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Influential Factors of Smart Health Users according to Usage Experience and Intention to Use

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Abstract

As the use of smartphones and applications has increased, the use of healthcare applications also has increased. In the burgeoning applications market, research regarding customers’ characteristics is necessary for the development of specific application products or services that fulfill customers’ various needs. This study aimed to analyze the characteristics of and differences in the influential factors of healthcare application use among smartphone users. A survey was conducted in 300 adults from September 16 to October 15, 2011. The research subjects were categorized into three groups, depending on their usage experience and intention to use healthcare applications. We analyzed the characteristics of each group and the differences in influential factors among the groups. The healthcare application users were female, older than 30 years old, and more educated. The current customers had high levels of self-efficacy and innovativeness. Perceived usefulness negatively affected to actual use of healthcare applications, and both perceived ease of use and enjoyment positively influenced on intention to use. Therefore, developers and managers of healthcare applications should consider these characteristics of current and potential customers to improve adoption of healthcare applications.

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References

  1. Nielsen. (2012). Nielsen Newswire, Nielsen, http://www.nielsen.com, accessed Dec. 10, 2012. Available from: http://www.nielsen.com/us/en/newswire/2012/smartphones-account-for-half-of-all-mobile-phones-dominate-new-phone-purchases-in-the-us.html.

  2. Go-Gulf.com. (2012). Go-Gulf.com, http://www.go-gulf.com, accessed Dec. 10, 2012. Available from: http://www.go-gulf.com/blog/smartphone/.

  3. Ahonen, T. (2012). TomiAhonen phone book 2012—statistical review of handset industry. e-book. TomiAhonen Consulting.

  4. Chung, K. H., Chung, J. H., & Park, E. O. (2011). A study on the antecedent factors for continuous usage of smartphone and recommendation. In Fall Korea distribution association conference 2011; Jeju, Korea, November 11–12, 2011.

  5. eMarketer. (2012). eMarketer Articles, eMarketer, http://www.emarketer.com, accessed Dec. 11, 2012. Available from: http://www.emarketer.com/Article/South-Korea-Enters-New-Smartphone-Age/1008875.

  6. Bandyopadhyay, D. (2009). Fast running technologies and their impact on fast growing industries. Wireless Personal Communications, 49(3), 365–373.

    Article  Google Scholar 

  7. Google play. (2013). Google play android application, Google play, http://www.play.google.com, accessed Jan. 10, 2013. Available from: https://play.google.com/store.

  8. App Store. (2013). Itunes App Store, http://www.itunes.apple.com, accessed Jan. 16, 2013. Available from: https://itunes.apple.com/us/genre/ios/id36?mt=8.

  9. Laakko, T., Leppanen, J., Lahteenmaki, J., & Nummiaho, A. (2008). Mobile health and wellness application framework. Methods of Information in Medicine, 47, 217–222.

    Google Scholar 

  10. Mikalajunaite, E. (2012). research2guidance, http://www.research2guidance.com, accessed Dec. 11, 2012. Available from: http://www.research2guidance.com/500m-people-will-be-using-healthcare-mobile-applications-in-2015/.

  11. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339.

    Article  Google Scholar 

  12. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). NewYork, NY: Free Press.

    Google Scholar 

  13. Kang, Y. M., Cho, C., & Lee, S. (2011). Analysis of factors affecting the adoption of smartphones. In 1st international technology management conference (ITMC) 2011. San Jose, California, US, June 27–30, 2011.

  14. Park, Y., & Chen, J. V. (2007). Acceptance and adoption of the innovative use of smartphone. Industrial Management & Data Systems, 107(9), 1349–1365.

    Article  Google Scholar 

  15. Verkasalo, H., Lpez-Nicols, C., Molina-Castillo, F. J., & Bouwman, H. (2010). Analysis of users and non-users of smartphone applications. Telematics and Informatics, 27, 242–255.

    Article  Google Scholar 

  16. Kanstrup, A. M., Bjerge, K., & Kristensen, J. E. (2010). A living laboratory exploring mobile support for everyday life with diabetes. Wireless Personal Communications, 53(3), 395–408.

