Skip to main content

The dark side of elderly acceptance of preventive mobile health services in China

Abstract

Mobile health services have become increasingly important for people, especially for the elderly. Despite the potential benefits, there are challenges and barriers for the elderly in adopting mobile health services. Drawing upon the dual factor model, we investigate the enablers and the inhibitors of the elderly mobile health service adoption behaviour. We also address two typical characteristics of elderly users—technology anxiety and dispositional resistance to change—to understand the antecedents of the enablers and the inhibitors. The hypothesized model is empirically tested using data collected from a field survey of 204 customers of a large elderly service providing company in China. The key findings include: (1) resistance to change influences perceived usefulness but does not influence perceived ease of use and adoption intention; (2) technology anxiety is negatively associated with perceived ease of use but positively associated with resistance to change; (3) dispositional resistance to change is negatively associated with perceived ease of use but positively associated with resistance to change. Implications for research and practice are discussed.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

Notes

  1. 1.

    http://www.stats.gov.cn/tjshujia/tjzl/t20101221_402692078.htm

References

  1. Akter, S., D’ Ambra, J., & Ray, P. (2010). Service quality of mHealth platforms: development and validation of a hierarchical model using PLS. Electronic Markets, 20(3–4), 1–19.

    Google Scholar 

  2. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: a review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423.

    Article  Google Scholar 

  3. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.

    Article  Google Scholar 

  4. Bhattacherjee, A., & Hikmet, N. (2007). Physicians’ resistance toward healthcare information technology: a theoretical model and empirical test. European Journal of Information Systems, 16(6), 725–737.

    Article  Google Scholar 

  5. Cenfetelli, R. T. (2004). Inhibitors and enablers as dual factor concepts in technology usage. Journal of the Association for Information Systems, 5(11), 472–492.

    Google Scholar 

  6. Cocosila, M., & Archer, N. (2010). Adoption of mobile ict for health promotion: an empirical investigation. Electronic Markets, 20(3–4), 241–250.

    Article  Google Scholar 

  7. 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 

  8. Durndell, A., & Haag, Z. (2002). Computer self efficacy, computer anxiety, attitudes towards the internet and reported experience with the internet, by gender, in an east european sample. Computers in Human Behavior, 18(5), 521–535.

    Article  Google Scholar 

  9. Dyck, J. L., Gee, N. R., & Smither, J. A. (1998). The changing construct of computer anxiety for younger and older adults. Computers in Human Behavior, 14(1), 61–77.

    Article  Google Scholar 

  10. Fornell, C., & Bookstein, F. L. (1982). Two structural equation models: lisrel and pls applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440–452.

    Article  Google Scholar 

  11. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  12. Free, C., Phillips, G., Felix, L., Galli, L., Patel, V., & Edwards, P. (2010). The effectiveness of M-health technologies for improving health and health services: a systematic review protocol. BMC Research Notes, 3(10), 250–256.

    Article  Google Scholar 

  13. Fukuoka, Y., Kamitani, E., Bonnet, K., & Lindgren, T. (2011). Real-time social support through a mobile virtual community to improve healthy behavior in overweight and sedentary adults: a focus group analysis. Journal of Medical Internet Research, 13(3).

  14. Ghose, A., & Han, S. (2011). An empirical analysis of user content generation and usage behavior on the mobile internet. Management Science, 57(9), 1671–1691.

    Article  Google Scholar 

  15. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Prentice-Hall: Upper Saddle River.

    Google Scholar 

  16. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19(2), 139–152.

    Article  Google Scholar 

  17. Harrington, K. V., McElroy, J. C., & Morrow, P. C. (1990). Computer anxiety and computer-based training: a laboratory experiment. Journal of Educational Computing Research, 6(3), 343–358.

    Article  Google Scholar 

  18. Heinssen, R. K. (1987). Assessing computer anxiety: development and validation of the computer anxiety rating scale. Computers in Human Behavior, 3(1), 49–59.

    Article  Google Scholar 

  19. Hill, R., Beynon-Davies, P., & Williams, M. D. (2008). Older people and internet engagement. Information Technology & People, 21(3), 244–266.

    Article  Google Scholar 

  20. Hsu, C. L., & Lin, J. C. C. (2008). Acceptance of blog usage: the roles of technology acceptance, social influence and knowledge sharing motivation. Information & Management, 45(1), 65–74.

    Article  Google Scholar 

  21. Istepanian, R. S. H., & Pattichis, C. S. (2006). M-health: emerging mobile health systems. New York: Springer.

    Google Scholar 

  22. Ivatury, G., Moore, J., & Bloch, A. (2009). A doctor in your pocket: health hotlines in developing countries. Innovations: Technology, Governance, Globalization, 4(1), 119–153.

    Article  Google Scholar 

  23. Jiang, J. J., Klein, G., & Carr, C. L. (2002). Measuring information system service quality: SERVQUAL from the other side. MIS Quarterly, 26(2), 145–166.

    Article  Google Scholar 

  24. Johnston, A. C., & Warkentin, M. (2010). Fear appeals and information security behaviors: an empirical study. Management Information Systems Quarterly, 34(3), 549–566.

    Google Scholar 

  25. Joshi, K. (1991). A model of users’ perspective on change: the case of information systems technology implementation. Mis Quarterly, 15(2), 229–242.

