Xinyi Lu
ABSTRACT
By means of obtaining health-related information published by physicians in online health communities (OHCs), patients are able to diagnose some simple diseases by themselves and then save queueing time, which can help optimize the allocation of medial resources and alleviate hospitals' congestion to a certain extent, However, some patients may be hesitant to adopt this information because of its shortcomings. This study established a research model based on the unified theory of acceptance and use of technology to examine patients' acceptance of information published by physicians in OHCs. An online survey involving 453 Chinese participants was conducted to collect data, and 378 (83.4%) were valid. Structural equation modelling and partial least squares were adopted to analyze data and test hypotheses Results reveal that performance expectancy, social influence and attitude toward using technology positively influence their behavioral intention and ultimately influence usage behavior of adopting information in OHCs. Our findings suggest that OHCs should inspire the intention of users to use information in OHCs, enhance the management of information, strengthen OHCs' reputation to increase social influence, and improve the service level of OHCs.
- Luo, P., Chen, K., Wu, C., and Li, Y. 2018. Exploring the social influence of multichannel access in an online health community. J. Assoc. Inf. Sci. Tech. 69, 1 (Sep. 2018), 98--109. DOI=http://doi.org/10.1002/asi.23928.Google Scholar
Digital Library
- Yan, Z., Wang, T., Chen, Y., and Zhang, H. 2016. Knowledge sharing in online health communities: A social exchange theory perspective. Inform. Manage. 53, 5 (Jul. 2016), 643--653. DOI=http://doi.org/10.1016/j.im.2016.02.001.Google ScholarDigital Library
- Berger, M., Wagner, T. H., and Baker, L. C. 2005. Internet use and stigmatized illness. Soc. Sci. Med. 61, 8 (Oct. 2005), 1821--1827. DOI=http://doi.org/10.1016/j.socscimed.2005.03.025.Google ScholarCross Ref
- Yan, L., and Tan, Y. 2014. Feeling blue? Go online: an empirical study of social support among patients. Inform. Syst. Res. 25, 4 (Dec. 2014), 690--709. DOI=http://doi.org/10.1287/isre.2014.0538.Google ScholarDigital Library
- Dobransky, K., and Hargittai, E. 2012. Inquiring minds acquiring wellness: Uses of online and offline sources for health information. Health. Commun. 27, 4 (Sep. 2012), 331--343. DOI=http://doi.org/10.1080/10410236.2011.585451.Google ScholarCross Ref
- Neter, E., and Brainin, E. 2012. eHealth literacy: Extending the digital divide to the realm of health information. J. Med. Internet. Res. 14, 1 (Jan. 2012), e19. DOI=http://doi.org/10.2196/jmir.1619.Google ScholarCross Ref
- Suri, V. R., Majid, S., Chang, Y. K., and Foo, S. 2016. Assessing the influence of health literacy on health information behaviors: A multi-domain skills-based approach. Patient. Educ. Couns. 99, 6 (Jun. 2016), 1038--1045. DOI=http://doi.org/10.1016/j.pec.2015.12.017.Google ScholarCross Ref
- Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. 2003. User acceptance of information technology: Toward a unified view. MIS. Quart. 27,3 (Sep. 2003), 425--478. DOI=http://doi.org/10.2307/30036540.Google ScholarCross Ref
- Chang, I. C., and Hsu, H. M. 2012. Predicting medical staff intention to use an online reporting system with modified unified theory of acceptance and use of technology. Telemed. J. E. Health. 18, 1 (Jan-Feb. 2012), 67--73. DOI=http://doi.org/10.1089/tmj.2011.0048.Google ScholarCross Ref
