J Korean Med Sci.  2017 Nov;32(11):1757-1763. 10.3346/jkms.2017.32.11.1757.

Analysis of the Factors Affecting Consumer Acceptance of Accredited Online Health Information

Affiliations
  • 1Department of Health Policy and Management, Kangwon National University College of Medicine, Chuncheon, Korea.
  • 2Department of Health Management, Sahmyook University, Seoul, Korea.
  • 3Korea Institute of Drug Safety and Risk Management, Anyang, Korea. bgkim@drugsafe.or.kr

Abstract

With the increasing use of the internet and the spread of smartphones, health information seekers obtain considerable information through the internet. As the amount of online health information increases, the need for quality management of health information has been emphasized. The purpose of this study was to investigate the factors affecting the intention of using accredited online health information by applying the extended technology acceptance model (Extended-TAM). An online survey was conducted from September 15, 2016 to October 3, 2016, on 500 men and women aged 19-69 years. The results showed that the greatest factor influencing the acceptance of the accredited health information was perceived usefulness, and the expectation for the quality of the accreditation system was the most important mediator variable. In order to establish the health information accreditation system as a means to provide easy and useful information to the consumers, it is necessary to carry out quality management and promote the system through the continuous monitoring of the accreditation system.

Keyword

Health Information; Accreditation; Internet Use; Technology Acceptance Model; Quality

MeSH Terms

Accreditation
Female
Humans
Intention
Internet
Male
Smartphone

Figure

  • Fig. 1 Research model for predicting acceptance factors for accredited online health information based on the TAM.TAM = technology acceptance model.

  • Fig. 2 Results of research hypotheses based on the SEM.SEM = structural equation model, GFI = goodness-of-fit index, RMSEA = root-mean-square error of approximation, CFI = comparative fit index, TLI = Tucker-Lewis index.*P < 0.05; †P < 0.01; ‡P < 0.001.


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