Effect of barrier related factors on perceived usefulness and ease of use of social media applications in the Australian healthcare sector
DOI:
https://doi.org/10.3127/ajis.v25i0.2625Keywords:
social media, healthcare professional, privavy threat, perceived usefulness, professional boundary, trustAbstract
Despite the growing popularity of social media internationally, an extant review of the literature revealed a low rate of social media usage among healthcare professionals. While cynicism amongst healthcare professionals might be a reason, there might be other factors that could explain healthcare professionals’ reluctance to use social media in their practices. This research investigated potential barriers that affected healthcare professionals’ behavioural intention to use social media. A cross-sectional survey was randomly administered to 824 healthcare professionals working in Australian healthcare organisations. At the end of data collection, 219 usable responses were collected. Analysis of data via structural equation model (SEM) found that perceived trust, privacy threats, professional boundary, facilitating conditions and self-efficacy significantly influence the notion of perceived usefulness and ease of use. In addition, information quality directly influences health professionals’ perceived ease of utilising social media technology. The result also indicated that gender moderates the relationship between barrier-related factors and perceived usefulness and ease of use. This study’s findings have important implications for healthcare providers and policymakers regarding medical professionals’ perceptions of the potential challenges in using social media as well as developing strategies to counter misinformation against the backdrop of COVID-19.
References
Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75-90. doi: https://doi.org/10.1016/j.chb.2016.05.014.
Abedin, B., Abedin, B., Khoei, T. T., & Ghapanchi, A. R. (2013). A review of critical factors for communicating with customers on social networking sites. The International Technology Management Review, 3(4), 208-218. doi: https://dx.doi.org/10.2991/ itmr.2013.3.4.1
Abedin, B., Erfani, S., & Blount, Y. (2017). Social media adoption framework for aged care service providers in Australia. In 2017 International Conference on Research and Innovation in Information Systems (ICRIIS) (pp. 1-6). IEEE. doi: https://doi.org/10.1109/ ICRIIS.2017.8002485
Adhikari, K., & Panda, R. K. (2018). Users' information privacy concerns and privacy protection behaviors in social networks. Journal of Global Marketing, 31(2), 96-110. doi: https://doi.org/10.1080/08911762.2017.1412552
Adzharuddin, N. A., & Ramly, N. M. (2015). Nourishing healthcare information over Facebook. Procedia-Social and Behavioral Sciences, 172, 383-389. doi: https://doi.org/10.1016/j.sbspro.2015.01.384
Al-Ammary, J. H., Al-Sherooqi, A. K., & Al-Sherooqi, H. K. (2014). The acceptance of social networking as a learning tools at University of Bahrain. International Journal of Information Education Technology, 4(2), 208-214.
Al-Muhtadi, J., Shahzad, B., Saleem, K., Jameel, W., & Orgun, M. (2019). Cybersecurity and privacy issues for socially integrated mobile healthcare applications operating in a multi-cloud environment. Health Informatics Journal, 25(2), 315-329. doi: https://doi.org/10.1177/1460458217706184
Al-Mushasha, N. F. A. (2013, May). Determinants of e-learning acceptance in higher education environment based on extended technology acceptance model. In 2013 Fourth International Conference on e-Learning" Best Practices in Management, Design and Development of e-Courses: Standards of Excellence and Creativity" (pp. 261-266). IEEE. doi: 10.1109/ECONF.2013.50
Al-Qirim, N. (2007). Championing telemedicine adoption and utilization in healthcare organizations in New Zealand. International Journal of Medical Informatics, 76(1), 42-54. doi: https://doi.org/10.1016/j.ijmedinf.2006.02.001
Ali-Hassan, H., Nevo, D., & Wade, M. (2015). Linking dimensions of social media use to job performance: The role of social capital. The Journal of Strategic Information Systems, 24(2), 65-89. doi: https://doi.org/10.1016/j.jsis.2015.03.001
Alwan, K., Ayele, T. A., & Tilahun, B. (2015). Knowledge and utilization of computers among health professionals in a developing country: a cross-sectional study. JMIR Human Factors, 2(1), e4. doi:10.2196/humanfactors.4184
Antheunis, M. L., Tates, K., & Nieboer, T. E. (2013). Patients’ and health professionals’ use of social media in health care: motives, barriers and expectations. Patient Education and Counseling, 92(3), 426-431. doi: https://doi.org/10.1016/j.pec.2013.06.020
Archambault, P. M., Bilodeau, A., Gagnon, M.-P., Aubin, K., Lavoie, A., Lapointe, J., . . . Légaré, F. (2012). Health care professionals’ beliefs about using wiki-based reminders to promote best practices in trauma care. Journal of Medical Internet Research, 14(2). doi:10.2196/jmir.1983
Awad, N. F., & Krishnan, M. S. (2006). The personalization privacy paradox: an empirical evaluation of information transparency and the willingness to be profiled online for personalization. MIS Quarterly, 30, 13-28. doi: https://doi.org/10.2307/25148715.
Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215. doi: https://doi.org/10.1037/0033-295X.84.2.191
Bansal, G., & Gefen, D. (2010). The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decision Support Systems, 49(2), 138-150. doi: https://doi.org/10.1016/j.dss.2010.01.010
Barnes, S. S., Kaul, V., & Kudchadkar, S. R. (2019). Social media engagement and the critical care medicine community. Journal of Intensive Care Medicine, 34(3), 175-182. doi: https://doi.org/10.1177/0885066618769599
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. doi: https://doi.org/10.1057/palgrave.ejis.3000717
Blendon, R. J., Schoen, C., DesRoches, C. M., Osborn, R., Scoles, K. L., & Zapert, K. (2002). Inequities in health care: a five-country survey. Health Affairs, 21(3), 182-191. doi: https://doi.org/10.1377/hlthaff.21.3.182
Brandyberry, A. A., Li, X., & Lin, L. (2010, August). Determinants of Perceived Usefulness and Perceived Ease of Use in Individual Adoption of Social Network Sites. In AMCIS (p. 544). URL: https://aisel.aisnet.org/amcis2010/544/
Braun, M. T. (2013). Obstacles to social networking website use among older adults. Computers in Human Behavior, 29(3), 673-680. doi: https://doi.org/10.1016/ j.chb.2012.12.004
Buettner, R. (2015, January). Analyzing the problem of employee internal social network site avoidance: Are users resistant due to their privacy concerns?. In 2015 48th Hawaii International Conference on System Sciences (pp. 1819-1828). IEEE. doi: 10.1109/HICSS.2015.220
Burton-Jones, A., & Straub Jr, D. W. (2006). Reconceptualizing system usage: An approach and empirical test. Information Systems Research, 17(3), 228-246. doi: https://doi.org/10.1287/isre.1060.0096
Cain, J. (2011). Social media in health care: the case for organizational policy and employee education. American Journal of Health-System Pharmacy, 68(11), 1036-1040. doi: https://doi.org/10.2146/ajhp100589
Chang, I.-C., Hwang, H.-G., Hung, W.-F., & Li, Y.-C. (2007). Physicians’ acceptance of pharmacokinetics-based clinical decision support systems. Expert Systems with Applications, 33(2), 296-303. doi: https://doi.org/10.1016/j.eswa.2006.05.001
Chau, M., & Xu, J. (2012). Business intelligence in blogs: Understanding consumer interactions and communities. MIS Quarterly, 36(4), 1189-1216. doi: https://doi.org/10.2307/41703504
Chau, P. Y., & Hu, P. J.-H. (2002). Investigating healthcare professionals’ decisions to accept telemedicine technology: an empirical test of competing theories. Information & Management, 39(4), 297-311. doi: https://doi.org/10.1016/S0378-7206(01)00098-2
Cheng, T. E., Lam, D. Y., & Yeung, A. C. (2006). Adoption of internet banking: an empirical study in Hong Kong. Decision Support Systems, 42(3), 1558-1572. doi: https://doi.org/10.1016/j.dss.2006.01.002
Chou, W.-Y. S., Hunt, Y. M., Beckjord, E. B., Moser, R. P., & Hesse, B. W. (2009). Social media use in the United States: implications for health communication. Journal of Medical Internet Research, 11(4). doi:10.2196/jmir.1249
Chretien, K. C., & Kind, T. (2013). Social media and clinical care: ethical, professional, and social implications. Circulation, 127(13), 1413-1421. doi: https://doi.org/10.1161/ CIRCULATIONAHA.112.128017
CivicWebMedia. (2021, 18/01/2021). Australia’s most popular social media sites 2021. Retrieved from https://www.civicwebmedia.com.au/australias-most-popular-social-media-sites/
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211. doi: https://doi.org/10.2307/249688
Conway, M., Cao, Y., & Hong, P. (2011). Antecedents and consequences of social media utilization in college teaching: a proposed model with mixed‐methods investigation. On the Horizon, 19(4), 297-306. doi: https://doi.org/10.1108/10748121111179420
Cuan-Baltazar, J. Y., Muñoz-Perez, M. J., Robledo-Vega, C., Pérez-Zepeda, M. F., & Soto-Vega, E. (2020). Misinformation of COVID-19 on the internet: infodemiology study. JMIR Public Health Surveillance, 6(2), e18444. doi: https://doi.org/10.2196/18444
