Asia pacific journal of information systems

The Korea Society of Management Information Systems (한국경영정보학회)
권호 표지
DOI QR코드

DOI QR Code

Roles of Health-Oriented Personal Factors in Influencing Koreans' Perceptions about Telemedicine: Exploration of Regional Differences

  • Jaehee Cho (School of Media & Communication at Chung-Ang University) ;
  • Ghee-Young Noh (School of Media Communication and Director of Healthcare Media Research Institute at Hallym University)
  • Received : 2017.02.23
  • Accepted : 2017.08.09
  • Published : 2017.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

This study aimed to investigate the roles of three health-oriented personal factors-health technology innovativeness (HTI), health consciousness (HC), and health information orientation (HIO)-in determining Koreans' perceptions about telemedicine. Based on an extended version of the technology acceptance model (TAM), two perceptual components-perceived usefulness (PU) and perceived ease of use (PEOU)-of telemedicine were considered for this investigation. Data from 699 usable surveys were analyzed using path analysis. The results from the path analysis indicated that while HTI and HC had no or limited effects on the PU and PEOU of telemedicine, the effects of HIO on those two perceptual components of telemedicine were statistically significant. Moreover, the results from the path analysis showed that there were significant regional differences in the effects of HTI and HC on the PU and PEOU of telemedicine. In general, these effects were greater among the metropolitan residents than they were among the rural residents.

Keywords

Acknowledgement

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2015S1A3A2046760).

