Health Care Professionals’ Social Media Behavior and the Underlying Factors of Social Media Adoption and Use: Quantitative Study
Open accessPublished version of a preprint
Abstract
BACKGROUND: In the last decade, social media has emerged as a newer platform for knowledge dissemination, information exchange, and interpersonal communication for health care professionals (HCPs). However, the underlying behaviors of HCPs and the ethical use of social media for productivity enhancement and a sustainable health care system remain ambiguous. OBJECTIVE: This study seeks to understand the factors that relate to the frequency use of social media in the health care discipline. It also aims to explore the underlying online behaviors of HCPs, which include the exchange of medical information with peers, interpersonal communication, and productivity enhancement in their daily practice. METHODS: This study adopted the quantitative method in collecting and analyzing data. A survey instrument based on the behavioral and technology acceptance theories was developed for this purpose. The survey was distributed via social media platforms to 973 participants that included physicians, pharmacists, and allied HCPs working in the United Arab Emirates. The responses from 203 completed questionnaires (response rate 20.3%) were analyzed. RESULTS: Of 203 respondents, 133 HCPs used WhatsApp (65.5%); therefore, WhatsApp had the highest number of users compared to Facebook and YouTube, with 101 users out of 203 (49.7%). Overall, 109 of 203 (53.6%) HCPs used social media platforms for the exchange of peer medical information and 108 of 203 (53.2%) used social media several times during the day to improve their interpersonal communication with colleagues. However, only 71 of 203 (34.9%) utilized social media to enhance their productivity in general. The structural model equation showed that behavioral intention (beta=.47; P<.001), habit (beta=.26; P=.001), attitude (beta=.20; P=.002), and perceived usefulness (beta=.12; P=.09) were positively and significantly related to frequency of use. The model explained a rate of 45% variance in the frequency of use and a rate of 17% variance in the social media intention of use. CONCLUSIONS: The research highlights the significant factors that relate to the adoption of social media platforms in health care practice. Based on the findings of this study, the use of online platforms facilitates the exchange of medical information among peers and enhances the share of experiences that support HCP's learning and development. Moreover, social media platforms foster a higher level of communication among practitioners and might improve daily productivity. Future researchers might explore other variables such as training and external factors. For instance, they may draw on areas related to guidelines and policies. From this standpoint, the health care discipline can benefit from highly interactive platforms and adopt them for development, collaboration, and better health outcomes.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Most included studies (n=77) had clear ethical statements within the paper either stating ethical board approval or exemption, and 11 studies did not explicitly report an ethical statement.
“Most studies included (n=77) had clear ethical statements within the paper either stating ethical board approval or exemption, and 11 studies did not explicitly report an ethical statement [37-39,53,54,72,74,90,105,108,117].”
From Dangers and Benefits of Social Media on E-Professionalism of Health Care Professionals: Scoping Review (2021) · cites this paper as [108]
Social media have enabled the creation of communities for support.
“Besides providing the opportunity for connecting with others and sharing experiences [38,39,46,53,69,72,78,106-108], SM have enabled the creation of communities for support.”
