The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Integrating Health Belief Model and Technology Acceptance Model: An Investigation of Health-Related Internet Use
Open accessAbstract
BACKGROUND: Today, people use the Internet to satisfy health-related information and communication needs. In Malaysia, Internet use for health management has become increasingly significant due to the increase in the incidence of chronic diseases, in particular among urban women and their desire to stay healthy. Past studies adopted the Technology Acceptance Model (TAM) and Health Belief Model (HBM) independently to explain Internet use for health-related purposes. Although both the TAM and HBM have their own merits, independently they lack the ability to explain the cognition and the related mechanism in which individuals use the Internet for health purposes. OBJECTIVE: This study aimed to examine the influence of perceived health risk and health consciousness on health-related Internet use based on the HBM. Drawing on the TAM, it also tested the mediating effects of perceived usefulness of the Internet for health information and attitude toward Internet use for health purposes for the relationship between health-related factors, namely perceived health risk and health consciousness on health-related Internet use. METHODS: Data obtained for the current study were collected using purposive sampling; the sample consisted of women in Malaysia who had Internet access. The partial least squares structural equation modeling method was used to test the research hypotheses developed. RESULTS: Perceived health risk (β=.135, t1999=2.676) and health consciousness (β=.447, t1999=9.168) had a positive influence on health-related Internet use. Moreover, perceived usefulness of the Internet and attitude toward Internet use for health-related purposes partially mediated the influence of health consciousness on health-related Internet use (β=.025, t1999=3.234), whereas the effect of perceived health risk on health-related Internet use was fully mediated by perceived usefulness of the Internet and attitude (β=.029, t1999=3.609). These results suggest the central role of perceived usefulness of the Internet and attitude toward Internet use for health purposes for women who were health conscious and who perceived their health to be at risk. CONCLUSIONS: The integrated model proposed and tested in this study shows that the HBM, when combined with the TAM, is able to predict Internet use for health purposes. For women who subjectively evaluate their health as vulnerable to diseases and are concerned about their health, cognition beliefs in and positive affective feelings about the Internet come into play in determining the use of health-related Internet use. Furthermore, this study shows that engaging in health-related Internet use is a proactive behavior rather than a reactive behavior, suggesting that TAM dimensions have a significant mediating role in Internet health management.
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Cites
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
Baron, R. M., & Kenny, D. A. (1986) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.51.6.1173 · Cited by 91,532
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
(1980) · Medical Entomology and Zoology · Cited by 25,810
Pls-Sem: indeed a silver bullet
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011) · Journal of Marketing Theory and Practice · doi:10.2753/mtp1069-6679190202 · Cited by 21,631
Confirmatory factor analysis for applied research
(2007) · Choice Reviews Online · doi:10.5860/choice.44-2769 · Cited by 15,614
Asymptotic Confidence Intervals for Indirect Effects in Structural Equation Models
Sobel, M. E. (1982) · Sociological Methodology · doi:10.2307/270723 · Cited by 12,810
Cross validatory choice and assessment of statistical predictions
Stone, M. (1974) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1974.tb00994.x · Cited by 10,881
Beyond Baron and Kenny: Statistical Mediation Analysis in the New Millennium
Hayes, A. F. (2009) · Communication Monographs · doi:10.1080/03637750903310360 · Cited by 9,757
Confidence Limits for the Indirect Effect: Distribution of the Product and Resampling Methods
MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr3901_4 · Cited by 7,806
Cited by
Zhang, Y., Liu, C., Luo, S., Xie, Y., Liu, F., Li, X., & Zhou, Z. (2019) · Journal of Medical Internet Research · doi:10.2196/15023 · Cited by 207
Ye, T., Xue, J., He, M., Gu, J., Lin, H., Xu, B., & Cheng, Y. (2019) · Journal of Medical Internet Research · doi:10.2196/14316 · Cited by 141
Renal transplant patient acceptance of a self-management support system
Wang, W., van Lint, C. L., Brinkman, W.-P., Rövekamp, T. J. M., van Dijk, S., van der Boog, P. J. M., & Neerincx, M. A. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0456-y · Cited by 30
Metadata
- Authors
- Ahadzadeh, A. S., Pahlevan Sharif, S., Ong, F. S., & Khong, K. W.
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2015-02-19
- Volume
- 17
- Issue
- 2
- Pages
- e45
- ISSN
- 1438-8871 (ISSN-L)
- OpenAlex
- W2156671921
- Cited by
- 364
- References
- 55
- FWCI
- 28.63
- Type
- journal article
- Language
- en
- Open access
- gold
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