A contextualized IT adoption and use model for telemedicine in EthiopiaFootnote

Roztocki and Piotr Soja are the accepting Associate Editors for this paper.

References

• Ahlan, A. R., & Ahmad, B. I. E. (2014). User acceptance of health information technology (HIT) in developing countries: A conceptual model. Procedia Technology, 16, 1287–1296. doi: 10.1016/j.protcy.2014.10.145
   Google Scholar
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. doi: 10.1016/0749-5978(91)90020-T
   Web of Science ®Google Scholar
• Alvesson, M., & Kärreman, D. (2007). Constructing mystery: Empirical matters in theory development. Academy of Management Review, 32(4), 1265–1281. doi: 10.5465/amr.2007.26586822
   Web of Science ®Google Scholar
• Avgerou, C. (2008). Information systems in developing countries: A critical research review. Journal of Information Technology, 23, 133–146. doi: 10.1057/palgrave.jit.2000136
   Web of Science ®Google Scholar
• Bara-Slupski, T. K. (2016). Holistic approach: Paradigm shift in the research agenda for digitalisation of healthcare in sub-Saharan Africa. The African Journal of Information Systems, 8(4), 1–21.
   Web of Science ®Google Scholar
• Cook, T. D., Campbell, D., & Day, A. (1979). Quasi-experimentation: Design & analysis issues for field settings (Vol. 351). Boston, MA: Houghton Mifflin.
   Google Scholar
• Currie, W. (2009). Contextualising the IT artifact: Towards a wider research agenda for IS using institutional theory. Information Technology & People, 22(1), 63–77. doi: 10.1108/09593840910937508
   Web of Science ®Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. doi: 10.2307/249008
   Web of Science ®Google Scholar
• Dünnebeil, S., Sunyaev, A., Blohm, I., Leimeister, J. M., & Krcmar, H. (2012). Determinants of physicians’ technology acceptance for e-health in ambulatory care. International Journal of Medical Informatics, 81(11), 746–760. doi: 10.1016/j.ijmedinf.2012.02.002
   PubMed Web of Science ®Google Scholar
• Feather, N. T. (1992). Values, valences, expectations, and actions. Journal of Social Issues, 48(2), 109–124. doi: 10.1111/j.1540-4560.1992.tb00887.x
   Web of Science ®Google Scholar
• Federal Democratic Republic of Ethiopia Ministry of Health. (2016). Health governance watch, 1–76. Addis Ababa, Ethiopia.
   Google Scholar
• Garrett, K., McLoughlin, I., & Zelle, G. (2014). Disruptive innovation in health care: Business models, moral orders and electronic records. Social Policy and Society, 13(04), 579–592. doi: 10.1017/S1474746413000560
   Google Scholar
• Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using PLS-graph: Tutorial and annotated example. Communications of the AIS, 16, 91–109.
   Google Scholar
• Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modelling and regression: Guidelines for research practice. Communications of the AIS, 4(7), 1–78.
   Google Scholar
• Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A primer on partial least squares structural equation modelling (PLS-SEM). Thousand Oaks, CA: Sage Publications.
   Google Scholar
• Heeks, R. (2002). e-Government in Africa: Promise and practice. Information Policy, 7(2/3), 97–114.
   Google Scholar
• Hong, W., Chan, F., Thong, J., Chasalow, L., & Dhillon, G. (2014). A framework and guidelines for context-specific theorizing in information systems research. Information Systems Research, 25(1), 111–136. doi: 10.1287/isre.2013.0501
   Web of Science ®Google Scholar
• Karahanna, E., Agarwal, R., & Angst, C. M. (2006). Re-conceptualizing compatibility beliefs in technology acceptance research. MIS Quarterly, 30(4), 781–804. doi: 10.2307/25148754
   Web of Science ®Google Scholar
