smartwatch. Computers in Human Behavior, 64, 383-392. doi: 10.1016/j.chb.2016.07.005
  36. Yang, H., Yu, J., Zo, H., & Choi, M. (2016). User acceptance of wearable devices: An extended perspective of perceived value. Telematics & Informatics, 33(2), 256-269. doi: 10.1016/j.tele.2015.08.007
  37. Zainab, B., & Bhatti, M. A. (2017). Factors affecting e-training adoption: An examination of perceived cost, computer self-efficacy and the technology acceptance model. Behavior & Information Technology, 36, 1261-1273. doi: 10.1080/0144929X.2017.1380703