References

  1. Agarwal, R., and Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9, 204-215. https://doi.org/10.1287/isre.9.2.204
  2. Basu, A., and Dutta-Bergman, M.J. (2008). The relationship between health information seeking and community participation: The roles of health information orientation and efficacy. Health Communication, 23, 70-79. https://doi.org/10.1080/10410230701807121
  3. Bidmon, S., and Terlutter, R. (2015). Gender differences in searching for health information on the Internet and the virtual patient-physician relationship in Germany: Exploratory results on how men and women differ and why. Journal of Medical Internet Research, 22, e156.
  4. Carlson, J. R., and Zmud, R. W. (1999). Channel expansion theory and the experiential nature of media richness perceptions. Academy of Management Journal, 42, 153-170. https://doi.org/10.2307/257090
  5. Cho, H. (2013). Equity in health care: Current situation in South Korea. Journal of Korean Medical Association, 56, 184-194. https://doi.org/10.5124/jkma.2013.56.3.184
  6. Chen, G. M. (2011). Tweet this: A uses and gratifications perspective on how active Twitter use gratifies a need to connect with others. Computers in Human Behavior, 27, 755-762. https://doi.org/10.1016/j.chb.2010.10.023
  7. Cho, S. J., Kwon, I. H., and Jeong, J. (2015). Application of telemedicine system to prehospital medical control. Healthcare Informatics Research, 21, 196-200. https://doi.org/10.4258/hir.2015.21.3.196
  8. Cho, J., Lee, H. E., Kim, S. J., and Park, D. (2015). Effects of body image on college students' attitudes toward diet/fitness apps on smartphones. Cyberpsychology, Behavior, and Social Networking, 18, 41-45. https://doi.org/10.1089/cyber.2014.0383
  9. Cho, J., Park, D., and Lee, H. E. (2014). Cognitive factors of using health apps: Systematic analysis of relationships among health consciousness, health information orientation, eHealth literacy, and health app use efficacy. Journal of Medical Internet Research, 16(5), e125.
  10. Choi, J., Park, D., and Noh, G. (2015). The effect of attributes of exercise/fitness apps on app usage: a convergency perspective based on the self-determination theory. Journal of Digital Convergence, 13(6), 327.
  11. Dabholkar, P. A., and Bagozzi, R. P. (2002). An attitudinal model of technology-based self-service: Moderating effects of consumer traits and situational factors. Journal of the Academy of Marketing Science, 30, 184-201. https://doi.org/10.1177/0092070302303001
  12. Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982-1003. https://doi.org/10.1287/mnsc.35.8.982
  13. Devaraj, S., Easley, R. F., and Crant, J. M. (2008). How does personality matter? Relating the five-factor model to technology acceptance and use. Information Systems Research, 19, 93-105. https://doi.org/10.1287/isre.1070.0153
  14. Dutta-Bergman, M. J. (2004). Primary sources of health information: Comparisons in the domain of health attitudes, health cognitions, and health behaviors. Health Communication, 16, 273-288. https://doi.org/10.1207/S15327027HC1603_1
  15. Gagnona, M., Godinb, G., Gagneb, C., Fortina, J., Lamothec, L., Reinharza, D., and Cloutierd, A. (2003). An adaptation of the theory of interpersonal behavior to the study of telemedicine adoption by physicians. International Journal of Medical Informatics, 71, 103-115. https://doi.org/10.1016/S1386-5056(03)00094-7
  16. Egea, J. M. O., and Gonzalez, M. V. R. (2011). Explaining physicians' acceptance of EHCR systems: An extension of TAM with trust and risk factors. Computers in Human Behavior, 27, 319-332. https://doi.org/10.1016/j.chb.2010.08.010
  17. Gallup Korea Daily Opinion, "Research on smartphone use from 2012 to 2014". Gallup Korea.
  18. Ghvanidze, S., Velikova, N., Dodd, T., and Oldewage-Theron, W. (2017). A discrete choice experiment of the impact of consumers' environmental values, ethical concerns, and health consciousness on food choices: A cross-cultural analysis. British Food Journal, 119, 863-881. https://doi.org/10.1108/BFJ-07-2016-0342
  19. Hirunyawipada, T., and Paswan, A. K. (2006). Consumer innovativeness and perceived risk: implications for high technology product adoption. Journal of Consumer Marketing, 23, 182-198. https://doi.org/10.1108/07363760610674310
  20. Hovick, S., Liang, M. C., and Kahlor, L. (2014). Predicting cancer risk knowledge and information seeking: the role of social and cognitive factors. Health Communication, 29, 656-668. https://doi.org/10.1080/10410236.2012.763204
  21. Im, I., Hong, S., and Kang, M. S. (2011). An international comparison of technology adoption: Testing the UTAUT model. Information and Management, 48, 1-9.
  22. Jackson, J. D., Yi, M. Y., and Park, J. S. (2013). An empirical test of three mediation models for the relationship between personal innovativeness and user acceptance of technology. Information & Management, 50, 154-161.
  23. Joo, J., and Sang, Y. (2013). Exploring Koreans' smartphone usage: An integrated model of the technology acceptance model and uses and gratifications theory. Computers in Human Behavior, 29, 2512-2518. https://doi.org/10.1016/j.chb.2013.06.002
  24. Kim, H., Ahn, M., Choi, Y., Choi, E., Kim, D., Shin, S., Park, K., and Han, J. (2015). Fifteen-year experience with telemedicine services in Gangwon Province in Korea. Healthcare Informatics Research, 21, 283-291. https://doi.org/10.4258/hir.2015.21.4.283
  25. Kim, J. S., Oh, S. H., Choi, J. W., and Kim, S. Y. (2015). Current status of state telemedicine policy in the United States and policy implications for Korea. Journal of Korean Medical Association, 58, 923-932. https://doi.org/10.5124/jkma.2015.58.10.923