• Koh, C. E., Prybutok, V. R., Ryan, S. D., & Wu, Y. (2010). A model for mandatory use of software technologies: An integrative approach by applying multiple levels of abstraction of informing science. Informing Science: The International Journal of an Emerging Trans-Discipline, 13(1), 177–203. doi: 10.28945/1326
   Google Scholar
• Kuo, F. Y., & Young, M. L. (2008). Predicting knowledge sharing practices through intention: A test of competing models. Computers in Human Behavior, 24(6), 2697–2722. doi: 10.1016/j.chb.2008.03.015
   Web of Science ®Google Scholar
• Kwankam, S. Y. (2004). What e-health can offer? Bulletin of the World Health Organization, 82(10), 800–802.
   PubMed Web of Science ®Google Scholar
• Latifov, M. A., & Sahay, S. (2013). Challenges in moving to “health information for action”: An infrastructural perspective from a case study in Tajikistan. Information Technology for Development, 19(3), 215–229. doi: 10.1080/02681102.2012.751575
   Web of Science ®Google Scholar
• Locker, N. K., Vos, J. F., & Boonstra, A. (2016). Understanding national culture effects on user behavior in integrative IS implementations. Procedia Computer Science, 100, 289–297. doi: 10.1016/j.procs.2016.09.157
   Google Scholar
• Marmot, M. (2005). Social determinants of health inequalities. The Lancet, 365, 1099–1104. doi: 10.1016/S0140-6736(05)74234-3
   PubMed Web of Science ®Google Scholar
• Miah, S. J., Hasan, J., & Gammack, J. (2017). On-cloud healthcare clinic: An e-health consultancy approach for remote communities in a developing country. Telematics & Informatics, 34(1), 311–322. doi: 10.1016/j.tele.2016.05.008
   Web of Science ®Google Scholar
• National Institutes of Health. (2017). Cultural respect. Retrieved from https://www.nih.gov/institutes-nih/nih-office-director/office-communications-public-liaison/clear-communication/cultural-respect
   Google Scholar
• Nicolini, D. (2006). The work to make telemedicine work: A social and articulative view. Social Science & Medicine, 62, 2754–2767. doi: 10.1016/j.socscimed.2005.11.001
   PubMed Web of Science ®Google Scholar
• Pratt, B., & Hyder, A. (2017). Governance of global health research consortia: Sharing sovereignty and resources within future health systems. Social Science & Medicine, 174, 113–121. doi: 10.1016/j.socscimed.2016.11.039
   PubMed Web of Science ®Google Scholar
• Solomon, N. (2010). Learning assessment of a videoconference-based training: Lessons from medical training between USA and Ethiopia. Information Technology for Development, 16(3), 212–231. doi: 10.1080/02681102.2010.497274
   Web of Science ®Google Scholar
• Sood, S., Mbarika, V., Jugoo, S., Dookhy, R., Doarn, C. R., Prakash, N., & Merrell, R. C. (2007). What is telemedicine? A collection of 104 peer-reviewed perspectives and theoretical underpinnings. Telemedicine and e-Health, 13(5), 573–590. doi: 10.1089/tmj.2006.0073
   PubMed Web of Science ®Google Scholar
• Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption- implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 29(1), 28–45. doi: 10.1109/TEM.1982.6447463
   Web of Science ®Google Scholar
• Traore, B. B., Kamsu-Foguem, B., & Tangara, F. (2016). Integrating MDA and SOA for improving telemedicine services. Telematics & Informatics, 33(3), 733–741. doi: 10.1016/j.tele.2015.11.009
   Web of Science ®Google Scholar
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. doi: 10.2307/30036540
   Web of Science ®Google Scholar
• Venkatesh, V., Thong, J., & 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: 10.2307/41410412
   Web of Science ®Google Scholar
• World Health Organization. (2018). Health and development. Retrieved from http://www.who.int/hdp/en/
   Google Scholar
• Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43(3), 350–363. doi: 10.1016/j.im.2005.08.006
   Web of Science ®Google Scholar