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. doi: https://doi.org/10.2307/ 249008
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. doi: https://doi.org/10.1287/mnsc.35.8.982
DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60-95. doi: https://doi.org/10.1287/isre.3.1.60
Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9-30. doi: https://doi.org/10.1080/07421222.2003.11045748
Denecke, K., Bamidis, P., Bond, C., Gabarron, E., Househ, M., Lau, A., . . . Hansen, M. (2015). Ethical issues of social media usage in healthcare. Yearbook of Medical Informatics, 10(1), 137. doi: 10.15265/IY-2015-001
Devine, P. J. (2017). Social Media and Health Care. Using Social Media to Build Library Communities: A LITA Guide, 65.
Duyck, P., Pynoo, B., Devolder, P., Voet, T., Adang, L., & Vercruysse, J. (2008). User acceptance of a picture archiving and communication system. Methods of Information in Medicine, 47(02), 149-156. doi: 10.3414/ME0477
Erfani, S. S., Abedin, B., & Blount, Y. (2017). The effect of social network site use on the psychological well‐being of cancer patients. Journal of the Association for Information Science, 68(5), 1308-1322. doi: https://doi.org/10.1002/asi.23702
Farnan, J. M., Paro, J. A., Higa, J. T., Reddy, S. T., Humphrey, H. J., & Arora, V. M. (2009). Commentary: the relationship status of digital media and professionalism: it's complicated. Academic Medicine, 84(11), 1479-1481. doi: 10.1097/ ACM .0b013e3181bb17af
Farnan, J. M., Sulmasy, L. S., Worster, B. K., Chaudhry, H. J., Rhyne, J. A., & Arora, V. M. (2013). Online medical professionalism: patient and public relationships: policy statement from the American College of Physicians and the Federation of State Medical Boards. Annals of Internal Medicine, 158(8), 620-627. doi: https://doi.org/10.7326/0003-4819-158-8-201304160-00100
Featherman, M. S., Miyazaki, A. D., & Sprott, D. E. (2010). Reducing online privacy risk to facilitate e‐service adoption: the influence of perceived ease of use and corporate credibility. Journal of Services Marketing, 24(3), 219-229. doi: https://doi.org/10.1108/08876041011040622
Fernández-Luque, L., & Bau, T. (2015). Health and social media: perfect storm of information. Healthcare Informatics Research, 21(2), 67-73. doi: 10.4258/hir.2015.21.2.67
Fisher, J., & Clayton, M. (2012). Who gives a tweet: assessing patients’ interest in the use of social media for health care? Worldviews on Evidence‐Based Nursing, 9(2), 100-108. doi: https://doi.org/10.1111/j.1741-6787.2012.00243.x
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382-388. doi: 10.1177/002224378101800313
Franko, O. I., & Tirrell, T. F. (2012). Smartphone app use among medical providers in ACGME training programs. Journal of Medical Systems, 36(5), 3135-3139. doi: https://doi.org/10.1007/s10916-011-9798-7
Gagnon, K., & Sabus, C. (2015). Professionalism in a digital age: opportunities and considerations for using social media in health care. Physical Therapy, 95(3), 406-414. doi: https://doi.org/10.2522/ptj.20130227
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90. doi: https://doi.org/10.2307/30036519
Gholami-Kordkheili, F., Wild, V., & Strech, D. (2013). The impact of social media on medical professionalism: a systematic qualitative review of challenges and opportunities. Journal of Medical Internet Research, 15(8), e184. doi:10.2196/jmir.2708
Grajales III, F. J., Sheps, S., Ho, K., Novak-Lauscher, H., & Eysenbach, G. (2014). Social media: a review and tutorial of applications in medicine and health care. Journal of Medical Internet Research, 16(2). doi:10.2196/jmir.2912
Gupta, B., Joshi, S., & Agarwal, M. (2012). The effect of expected benefit and perceived cost on employees’ knowledge sharing behavior: A study of IT employees in India. Organizations Markets in Emerging Economies, 3(1), 8-19. doi:10.15388/omee.2012.3.1.14272.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis, 6th ed. Uppersaddle River: Pearson Prentice Hall.