- Veer, A. J., Peeters, J. M., Brabers, A. E., Schellevis, F. G., Rademakers, J. J., and Francke, A. L. 2015. Determinants of the intention to use e-health by community dwelling older people. BMC. Health. Serv. Res. 15, 1 (Mar. 2015), 103. DOI=http://doi.org/10.1186/s12913-015-0765-8.Google ScholarCross Ref
- Lamberg, L. 2003. Online empathy for mood disorders. J. Am. Med. Assoc. 289, 23 (Jun. 2003), 3073--3077. DOI=http://doi.org/10.1001/jama.289.23.3073.Google ScholarCross Ref
- Jiang, S., and Street, R. L. 2017. Pathway linking Internet health information seeking to better health: A moderated mediation study. Health. Commun. 32, 8 (Aug. 2017),1024--1031. DOI=http://doi.org/10.1080/10410236.2016.1196514.Google ScholarCross Ref
- Wu, B. 2018. Patient continued use of online health care communities: Web mining of patient-doctor communication. J. Med. Internet. Res. 20, 4 (Apr. 2018), e126. DOI=http://doi.org/10.2196/jmir.9127.Google ScholarCross Ref
- Cacciatore, M. A., Anderson, A. A., Choi, D. H., Brossard, D., Scheufele, D. A., Liang, X., Ladwig, P., Xenos, M. A., and Dudo, A. D. 2012. Coverage of emerging technologies: A comparison between print and online media. New. Media. Society. 14, 6 (Aug. 2012), 1039--1059. DOI=http://doi.org/10.1177/1461444812439061.Google ScholarCross Ref
- Huang, L. C., Ko, H. H. C., and Kuo, F. Y. 2007. Influential factors on physicians' behaviour towards the medical web messaging board: a qualitative exploration. Int. J. Electron. Healthc. 3, 2 (Jan. 2007), 220--231. DOI=http://doi.org/10.1504/IJEH.2007.013102.Google ScholarCross Ref
- Kaba, B., and Touré, B. 2014. Understanding information and communication technology behavioral intention to use: Applying the UTAUT model to social networking site adoption by young people in a least developed country. J. Assoc. Inf. Sci. Tech. 65, 8 (Aug. 2014), 1662--1674. DOI=http://doi.org/10.1002/asi.23069.Google ScholarDigital Library
- Sun, J., and Lu, J. 2014. An empirical study on user acceptance of healthcare website. Int. J. Netw. Virt. Organ. 14, 1-2 (Jan. 2014), 57--73. DOI=http://doi.org/10.1504/IJNVO.2014.065093.Google ScholarDigital Library
- Hennemann, S., Witthöft, M., Bethge, M., Spanier, K., Beutel, M. E., and Zwerenz, R. 2018. Acceptance and barriers to access of occupational e-mental health: cross-sectional findings from a health-risk population of employees. Int. Arch. Occup. Environ. Health. 91, 3 (Apr. 2018), 305--316. DOI=http://doi.org/10.1007/s00420-017-1280-5.Google ScholarCross Ref
- Wong, W. S., Chen, P. P., Chow, Y. F., Wong, S., and Fielding, R. 2016. A study of the reliability and concurrent validity of the Chinese version of the pain medication attitude questionnaire (ChPMAQ) in a sample of Chinese patients with chronic pain. Pain. Medicine. 17, 6 (Feb. 2016), 1137--1144. DOI=http://doi.org/10.1093/pm/pnv086.Google ScholarCross Ref
- Audrain-Pontevia, A. F., and Menvielle, L. 2018. Do online health communities enhance patient--physician relationship? An assessment of the impact of social support and patient empowerment. Health. Serv. Manage. Res. 31, 3 (Aug. 2018), 154--162. DOI=http://doi.org/10.1177/0951484817748462.Google ScholarCross Ref
- Huh, J., Kwon, B. C., Kim, S. H., Lee, S., Choo, J., Kim, J., Choi, M. J., and Yi, J. S. 2016. Personas in online health communities. J. Biomed. Inform. 63 (Oct. 2016), 212--225. DOI=http://doi.org/10.1016/j.jbi.2016.08.019.Google ScholarDigital Library
- Laugesen, J., Hassanein, K., and Yuan, Y. 2015. The impact of Internet health information on patient compliance: A research model and an empirical study. J. Med. Internet. Res. 17, 6 (Jun. 2015), e143. DOI=http://doi.org/10.2196/jmir.4333.Google ScholarCross Ref