From Dangers and Benefits of Social Media on E-Professionalism of Health Care Professionals: Scoping Review (2021) · cites this paper as [108]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The theory of planned behavior
Ajzen, I. (1991) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(91)90020-t · Cited by 90,587 · see citing works
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 · see citing works
Structural equation modeling in practice: A review and recommended two-step approach
Anderson, J. C., & Gerbing, D. W. (1988) · Psychological Bulletin · doi:10.1037/0033-2909.103.3.411 · Cited by 41,941 · see citing works
On the Evaluation of Structural Equation Models
Bagozzi, R. P., & Yi, Y. (1988) · Journal of the Academy of Marketing Science · doi:10.1007/bf02723327 · Cited by 26,163 · see citing works
A Paradigm for Developing Better Measures of Marketing Constructs
Churchill, G. A. (1979) · Journal of Marketing Research · doi:10.1177/002224377901600110 · Cited by 16,549 · see citing works
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012) · MIS Quarterly · doi:10.2307/41410412 · Cited by 15,756 · see citing works
Sources of method bias in social science research and recommendations on how to control it
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012) · Annual Review of Psychology · doi:10.1146/annurev-psych-120710-100452 · Cited by 15,744 · see citing works
Structural equation models with unobservable variables and measurement error: Algebra and statistics
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800313 · Cited by 13,287 · see citing works
MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011) · MIS Quarterly · doi:10.2307/23044045 · Cited by 3,165 · see citing works
The spread of behavior in an online social network experiment
Centola, D. (2010) · Science · doi:10.1126/science.1185231 · Cited by 2,692 · see citing works
Moorhead, S. A., Hazlett, D. E., Harrison, L., Carroll, J. K., Irwin, A., & Hoving, C. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.1933 · Cited by 2,517 · see citing works
Readings in attitude theory and measurement
Jahoda, M., & Fishbein, M. (1968) · British Journal of Sociology · doi:10.2307/588703 · Cited by 1,287 · see citing works
Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006) · Information & Management · doi:10.1016/j.im.2005.08.006 · Cited by 1,139 · see citing works
Francke, A. L., Smit, M. C., de Veer, A. J. E., & Mistiaen, P. (2008) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-8-38 · Cited by 1,131 · see citing works
Social media and health care professionals: benefits, risks, and best practices.
(2014) · PubMed Central · Cited by 1,011 · see citing works
The role of theory in HIV prevention
Fishbein, M. (2000) · AIDS Care · doi:10.1080/09540120050042918 · Cited by 965 · see citing works
Social Media: A Review and Tutorial of Applications in Medicine and Health Care
Grajales III, F. J., Sheps, S., Ho, K., Novak-Lauscher, H., & Eysenbach, G. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.2912 · Cited by 737 · see citing works
McGowan, B. S., Wasko, M. M., Vartabedian, B. S., Miller, R. S., Freiherr, D. D., & Abdolrasulnia, M. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.2138 · Cited by 381 · see citing works
Discovering statistics using IBM SPSS statistics, 5th edition
Field, A. P. (2017) · Figshare · Cited by 322 · see citing works
Reviewing social media use by clinicians: Table 1
von Muhlen, M., & Ohno-Machado, L. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-000990 · Cited by 319 · see citing works
Shaw, T. J., McGregor, D., Brunner, M., Keep, M., Janssen, A. L., & Barnet, S. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.8106 · Cited by 313 · see citing works
How Health Care Professionals Use Social Media to Create Virtual Communities: An Integrative Review
Rolls, K., Hansen, M. M., Jackson, D. W., & Elliott, D. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5312 · Cited by 295 · see citing works
Health Care Provider Adoption of eHealth: Systematic Literature Review
Li, J., Talaei-Khoei, A., Seale, H., Ray, P., & MacIntyre, C. R. (2013) · interactive Journal of Medical Research · doi:10.2196/ijmr.2468 · Cited by 250 · see citing works