Hansen, J. M., Saridakis, G., & Benson, V. (2018). Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions. Computers in Human Behavior, 80, 197-206. doi: https://doi.org/10.1016/j.chb.2017.11.010
Hanson, C. L., West, J., Thackeray, R., Barnes, M. D., & Downey, J. (2014). Understanding and predicting social media use among community health center patients: a cross-sectional survey. Journal of Medical Internet Research, 16(11), e270. doi:10.2196/jmir.3373
Hocevar, K. P., Flanagin, A. J., & Metzger, M. J. (2014). Social media self-efficacy and information evaluation online. Computers in Human Behavior, 39, 254-262. https://doi.org/10.1016/j.chb.2014.07.020
Holden, R. J., & Karsh, B.-T. (2010). The technology acceptance model: its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159-172. doi: https://doi.org/10.1016/j.jbi.2009.07.002
Hoque, M. R., Bao, Y., & Sorwar, G. (2017). Investigating factors influencing the adoption of e-Health in developing countries: A patient’s perspective. Informatics for Health Social Care, 42(1), 1-17. https://doi.org/10.3109/17538157.2015.1075541
Househ, M., Borycki, E., & Kushniruk, A. (2014). Empowering patients through social media: the benefits and challenges. Health Informatics Journal, 20(1), 50-58. doi: https://doi.org/10.1177/1460458213476969
Hsu, M.-H., & Chiu, C.-M. (2004). Internet self-efficacy and electronic service acceptance. Decision Support Systems, 38(3), 369-381. doi: https://doi.org/10.1016/j.dss.2003.08.001
Jo, H. S., Song, T.-M., & Kim, B. G. (2017). Analysis of the Factors Affecting Consumer Acceptance of Accredited Online Health Information. Journal of Korean Medical Science, 32(11), 1757-1763. doi: https://doi.org/10.3346/ jkms.2017.32.11.1757
Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212. doi: https://doi.org/10.1016/j.techsoc.2019.101212
Kamboj, S., & Rahman, Z. (2016). The influence of user participation in social media-based brand communities on brand loyalty: age and gender as moderators. Journal of Brand Management, 23(6), 679-700. doi: https://doi.org/10.1057/s41262-016-0002-8
Kapoor, K. K., Tamilmani, K., Rana, N. P., Patil, P., Dwivedi, Y. K., & Nerur, S. (2018). Advances in social media research: Past, present and future. Information Systems Frontiers, 20(3), 531-558. doi: https://doi.org/10.1007/s10796017-9810-y
Keir, A., Bamat, N., Patel, R. M., Elkhateeb, O., & Roland, D. (2019). Utilising social media to educate and inform healthcare professionals, policy-makers and the broader community in evidence-based healthcare. BMJ Evidence-Based Medicine, 24(3), 87-89. doi: http://dx.doi.org/10.1136/bmjebm-2018-111016
Kline, S., Dyer-Witheford, N., & De Peuter, G. (2003). Digital play: The interaction of technology, culture, and marketing. McGill-Queen's University Press.