  26. Kim, Y., Sohn, D., and Choi, S. (2011). Cultural difference in motivations for using social network sites: A comparative study of American and Korean college students. Computers in Human Behavior, 27, 365-372. https://doi.org/10.1016/j.chb.2010.08.015
  27. King, W. R., and He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43, 740-755. https://doi.org/10.1016/j.im.2006.05.003
  28. Korea Internet and Security Agency. Survey on the Internet usage (on-line). Available at http://isis.kisa.or.kr/board/fileDown.jsp?pageId=040000andbbsId=7anditemId=792andathSeq=1.
  29. Kuo, K. M., Liu, C. F., and Ma, C. C. (2013). An investigation of the effect of nurses' technology readiness on the acceptance of mobile electronic medical record systems. BMC Medical Informatics and Decision Making, 13(88), 1-14. https://doi.org/10.1186/1472-6947-13-1
  30. Lee, K., Conklin, M., Cranage, D. A., and Lee, S. (2014). The role of perceived corporate social responsibility on providing healthful foods and nutrition information with health-consciousness as a moderator. International Journal of Hospitality Management, 37, 29-37. https://doi.org/10.1016/j.ijhm.2013.10.005
  31. Lee, K., Lee, H., Yoon, J., Nam, C., Yoon, J., Kim, S., and Lee, H. (2015). Evaluation of technical safety for telemedicine system. Research Institute for Healthcare Policy.
  32. Lee, H.-Y., Lee, J.-S., and Kim, J. (2015). Prerequisites for effective implementation of telemedicine: Focusing on current situations in Korea. Health Informatics Research, 21, 251-254. https://doi.org/10.4258/hir.2015.21.4.251
  33. Legris, P., Ingham, J., and Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information and Management, 40, 191-204. https://doi.org/10.1016/S0378-7206(01)00143-4
  34. Lu, J. (2014). Are personal innovativeness and social influence critical to continue with mobile commerce? Internet Research, 24, 134-159. https://doi.org/10.1108/IntR-05-2012-0100
  35. Lu, J., Yao, J. E. and Yu, C. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14, 245-268. https://doi.org/10.1016/j.jsis.2005.07.003
  36. Oh, J., Park, Y., Jo, E., and Kim, S. (2015). Current status and progress of telemedicine in Korea and other countries. Healthcare Informatics Research, 21, 239-243. https://doi.org/10.4258/hir.2015.21.4.239
  37. Park, H., Chon, Y., Lee, J., Choi, I., and Yoon, K. (2011). Service design attributes affecting diabetic patient preferences of telemedicine in South Korea. Telemedicine and e-Health, 17, 442-451. https://doi.org/10.1089/tmj.2010.0201
  38. Putzer, G. J. (2012). Are physicians likely to adopt emerging mobile technologies? Attitudes and innovation factors affecting smartphone use in the Southeastern United States. Perspectives in Health information Management, 9, e1-22.
  39. Rakhi, T., and Mala S. (2014). Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Research, 24, 369-392. https://doi.org/10.1108/IntR-12-2012-0244
  40. Rauschnabei, P. A., Brem, A., and Bjoern, S. I. (2015). Who will buy smart glasses? Empirical results of two pre-market-entry studies on the role of personality in individual awareness and intended adoption of Google Glass wearables. Computers in Human Behavior, 49, 635-647. https://doi.org/10.1016/j.chb.2015.03.003
  41. Rho, M., and Bae, J. K. (2013). The physicians' expected benefits: an empirical study of the telemedicine system acceptance. Journal of Convergence Information Technology, 8, 200-207.
  42. Rho, M. J., Yoon, K. H., Kim, H., and Choi, I. Y. (2015). Users' perception on telemedicine service: a comparative study of public healthcare and private healthcare. Multimedia Tools Applications, 74, 2483-2497. https://doi.org/10.1007/s11042-014-1966-6
  43. Rogers, E. M. (2003). Diffusion of innovations. (5th ed.). New York: The Free Press.
  44. Rubin, A. M. (2002). The uses and gratifications perspective of media effects. In J. Bryant & D. Zillman (Eds.), Media effects: Advances in theory and research (2nd ed.) (pp. 525-548). Mahwah, NJ: Erlbaum.
  45. Schepers, J., and Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44, 90-103. https://doi.org/10.1016/j.im.2006.10.007
  46. Svendsen, G. B., Johnsen, J. K., Almas-Sorensen, L., and Vitterso, J. (2013). Personality and technology acceptance: The influence of personality factors on the core constructs of the technology acceptance model. Behavior and Information Technology, 32, 323-334. https://doi.org/10.1080/0144929X.2011.553740
  47. Venkatesh, V., and Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46, 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
  48. Weaver, K. E., Geiger, A. M., Lu, L., and Case L. D. (2013). Rural-urban disparities in health status among US cancer survivors. Cancer, 119, 1050-1057. https://doi.org/10.1002/cncr.27840
  49. Witt, E. A., Massman, A. J., and Jackson, L. A. (2011). Trends in youth's videogame playing, overall computer use, and communication technology use: The impact of self-esteem and the Big Five personality factors. Computers in Human Behavior, 27, 763-769. https://doi.org/10.1016/j.chb.2010.10.025
  50. Xiao, N., Sharman, R., Rao, H. R., and Upadhyaya, S. (2014). Factors influencing online health information search: An empirical analysis of a national cancer-related survey. Decision Support Systems, 57, 417-427. https://doi.org/10.1016/j.dss.2012.10.047
  51. Yarbrough, A. K., and Smith, T. B. (2007). Technology acceptance among physicians: A new take on TAM. Medical Care Research and Review, 64, 650-672. https://doi.org/10.1177/1077558707305942
  52. Yun, E. K., and Park, H-A. (2010). Consumers' disease information-seeking behavior on the Internet in Korea. Journal of Clinical Nursing, 19, 2860-2868. https://doi.org/10.1111/j.1365-2702.2009.03187.x
  53. Zhou, T., Lu, Y., and Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26, 760-767. https://doi.org/10.1016/j.chb.2010.01.013