The diffusion of virtual communities in health care: Concepts and challenges
Demiris, G. (2006) · Patient Education and Counseling · doi:10.1016/j.pec.2005.10.003 · Cited by 249 · see citing works
Privacy, professionalism and Facebook: a dilemma for young doctors
MacDonald, J., Sohn, S., & Ellis, P. (2010) · Medical Education · doi:10.1111/j.1365-2923.2010.03720.x · Cited by 245 · see citing works
Web 2.0 technologies for undergraduate and postgraduate medical education: an online survey
Sandars, J., & Schroter, S. (2007) · Postgraduate Medical Journal · doi:10.1136/pgmj.2007.063123 · Cited by 219 · see citing works
Balatsoukas, P., Kennedy, C. M., Buchan, I. E., powell, J. A., & Ainsworth, J. D. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.3662 · Cited by 172 · see citing works
Gholami-Kordkheili, F., Wild, V., & Strech, D. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2708 · Cited by 166 · see citing works
Pan, Z., Lu, Y., Wang, B., & Chau, P. Y. K. (2017) · Journal of Management Information Systems · doi:10.1080/07421222.2017.1296747 · Cited by 163 · see citing works
Information-seeking behaviors and reflective practice
Bennett, N. L., Casebeer, L. L., Zheng, S., & Kristofco, R. E. (2006) · Journal of Continuing Education in the Health Professions · doi:10.1002/chp.60 · Cited by 126 · see citing works
Ariëns, L. F. M., Schussler-Raymakers, F. M., Frima, C., Flinterman, A. E., Hamminga, E. A., Arents, B. W. M., Bruijnzeel-Koomen, C. A. F. M., de Bruin-Weller, M. S., & van Os‐Medendorp, H. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7512 · Cited by 110 · see citing works
Alsobayel, H. (2016) · JMIR Medical Education · doi:10.2196/mededu.6232 · Cited by 107 · see citing works
Costs and Efficiency of Online and Offline Recruitment Methods: A Web-Based Cohort Study
Christensen, T., Riis, A. H., Hatch, E. E., Wise, L. A., Nielsen, M. G., Rothman, K. J., Sørensen, H. T., & Mikkelsen, E. M. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6716 · Cited by 100 · see citing works
Social Influence as a Driver of Engagement in a Web-Based Health Intervention
Poirier, J., & Cobb, N. K. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1957 · Cited by 97 · see citing works
Stewart, S. A., & Abidi, S. S. R. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1982 · Cited by 89 · see citing works
Lau, A. S. M. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1398 · Cited by 76 · see citing works
Merolli, M., Gray, K. M., Martin-Sanchez, F. J., & López–Campos, G. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.3915 · Cited by 72 · see citing works
Roland, D., Spurr, J., & Cabrera, D. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7072 · Cited by 70 · see citing works
Pharmacist use of social media
Alkhateeb, F. M., Clauson, K., & Latif, D. A. (2011) · International Journal of Pharmacy Practice · doi:10.1111/j.2042-7174.2010.00087.x · Cited by 65 · see citing works
Dumas, A., Lapointe, A., & Desroches, S. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9230 · Cited by 60 · see citing works
D'Souza, K., Henningham, L., Zou, R., Huang, J., O'Sullivan, E., Last, J., & Ho, K. (2017) · JMIR Medical Education · doi:10.2196/mededu.6429 · Cited by 53 · see citing works
Mishori, R., Singh, L., Levy, B., & Newport, C. C. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3006 · Cited by 52 · see citing works
Pharmacy Faculty Members' Perspectives on the Student/Faculty Relationship in Online Social Networks
Metzger, A. H., Finley, K., Ulbrich, T., & McAuley, J. (2010) · American Journal of Pharmaceutical Education · doi:10.5688/aj7410188 · Cited by 51 · see citing works
Barnett, S., Jones, S. C., Bennett, S., Iverson, D. C., & Bonney, A. D. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2555 · Cited by 48 · see citing works
Social Media for the Dissemination of Cochrane Child Health Evidence: Evaluation Study
Dyson, M. P., Newton, A. S., Shave, K., Featherstone, R. M., Thomson, D., Wingert, A., Fernandes, R. M., & Hartling, L. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7819 · Cited by 43 · see citing works