Kotsenas, A. L., Arce, M., Aase, L., Timimi, F. K., Young, C., & Wald, J. T. (2018). The strategic imperative for the use of social media in health care. Journal of the American College of Radiology, 15(1), 155-161. doi: https://doi.org/10.1016/j.jacr.2017.09.027
Kouzy, R., Abi Jaoude, J., Kraitem, A., El Alam, M. B., Karam, B., Adib, E., Baddour, K. (2020). Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on Twitter. Cureus, 12(3). doi: https://dx.doi.org/10.7759%2Fcureus.7255
Kucukusta, D., Law, R., Besbes, A., & Legohérel, P. (2015). Re-examining perceived usefulness and ease of use in online booking. International Journal of Contemporary Hospitality Management, 27(2), 185-198. doi: https://doi.org/10.1108/IJCHM-09-2013-0413
Kuek, A., & Hakkennes, S. (2020). Healthcare staff digital literacy levels and their attitudes towards information systems. Health Informatics Journal, 26(1), 592-612. doi: https://doi.org/10.1177/1460458219839613
Kunst, H., Groot, D., Latthe, P. M., Latthe, M., & Khan, K. S. (2002). Accuracy of information on apparently credible websites: survey of five common health topics. British Medical Journal, 324(7337), 581-582. doi: https://doi.org/10.1136/bmj.324.7337.581
Kwon, J. H., Kye, S.-Y., Park, E. Y., Oh, K. H., & Park, K. (2015). What predicts the trust of online health information? Epidemiology Health Communication, 37(e2015030.). doi: 10.4178/epih/e2015030
Lanier Jr, C. D., & Saini, A. (2008). Understanding consumer privacy: A review and future directions. Academy of Marketing Science Review, 2008, 12(2).
Lau, A. S. (2011). Hospital-based nurses’ perceptions of the adoption of Web 2.0 tools for knowledge sharing, learning, social interaction and the production of collective intelligence. Journal of Medical Internet Research, 13(4), e92.doi:10.2196/jmir.1398
Lee, Y.-H., Hsieh, Y.-C., & Chen, Y.-H. (2013). An investigation of employees' use of e-learning systems: applying the technology acceptance model. Behaviour Information Technology, 32(2), 173-189. https://doi.org/10.1080/0144929X.2011.577190
Liaw, S.-S., & Huang, H.-M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers Education, 60(1), 14-24. doi: https://doi.org/10.1016/ j.compedu.2012.07.015
Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2014). Antecedents of the adoption of the new mobile payment systems: The moderating effect of age. Computers in Human Behavior, 35, 464-478. doi: https://doi.org/10.1016/j.chb.2014.03.022
Lin, W.-Y., Zhang, X., Song, H., & Omori, K. (2016). Health information seeking in the Web 2.0 age: Trust in social media, uncertainty reduction, and self-disclosure. Computers in Human Behavior, 56, 289-294. doi: https://doi.org/10.1016/j.chb.2015.11.055
Lober, W. B., & Flowers, J. L. (2011). Consumer empowerment in health care amid the internet and social media. Seminars in Oncology Nursing, 27(3),169-182. doi: https://doi.org/10.1016/j.soncn.2011.04.002
Machdar, N. M. (2019). The effect of information quality on Percieved usefulness and ease of ose Business Entrepreneurial Review, 15(2), 131-146. doi: http://dx.doi.org/10.25105/ber.v15i2.4630
MacLure, K., & Stewart, D. (2016). Digital literacy knowledge and needs of pharmacy staff: a systematic review. Journal of Innovation in Health Informatics, 23(3). doi: https://doi.org/10.14236/jhi.v23i3.840
Madnick, S. E., Wang, R. Y., Lee, Y. W., & Zhu, H. (2009). Overview and framework for data and information quality research. Journal of Data Information Quality, 1(1), 1-22. doi: https://doi.org/10.1145/1515693.1516680
Malik, S., & Coulson, N. S. (2010). ‘They all supported me but I felt like I suddenly didn't belong anymore’: an exploration of perceived disadvantages to online support seeking. Journal of Psychosomatic Obstetrics & Gynecology, 31(3), 140-149. https://doi.org/10.3109/0167482X.2010.504870
Martinasek, M. P., Panzera, A. D., Schneider, T., Lindenberger, J. H., Bryant, C. A., McDermott, R. J., & Couluris, M. (2011). Benefits and barriers of pediatric healthcare providers toward using social media in asthma care. American Journal of Health Education, 42(4), 213-221. doi: https://doi.org/10.1080/19325037.2011.10599190
McGowan, B. S., Wasko, M., Vartabedian, B. S., Miller, R. S., Freiherr, D. D., & Abdolrasulnia, M. (2012). Understanding the factors that influence the adoption and meaningful use of social media by physicians to share medical information. Journal of Medical Internet Research, 14(5), e117. doi:10.2196/jmir.2138
