The Technology Acceptance Model for Resource-Limited Settings (TAM-RLS): A Novel Framework for Mobile Health Interventions Targeted to Low-Literacy End-Users in Resource-Limited Settings
Open accessAbstract
Although mobile health (mHealth) technologies have shown promise in improving clinical care in resource-limited settings (RLS), they are infrequently brought to scale. One limitation to the success of many mHealth interventions is inattention to end-user acceptability, which is an important predictor of technology adoption. We conducted in-depth interviews with 43 people living with HIV in rural Uganda who had participated in a clinical trial of a short messaging system (SMS)-based intervention designed to prompt return to clinic after an abnormal laboratory test. Interviews focused on established features of technology acceptance models, including perceived ease of use and perceived usefulness, and included open-ended questions to gain insight into unexplored issues related to the intervention's acceptability. We used conventional (inductive) and direct content analysis to derive categories describing use behaviors and acceptability. Interviews guided development of a proposed conceptual framework, the technology acceptance model for resource-limited settings (TAM-RLS). This framework incorporates both classic technology acceptance model categories as well as novel factors affecting use in this setting. Participants described how SMS message language, phone characteristics, and experience with similar technologies contributed to the system's ease of use. Perceived usefulness was shaped by the perception that the system led to augmented HIV care services and improved access to social support from family and colleagues. Emergent themes specifically related to mHealth acceptance among PLWH in Uganda included (1) the importance of confidentiality, disclosure, and stigma, and (2) the barriers and facilitators downstream from the intervention that impacted achievement of the system's target outcome. The TAM-RLS is a proposed model of mHealth technology acceptance based upon end-user experiences in rural Uganda. Although the proposed model requires validation, the TAM-RLS may serve as a useful tool to guide design and implementation of mHealth interventions.
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Cites
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
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035 · see citing works
Three Approaches to Qualitative Content Analysis
Hsieh, H.-F., & Shannon, S. E. (2005) · Qualitative Health Research · doi:10.1177/1049732305276687 · Cited by 44,601 · see citing works
A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies
Venkatesh, V., & Davis, F. D. (2000) · Management Science · doi:10.1287/mnsc.46.2.186.11926 · Cited by 23,066 · 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
The DeLone and McLean Model of Information Systems Success: A Ten-Year Update
(2003) · Journal of Management Information Systems · doi:10.1080/07421222.2003.11045748 · Cited by 11,791 · see citing works
Technology Acceptance Model 3 and a Research Agenda on Interventions
Venkatesh, V., & Bala, H. (2008) · Decision Sciences · doi:10.1111/j.1540-5915.2008.00192.x · Cited by 8,159 · cited 1× · see citing works
“Subsequent iterations of TAM models have incorporated additional factors such as social norms and technology experience [18, 19].”
BackgroundExtrinsic and Intrinsic Motivation to Use Computers in the Workplace <sup>1</sup>
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992) · Journal of Applied Social Psychology · doi:10.1111/j.1559-1816.1992.tb00945.x · Cited by 6,231 · see citing works
A Model of the Antecedents of Perceived Ease of Use: Development and Test
Venkatesh, V., & Davis, F. D. (1996) · Decision Sciences · doi:10.1111/j.1540-5915.1996.tb00860.x · Cited by 3,854 · see citing works
A Model of the Antecedents of Perceived Ease of Use: Development and Test
Venkatesh, V., & Davis, F. D. (1996) · Decision Sciences · doi:10.1111/j.1540-5915.1996.tb01822.x · Cited by 3,802 · see citing works
The Technology Acceptance Model: Its past and its future in health care
Holden, R. J., & Karsh, B.-T. (2010) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2009.07.002 · Cited by 2,669 · see citing works
Self-citationThe Technology Acceptance Model: Past, Present, and Future
Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003) · Communications of the Association for Information Systems · doi:10.17705/1cais.01250 · Cited by 2,572 · see citing works
AGE DIFFERENCES IN TECHNOLOGY ADOPTION DECISIONS: IMPLICATIONS FOR A CHANGING WORK FORCE
MORRIS, M. G., & VENKATESH, V. (2000) · Personnel Psychology · doi:10.1111/j.1744-6570.2000.tb00206.x · Cited by 1,723 · see citing works
User Acceptance Enablers in Individual Decision Making About Technology: Toward an Integrated Model
Venkatesh, V., Speier, C., & Morris, M. G. (2002) · Decision Sciences · doi:10.1111/j.1540-5915.2002.tb01646.x · Cited by 923 · see citing works
Does the technology acceptance model predict actual use? A systematic literature review
Turner, M., Kitchenham, B., Brereton, P., Charters, S., & Budgen, D. (2010) · Information and Software Technology · doi:10.1016/j.infsof.2009.11.005 · Cited by 851 · see citing works
Mills, E. J., Nachega, J. B., Bangsberg, D. R., Singh, S., Rachlis, B., Wu, P., Wilson, K., Buchan, I., Gill, C. J., & Cooper, C. (2006) · PLoS Medicine · doi:10.1371/journal.pmed.0030438 · Cited by 818 · see citing works
Self-citationScaling Up mHealth: Where Is the Evidence?