- Wiertz, C., and de Ruyter, K. 2016. Beyond the call of duty: Why customers contribute to firm-hosted commercial online communities. Organ. Stud. 28, 3 (Jun. 2016):347--376. DOI=http://doi.org/10.1177/0170840607076003.Google ScholarCross Ref
- Buysse, H. E., Coorevits, P., Van Maele, G., Hutse, A., Kaufman, J., Ruige, J., and De Moor, G. J. 2010. Introducing telemonitoring for diabetic patients: development of a telemonitoring 'Health Effect and Readiness' questionnaire. Int. J. Med. Inform. 79, 8 (Aug. 2010), 576--584. DOI=http://doi.org/10.1016/j.ijmedinf.2010.05.005.Google ScholarCross Ref
- Fornell, C., and Larcker, D. F. 1981. Evaluating structural equation models with unobservable variables and measurement error. J. Marketing. Res. 18, 1 (Aug. 1981), 39--50. DOI=http://doi.org/10.2307/3150980.Google ScholarCross Ref
- Sobral, M. P., Costa, M. E., Schmidt, L., and Martins, M. V. 2017. Corrigendum: COMPI fertility problem stress scales is a brief, valid and reliable tool for assessing stress in patients seeking treatment. Hum. Reprod. 32, 2 (Feb. 2017), 375--382. DOI=http://doi.org/10.1093/humrep/dew315.Google ScholarCross Ref
- Lu, X, Zhang, R., Wu, W., Shang, X., and Liu, M. 2018. Relationship between Internet health information and patient compliance based on Trust: Empirical study. J. Med. Internet. Res. 20, 8 (Jul. 2018), e253. DOI=http://doi.org/10.2196/jmir.9364.Google ScholarCross Ref
- Katona, Z., Zubcsek, P. P., and Sarvary, M. 2011. Network effects and personal influences: The diffusion of an online social network. J. Marketing. Res. 48, 3 (Jun. 2011), 425--443. DOI=http://doi.org/10.2307/23033849.Google ScholarCross Ref
- Lee, Y. J., Hosanagar, K., and Tan, Y. 2015. Do I follow my friends or the crowd? Information cascades in online movie ratings. Manage. Sci. 61, 9 (Apr. 2015), 2241--2258. DOI=http://doi.org/10.1287/mnsc.2014.2082.Google ScholarDigital Library
- Li, D., Luo, Z., Ding, Y., Sun, G. Z., Sun, G. Z., Dai, X., Du, J., Zhang, J., and Kong, S. 2017. User-level microblogging recommendation incorporating social influence. User-level microblogging recommendation incorporating social influence. 68, 3 (Aug. 2016), 553--568. DOI=http://doi.org/10.1002/asi.23681.Google ScholarCross Ref
- Luo, P., Chen, K., and Wu, C. 2016. Measuring social influence for firm-level financial performance. Electron. Commer. R. A. 20 (Sep. 2016), 15--29. DOI=http://doi.org/10.1016/j.elerap.2016.09.004.Google ScholarDigital Library
Index Terms
- Patients' Acceptance of Information Published by Physicians in Online Health Communities: An Empirical Study
Recommendations
Physicians' acceptance of telemedicine technology: an empirical test of competing theories
The purpose of this study is to compare two theories of technology adoption, the technology acceptance model and the theory of planned behaviour, to explain and predict physicians' acceptance and use of the telehomecare technology. We administered a two-...
Electronic health record acceptance by physicians
Previous studies found that the TAM only partly explained physicians' EHR adoption.Our study tested an integrated theoretical model based on the TAM and other theoretical frameworks.Perceived ease of use is the strongest predictor of physicians' ...
Physicians' acceptance of pharmacokinetics-based clinical decision support systems
This paper reports a finding of a field study on the physicians' acceptance of a pharmacokinetics-based clinical decision support system (CDSS). A research model was used to explain the acceptance of the technology of 140 participated physicians from ...
Comments