    Article  Google Scholar 

  17. Boulos, M. N., Wheeler, S., Tavares, C., & Jones, R. (2011). How smartphones are changing the face of mobile and participatory healthcare: An overview, with example from eCAALYX. Biomedical Engineering Online. doi:10.1186/1475-925X-10-24.

  18. Yang, S., Song, W., & Zhong, Z. (2013). Resource allocation for aggregate multimedia and healthcare services over heterogeneous multi-hop wireless networks. Wireless Personal Communications, 69(1), 229–251.

    Article  Google Scholar 

  19. Kim, J., & Chung, K. Y. (2013). Ontology-based healthcare context information model to implement ubiquitous environment. Multimedia Tools and Applications,. doi:10.1007/s11042-011-0919-6.

    Google Scholar 

  20. Franko, O. I., & Bhola, S. (2011). iPad apps for orthopedic surgeons. Orthopedics, 34(12), 978–981.

    Article  Google Scholar 

  21. Sarasohn-kahn, J. (2010). How smartphones are changing health care for consumers and providers. Oakland, CA: California HealthCare Foundation.

    Google Scholar 

  22. Middendorff, E. (2002). Computernutzung und neue Medien im Studium. Ergebnisse der, 16.

  23. Imhof, M., Vollmeyer, R., & Beierlein, C. (2007). Computer use and the gender gap: The issue of access, use, motivation, and performance. Computers in Human Behavior, 23(6), 2823–2837.

    Article  Google Scholar 

  24. Wang, B. R., Park, J. Y., & Choi, I. Y. (2011). Influencing factors for the adoption of smart phone healthcare application. Journal of Korea Contents Association, 11(10), 396–404.

    Article  Google Scholar 

  25. Kennedy, T., Wellman, B., & Klement, K. (2003). Gendering the digital divide. It & Society, 1(5), 72–96.

    Google Scholar 

  26. Bigné, E., Ruiz, C., & Sanz, S. (2007). Key drivers of mobile commerce adoption an exploratory study of Spanish mobile users. Journal of Theoretical and Applied Electronic Commerce Research, 2(2), 48–60.

    Google Scholar 

  27. Brown, I., Cajee, Z., Davies, D., & Stroebel, S. (2003). Cell phone banking: Predictors of adoption in South Africa—an exploratory study. International Journal of Information Management, 23(5), 381–394.

    Article  Google Scholar 

  28. Myring, J. (2012). HighBeamResarch, http://www.highbeam.com, accessed Dec. 15, 2012. Available from: http://www.highbeam.com/doc/1G1-108911848.html.

  29. Kim, S. H. (2010). Effects of perceived attributes on the purchase intention of smart-phone. Journal of Korea Contents Association, 10(9), 318–326.

    Article  Google Scholar 

  30. Bandura, A. (1997). Self-efficacy; The exercise of control. New York, NY: W. H. Freeman.

    Google Scholar 

  31. Wu, J. H., Wang, S. C., & Lin, L. M. (2007). Mobile computing acceptance factors in the healthcare industry: A structural equation model. International Journal of Medical Informatics, 76(1), 66–77.

    Article  Google Scholar 

  32. Luarn, P., & Lin, H. H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873–891.

    Article  Google Scholar 

  33. Chatzoglou, P. D., Sarigiannidis, L., Vraimaki, E., & Diamantidis, A. (2009). Investigating Greek employees’ intention to use web-based training. Computers & Education, 53(3), 877–889.

    Article  Google Scholar 

  34. Park, I. G., & Shin, D. H. (2010). Using the uses and gratifications theory to understand the usage and the gratifications of smartphones. Journal of Communication Science, 10(4), 192–225.

    Google Scholar 

  35. Yi, M. Y., Fiedler, K. D., & Park, J. S. (2006). Understanding the role of individual innovativeness in the acceptance of IT-based innovations: Comparative analyses of models and measures. Decision Sciences, 37(3), 393–426.

    Article  Google Scholar 

  36. Wang, Q., Dacko, S., & Gad, M. (2008). Factors influencing consumers’ evaluation and adoption intention of really-new products or services: Prior knowledge, innovativeness and timing of product evaluation. Advances in Consumer Research, 35, 416–422.