    Article  Google Scholar 

  26. Kahneman, D., & Miller, D. T. (1986). Norm theory: comparing reality to its alternatives. Psychological Review, 93(2), 136–153.

    Article  Google Scholar 

  27. Laguna, K., & Babcock, R. L. (1997). Computer anxiety in young and older adults: implications for human–computer interactions in older populations. Computers in Human Behavior, 13(3), 317–326.

    Article  Google Scholar 

  28. Lewin, K. (1947). Frontiers in group dynamics: I. concept, method, and reality in social sciences, social equilibria, and social change. Human Relations, 1(2), 5–41.

    Article  Google Scholar 

  29. Liang, H., Saraf, N., Hu, Q., & Xue, Y. (2007). Assimilation of enterprise systems: the effect of institutional pressures and the mediating role of top management. MIS Quarterly, 31(1), 59–87.

    Google Scholar 

  30. Mechael, P. N. (2009). The case for mhealth in developing countries. Innovations: Technology, Governance, Globalization, 4(1), 103–118.

    Article  Google Scholar 

  31. Nov, O., & Schecter, W. (2012). Dispositional resistance to change and hospital physicians’ use of electronic medical records: a multidimensional perspective. Journal of the American Society for Information Science and Technology, 63(4), 648–656.

    Article  Google Scholar 

  32. Nov, O., & Ye, C. (2008). Users’ personality and perceived ease of use of digital libraries: the case for resistance to change. Journal of the American Society for Information Science and Technology, 59(5), 845–851.

    Article  Google Scholar 

  33. Or, C. K. L., & Karsh, B.-T. (2009). A systematic review of patient acceptance of consumer health information technology. Journal of American Medical Informatics Association, 16(5), 550–560.

    Article  Google Scholar 

  34. Oreg, S. (2003). Resistance to change: developing an individual differences measure. Journal of Applied Psychology, 88(4), 680–693.

    Article  Google Scholar 

  35. Oreg, S. (2006). Personality, context, and resistance to organizational change. European Journal of Work and Organizational Psychology, 15(1), 73–101.

    Article  Google Scholar 

  36. Oreg, S., Bayazit, M., Vakola, M., Arciniega, L., Armenakis, A., Barkauskiene, R., et al. (2008). Dispositional resistance to change: measurement equivalence and the link to personal values across 17 nations. Journal of Applied Psychology, 93(4), 935–944.

    Article  Google Scholar 

  37. Phillips, B. N., Martin, R. P., & Meyers, J. (Eds.). (1972). Interventions in relation to anxiety in school (Vol. 2). New York: Academic.

    Google Scholar 

  38. Selwyn, N. (2004). The information aged: a qualitative study of older adults’ use of information and communications technology. Journal of Aging Studies, 18, 369–384.

    Article  Google Scholar 

  39. Spil, T. A. M., Schuring, R. W., & Michel-Verkerke, M. B. (2004). Electronic prescription system: do the professionals use it? International Journal of Healthcare Technology and Management, 6(1), 32–55.

    Article  Google Scholar 

  40. Tatnall, A., & Lepa, J. (2003). The internet, e-commerce and older people: an actor-network approach to researching reasons for adoption and use. Logistics Information Management, 16(1), 56–63.

    Article  Google Scholar 

  41. Thatcher, J. B., & Perrewe, P. L. (2002). An empirical examination of individual traits as antecedents to computer anxiety and computer self-efficacy. Mis Quarterly, 26(4), 381–396.

    Article  Google Scholar 

  42. Tuckman, J., & Lorge, I. (1953). Attitudes toward old people. Journal of Social Psychology, 37, 249–260.

    Google Scholar 

  43. van de Vijver, F. J. R. (2006). Conducting cross-cultural research. Paper presented at the 2006 Regional Conference of Academy of Management Research Method Division, Hong Kong SAR.

  44. van de Vijver, F. J. R., & Leung, K. (1997). Methods and data-analysis for cross-cultural research. Thousand Oaks: Sage.

    Google Scholar 

  45. Venkatesh, V. (2000). Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.

    Article  Google Scholar 

  46. Venkatesh, V., & Morris, M. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425–478.

    Google Scholar 

  47. Wilson, E. V., & Lankton, N. K. (2004). Modeling patients’ acceptance of provider-delivered e-health. Journal of the American Medical Informatics Association, 11(4), 241–248.

    Article  Google Scholar 

  48. Woo, J., Kwok, T., Sze, F., & Yuan, H. (2002). Ageing in China: health and social consequences and responses. International Journal of Epidemiology, 31(4), 772–775.

    Article  Google Scholar 

  49. 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 

Download references

Acknowledgements

This study was partially supported by the Hong Kong Scholars Program and the National Science Foundation of China Grant (71201118, 71101037, 71201058), Self-dependent Research Project for Social and Humanity Science of Wuhan University (274130), and Wuhan University Academic Development Plan for Scholars after 1970s (“Research on Network User Behavior”).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Yongqiang Sun.

Additional information

Responsible editor: Doug Vogel

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Guo, X., Sun, Y., Wang, N. et al. The dark side of elderly acceptance of preventive mobile health services in China. Electron Markets 23, 49–61 (2013). https://doi.org/10.1007/s12525-012-0112-4

Download citation

Keyword

  • Preventive mobile health services
  • Technology anxiety
  • Resistance to change
  • Technology acceptance model
  • Dual factor model
  • JEL classification M31
  • Marketing