Moick, M., & Terlutter, R. (2012). Physicians' motives for professional internet use and differences in attitudes toward the internet-informed patient, physician–patient communication, and prescribing behavior. Medicine 2.0, 1(2), e2. doi: 10.2196/med20.1996
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222. doi: https://doi.org/10.1287/isre.2.3.192
Moorhead, S. A., Hazlett, D. E., Harrison, L., Carroll, J. K., Irwin, A., & Hoving, C. (2013). A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. Journal of Medical Internet Research, 15(4), e85. doi:10.2196/jmir.1933
Morosan, C. (2012). Theoretical and empirical considerations of guests’ perceptions of biometric systems in hotels: Extending the technology acceptance model. Journal of Hospitality Tourism Research, 36(1), 52-84. https://doi.org/10.1177/1096348010380601
Mou, J., Shin, D.-H., & Cohen, J. (2017). Understanding trust and perceived usefulness in the consumer acceptance of an e-service: a longitudinal investigation. Behaviour Information Technology, 36(2), 125-139.doi: https://doi.org/10.1080/0144929X.2016 .1203024
Nikou, S., Agahari, W., Keijzer-Broers, W., & de Reuver, M. (2019). Digital healthcare technology adoption by elderly people: A capability approach model. Journal of Telematics Informatics, 53, 101315. https://doi.org/10.1016/j.tele.2019.101315
O'Brien, L. (2017, September 23, 2017). The Inside Scoop Part 1: A comparison of the U.S. and Australian healthcare systems. onthewards.
Panahi, S., Watson, J., & Partridge, H. (2016). Social media and physicians: exploring the benefits and challenges. Health Informatics Journal, 22(2), 99-112. doi: https://doi.org/10.1177/1460458214540907
Park, A., Bowling, J., Shaw, G., Li, C., & Chen, S. (2019). Adopting Social Media for Improving Health Opportunities and Challenges. North Carolina Medical Journal, 80(4), 240-243. doi: https://doi.org/10.18043/ncm.80.4.240
Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134. doi: https://doi.org/10.1080/10864415.2003.11044275
Pavlou, P. A., & Gefen, D. (2004). Building effective online marketplaces with institution-based trust. Information Systems Research, 15(1), 37-59. doi: https://doi.org/10.1287/isre .1040.0015
Pavlou, P. A., Liang, H., & Xue, Y. (2007). Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective. MIS Quarterly, 31(1), 105-136. doi: https://doi.org/10.2307/25148783
Peñarroja, V., Sánchez, J., Gamero, N., Orengo, V., & Zornoza, A. M. (2019). The influence of organisational facilitating conditions and technology acceptance factors on the effectiveness of virtual communities of practice. Behaviour Information Technology, 38(8), 845-857. https://doi.org/10.1080/0144929X.2018.1564070
Robeyns, I. (2005). The capability approach: a theoretical survey. Journal of Human Development, 6(1), 93-117. https://doi.org/10.1080/146498805200034266
Rogers, E. M. (2003). Elements of diffusion. Diffusion of Innovations, 5(1.38).
Rolls, K., Hansen, M., Jackson, D., & Elliott, D. (2016). How health care professionals use social media to create virtual communities: an integrative review. Journal of Medical Internet Research, 18(6), e166. doi: https://doi.org/10.2196/jmir.5312
Saeed, K. A., & Abdinnour-Helm, S. (2008). Examining the effects of information system characteristics and perceived usefulness on post adoption usage of information systems. Information Management Decision, 45(6), 376-386. doi: https://doi.org/ 10.1016/j.im.2008.06.002
Salim, B. (2012). An application of UTAUT model for acceptance of social media in Egypt: A statistical study. International Journal of Information Science, 2(6), 92-105. doi: 10.5923/j.ijis.20120206.05
Smailhodzic, E., Hooijsma, W., Boonstra, A., & Langley, D. J. (2016). Social media use in healthcare: A systematic review of effects on patients and on their relationship with healthcare professionals. BMC Health Services Research, 16(1), 442. doi:10.1186/s12913-016-1691-0
Steenkamp, J.-B. E., & Baumgartner, H. (2000). On the use of structural equation models for marketing modeling. International Journal of Research in Marketing, 17(2-3), 195-202. doi: https://doi.org/10.1016/S0167-8116(00)00016-1
Talaei-Khoei, A., Lewis, L., Khoei, T., Ghapanchi, A., & Vichitvanichphong, S. (2015). Seniors' perspective on perceived transfer effects of assistive robots in elderly care: Capability approach analysis. In: 36th International Conference on Information Systems (ICIS 2015) - Exploring the Information Frontier, 13-15 December 2015, Fort Worth, Texas.