    Google Scholar 

  37. Shoham, S., & Gonen, A. (2008). Intentions of hospital nurses to work with computers: Based on the theory of planned behavior. Computers Informatics Nursing, 26(2), 106–116.

    Article  Google Scholar 

  38. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.

    Article  Google Scholar 

  39. Engel, J. E., & Blackwell, R. D. (1982). Consumer behavior (4th ed.). New York, NY: The Dryden Press.

    Google Scholar 

  40. Park, E., & del Pobil, A. P. (2013). Technology acceptance model for the use of tablet PCs (pp. 1–12). Wireless Personal Communications.

  41. Ong, C. S., Lai, J. Y., & Wang, Y. S. (2004). Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Information & Management, 41(6), 795–804.

    Article  Google Scholar 

  42. Yu, P., Li, H., & Gagnon, M. P. (2009). Health IT acceptance factors in long-term care facilities: A cross-sectional survey. International Journal of Medical Informatics, 78(4), 219–229.

    Article  Google Scholar 

  43. Mahanti, R. (2009). The application of quality function deployment to user interface design. The Quality Management Journal, 16(1), 29–41.

    Google Scholar 

  44. Liang, T. P. (1987). User interface design for decision support systems: A self-adaptive approach. Information & Management, 12(4), 181–193.

    Article  Google Scholar 

  45. Sung, J. Y., & Park, J. H. (2011). A study on influence of smartphone user interface upon brand loyalty: With a focus on coordinating role of perceived skills on the device. Korean Society of Basic Design & Art, 12(1), 313–321.

    MathSciNet  Google Scholar 

  46. Hong, J. C., Hwang, M. Y., Hsu, H. F., Wong, W. T., & Chen, M. Y. (2011). Applying the technology acceptance model in a study of the factors affecting usage of the Taiwan digital archives system. Computers & Education, 57(3), 2086–2094.

    Article  Google Scholar 

  47. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace1. Journal of Applied Social Psychology, 22(14), 1111–1132.

    Article  Google Scholar 

  48. ur Rehman, U., Rizwan, M., ud din Ahmed, A., Ali, N., & Khan, M. H. (2013). E-TAM model: A comprehensive approach to understand the adoption of electronic shopping. Journal of Basic and Applied Scientific Research, 3(11), 178–188.

  49. Wu, J., & Liu, D. (2007). The effects of trust and enjoyment on intention to play online games. Journal of Electronic Commerce Research, 8(2), 128–140.

    Google Scholar 

  50. Lee, S. J., Kim, Y. W., Seoh, H. S., & Lee, B. G. (2010). Acceptance and rejection of smartphones and application genre repertoire. In Korean society for internet information conference, Seoul, Korea.

  51. Conboy, L., Patel, S., Kaptchuk, T. J., Gottlieb, B., Eisenberg, D., & Acevedo-Garcia, D. (2005). Sociodemographic determinants of the utilization of specific types of complementary and alternative medicine: an analysis based on a nationally representative survey sample. Journal of Alternative & Complementary Medicine: Research on Paradigm, Practice, and Policy, 11(6), 977–994.

    Article  Google Scholar 

  52. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25, 351–370.

    Article  Google Scholar 

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Acknowledgments

This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2010-332-B00074).

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Authors and Affiliations

  1. Catholic Institute for Healthcare Management, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, 137-701, Seoul, Korea

    Bo Ram Wang, Ji-Yun Park & In Young Choi

  2. Industry-Academic Cooperation Foundation, The Catholic University of Korea, 43 Jibong-ro, Wonmi-gu, Bucheon-si, Gyeonggi-do, 420-743, Korea

    Ji-Yun Park

  3. School of Computer Information Engineering, Sangji University, Usan-dong, Wonju-si, Gangwon-do, 220-702, Korea

    Kyungyong Chung

  4. Department of Medical Informatics, The Catholic University of Korea College of Medicine, 222 Banpo-daero, Seocho-gu, 137-701, Seoul, Korea

    In Young Choi

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  1. Bo Ram Wang
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Correspondence to In Young Choi.

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Wang, B.R., Park, JY., Chung, K. et al. Influential Factors of Smart Health Users according to Usage Experience and Intention to Use. Wireless Pers Commun 79, 2671–2683 (2014). https://doi.org/10.1007/s11277-014-1769-0

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