Wu, T., Deng, Z., Feng, Z., Gaskin, D. J., Zhang, D. “., & Wang, R. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9003 · Cited by 40 · see citing works
Dental Blogs, Podcasts, and Associated Social Media: Descriptive Mapping and Analysis
Melkers, J. E., Hicks, D. M., Rosenblum, S., Isett, K. R., & Elliott, J. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7868 · Cited by 39 · see citing works
Social Media in Health Science Education: An International Survey
O'Sullivan, E., Cutts, E., Kavikondala, S., Salcedo, A., D'Souza, K., Hernandez-Torre, M., Anderson, C., Tiwari, A., Ho, K., & Last, J. (2017) · JMIR Medical Education · doi:10.2196/mededu.6304 · Cited by 28 · see citing works
Davies, B. M., & Kotter, M. (2018) · JMIR Research Protocols · doi:10.2196/resprot.6567 · Cited by 28 · see citing works
Hart, M. W., Stetten, N. E., Islam, S. N., et al. (2017) · JMIR Public Health and Surveillance · doi:10.2196/publichealth.6795 · Cited by 18 · see citing works
Social Media for e-Government in the Public Health Sector: Protocol for a Systematic Review
Franco, M., Tursunbayeva, A., & Pagliari, C. (2016) · JMIR Research Protocols · doi:10.2196/resprot.5421 · Cited by 16 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Vukušić Rukavina, T., Viskić, J., Machala Poplašen, L., Relić, D., Marelić, M., Jokic, D., & Sedak, K. (2021) · Journal of Medical Internet Research · doi:10.2196/25770 · Cited by 199 · see citing works
“…88 studies, 49 were quantitative [32, 33, 35, 38-43, 51-53, 55, 59-61, 64, 66-69, 71, 72, 74, 75, 77-80, 82, 85, 88, 89, 91, 94-96, 98, 100, 102, 105-108, 110, 113, 114, 116, 118], 12 were qualitative [34, 36, 44, 45, 49, 57, 73, 76, 92, 93, 101, 104], and 27 used mixed methods [35, 37, 46-48,…”
Climate change and global health: a call to more research and more action
Agache, I., Sampath, V., Aguilera, J., Akdis, C. A., Akdis, M., Barry, M., Bouagnon, A., Chinthrajah, S., Collins, W., Dulitzki, C., et al. (2022) · Allergy · doi:10.1111/all.15229 · Cited by 183 · see citing works
Ruan, B., Yilmaz, Y., Lu, D., Lee, M., & Chan, T. M. (2020) · Journal of Medical Internet Research · doi:10.2196/21416 · Cited by 47 · see citing works
Marelić, M., Viskić, J., Poplašen, L. M., Relić, D., Jokić, D., & Rukavina, T. V. (2021) · BMC Medical Education · doi:10.1186/s12909-021-02879-2 · Cited by 31 · see citing works
Kerr, H., Booth, R., & Jackson, K. (2020) · Journal of Medical Internet Research · doi:10.2196/16540 · Cited by 31 · see citing works
Viskić, J., Jokić, D., Marelić, M., Machala Poplašen, L., Relić, D., Sedak, K., & Vukušić Rukavina, T. (2021) · Croatian Medical Journal · doi:10.3325/cmj.2021.62.569 · Cited by 24 · see citing works
Dang, T. K. C., Yeh, B.-I., Park, Y. C., Hoang, B. B., Vinh, K., & Park, K. H. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1703842 · Cited by 1 · see citing works
“The use of SM by medical doctors and medical students is increasing both personally and professionally (5).”
- Authors
- Hazzam, J., & Lahrech, A.
- Affiliations
- British University in Dubai (AE)
- Corresponding author
- Hazzam, Joe
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2018-11-07
- Volume
- 20
- Issue
- 11
- Pages
- e12035
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC6249501
- OpenAlex
- W2897876466
- Cited by
- 97
- References
- 51
- Citations by year
- 2026: 8 · 2025: 4 · 2024: 8 · 2023: 17 · 2022: 20 · 2021: 21 · 2020: 14 · 2019: 5
- FWCI
- 10.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Health › Social Media in Health Education
- Physical Sciences › Computer Science › Computer Networks and Communications › Media and Digital Communication
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Keywords
- healthcare workers, social media use, social media adoption, technology acceptance theory, behavioral intention, habit, attitudes, perceived usefulness, WhatsApp, interpersonal communication, productivity improvement, physicians, pharmacists, allied health professionals, social media ethics, United Arab Emirates, health professions education, continuing professional development, knowledge sharing
- MeSH terms
- Health Personnel, Humans, Surveys and Questionnaires, Social Behavior, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review