Teigland, R., & Wasko, M. M. (2003). Integrating knowledge through information trading: Examining the relationship between boundary spanning communication and individual performance. Decision Sciences, 34(2), 261-286. doi: https://doi.org/10.1111/1540-5915.02341
Teo, T. (2011). Modeling the determinants of pre‐service teachers' perceived usefulness of e‐learning. Campus-Wide Information Systems, 28(2), 124-140. doi: https://doi.org/10.1108/10650741111117824
Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: toward a conceptual model of utilization. MIS Quarterly, 15(1), 125-143. doi: https://doi.org/ 10.2307/249443
Triandis, H. C. (1979). Values, attitudes, and interpersonal behavior. Paper presented at the Nebraska symposium on motivation, 27, 195–259.
Usher, K., Woods, C., Casella, E., Glass, N., Wilson, R., Mayner, L., Mather, C. (2014). Australian health professions student use of social media. Collegian, 21(2), 95-101. doi: https://doi.org/10.1016/j.colegn.2014.02.004
Usher, W. (2011). Types of social media (Web 2.0) used by Australian allied health professionals to deliver early twenty-first-century practice promotion and health care. Social Work in Health Care, 50(4), 305-329. doi: 10.1080/00981389.2010.534317
Usher, W. T. (2012). Australian health professionals’ social media (Web 2.0) adoption trends: early 21st century health care delivery and practice promotion. Australian Journal of Primary Health, 18(1), 31-41. doi: https://doi.org/10.1071/PY10084
Van Der Velden, M., & El Emam, K. (2013). “Not all my friends need to know”: a qualitative study of teenage patients, privacy, and social media. Journal of the American Medical Informatics Association, 20(1), 16-24.doi: https://doi.org/10.1136/amiajnl-2012-000949
van Uden-Kraan, C. F., Drossaert, C. H., Taal, E., Smit, W. M., Seydel, E. R., & van de Laar, M. A. (2010). Experiences and attitudes of Dutch rheumatologists and oncologists with regard to their patients’ health-related Internet use. Clinical Rheumatology, 29(11), 1229-1236. doi: https://doi.org/10.1007/s10067-010-1435-1
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. doi: https://doi.org/10.1287/isre.11.4.342.11872
Venkatesh, V., & Davis, F. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204. doi:10.1287/mnsc.46.2.186.11926
Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481. doi: https://doi.org/10.1111/ j.1540-5915.1996.tb00860.x
Venkatesh, V., Sykes, T. A., & Zhang, X. (2011). 'Just what the doctor ordered': a revised UTAUT for EMR system adoption and use by doctors. Paper presented at the 2011 44th Hawaii International Conference on System Sciences.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. doi: https://doi.org/10.2307/41410412
Westin, A. F. J. W., & Review, L. L. (1968). Privacy and freedom. Washington and Lee Law Review, 25(1), 166-170.
Yarbrough, A. K., & Smith, T. B. (2007). Technology acceptance among physicians: a new take on TAM. Medical Care Research and Review, 64(6), 650-672. https://doi.org/10.1177/1077558707305942
Zhang, X., Guo, X., Lai, K.-h., Guo, F., & Li, C. (2014). Understanding gender differences in m-health adoption: a modified theory of reasoned action model. Telemedicine e-Health, 20(1), 39-46. https://doi.org/10.1089/tmj.2013.0092
Zhao, Y., Ni, Q., & Zhou, R. (2018). What factors influence the mobile health service adoption? A meta-analysis and the moderating role of age. International Journal of Information Management, 43, 342-350. doi: https://doi.org/10.1016/j.ijinfomgt.2017.08.006
Zhou, Z., Jin, X.-L., & Fang, Y. (2014). Moderating role of gender in the relationships between perceived benefits and satisfaction in social virtual world continuance. Decision Support Systems, 65, 69-79. doi: https://doi.org/10.1016/j.dss.2014.05.004
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