Tomlinson, M., Rotheram-Borus, M. J., Swartz, L., & Tsai, A. C. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001382 · Cited by 673 · see citing works
Aranda-Jan, C. B., Mohutsiwa-Dibe, N., & Loukanova, S. (2014) · BMC Public Health · doi:10.1186/1471-2458-14-188 · Cited by 644 · see citing works
Inhibitors and Enablers as Dual Factor Concepts in Technology Usage
Cenfetelli, R. T. (2004) · Journal of the Association for Information Systems · doi:10.17705/1jais.00059 · Cited by 393 · see citing works
Chib, A., van Velthoven, M. H., & Car, J. (2015) · Journal of Health Communication · doi:10.1080/10810730.2013.864735 · Cited by 384 · see citing works
Tuller, D. M., Bangsberg, D. R., Senkungu, J. K., Ware, N. C., Emenyonu, N. I., & Weiser, S. D. (2009) · AIDS and Behavior · doi:10.1007/s10461-009-9533-2 · Cited by 379 · see citing works
Self-citationMuessig, K. E., Nekkanti, M., Bauermeister, J. A., Bull, S. S., & Hightow‐Weidman, L. (2015) · Current HIV/AIDS Reports · doi:10.1007/s11904-014-0239-3 · Cited by 321 · see citing works
Brown, W., Yen, P.-Y., Rojas, M., & Schnall, R. (2013) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2013.08.001 · Cited by 299 · see citing works
Lankowski, A. J., Siedner, M. J., Bangsberg, D. R., & Tsai, A. C. (2014) · AIDS and Behavior · doi:10.1007/s10461-014-0729-8 · Cited by 261 · see citing works
Self-citationDuff, P., Kipp, W. E., Wild, T. C., Rubaale, T., & Okech‐Ojony, J. (2010) · Journal of the International AIDS Society · doi:10.1186/1758-2652-13-37 · Cited by 232 · see citing works
mHealth for HIV Treatment & Prevention: A Systematic Review of the Literature
Catalani, C., Philbrick, W., Fraser, H., Mechael, & Israelski, D. M. (2013) · The Open AIDS Journal · doi:10.2174/1874613620130812003 · Cited by 230 · see citing works
Bezabhe, W., Chalmers, L., Bereznicki, L., Peterson, G., Bimirew, M. A., & Kassie, D. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0097353 · Cited by 213 · see citing works
Schnall, R., Higgins, T., Brown, W., Carballo‐Diéguez, A., & Henry, S. B. (2015) · Studies in health technology and informatics · doi:10.3233/978-1-61499-564-7-467 · Cited by 197 · see citing works
Valdez, R. S., Holden, R. J., Novak, L. L., & Veinot, T. C. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2014-002826 · Cited by 194 · see citing works
Self-citationHoque, M. R. (2016) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-016-0289-0 · Cited by 181 · see citing works
Briscoe, C., & Aboud, F. E. (2012) · Social Science & Medicine · doi:10.1016/j.socscimed.2012.03.016 · Cited by 180 · see citing works
WHO'S AFRAID OF THE VIRTUAL WORLD? Anxiety and Computer-Mediated Communication
Brown, S. A., Fuller, R. M., & Vician, C. M. (2004) · Journal of the Association for Information Systems · doi:10.17705/1jais.00046 · Cited by 177 · see citing works
Mobile applications for the health sector
Miller, R. J., Qiang, C. Z., Yamamichi, M., & Hausman, V. (2012) · Cited by 176 · see citing works
Haberer, J. E., Kiwanuka, J., Nansera, D., Wilson, I. B., & Bangsberg, D. R. (2010) · AIDS and Behavior · doi:10.1007/s10461-010-9720-1 · Cited by 158 · see citing works
Self-citationDahab, M., Charalambous, S., Hamilton, R., Fielding, K., Kielmann, K., Churchyard, G., & Grant, A. D. (2008) · BMC Public Health · doi:10.1186/1471-2458-8-63 · Cited by 158 · see citing works
Chang, L. W., Njie-Carr, V., Kalenge, S., Kelly, J. F., Bollinger, R. C., & Alamo-Talisuna, S. (2013) · AIDS Care · doi:10.1080/09540121.2013.774315 · Cited by 111 · see citing works
Siedner, M. J., Lankowski, A. J., Tsai, A. C., Muzoora, C. K., Martin, J. N., Hunt, P. W., Haberer, J. E., & Bangsberg, D. R. (2013) · AIDS · doi:10.1097/qad.0b013e32835fd873 · Cited by 100 · see citing works
Self-citationSiedner, M. J., Haberer, J. E., Bwana, M. B., Ware, N. C., & Bangsberg, D. R. (2012) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-12-56 · Cited by 98 · see citing works
Self-citationKingori, P. (2013) · Social Science & Medicine (1967) · doi:10.1016/j.socscimed.2013.10.013 · Cited by 97 · see citing works
Holden, R. J. (2011) · Journal of Patient Safety · doi:10.1097/pts.0b013e3182388cfa · Cited by 95 · see citing works
Self-citationPreferences for a Mobile HIV Prevention App for Men Who Have Sex With Men
Goldenberg, T., McDougal, S. J., Sullivan, P., Stekler, J. D., & Stephenson, R. (2014) · JMIR mHealth and uHealth · doi:10.2196/mhealth.3745 · Cited by 91 · see citing works
Chaiyachati, K. H., Loveday, M., Lorenz, S., Lesh, N., Larkan, L.-M., Cinti, S., Friedland, G. H., & Haberer, J. E. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064662 · Cited by 90 · see citing works
Self-citationChan, B. T., Weiser, S. D., Boum, Y., Siedner, M. J., Mocello, A. R., Haberer, J. E., Hunt, P. W., Martin, J. N., Mayer, K. H., Bangsberg, D. R., et al. (2014) · AIDS · doi:10.1097/qad.0000000000000495 · Cited by 82 · see citing works
Self-citationHolden, R. J., Brown, R., Scanlon, M. C., & Karsh, B. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000754 · Cited by 73 · see citing works
Self-citationMoHTAM: A Technology Acceptance Model for Mobile Health Applications
Mohamed, A. H. H. M., Tawfik, H., Al‐Jumeily, D., & Norton, L. (2011) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese.2011.79 · Cited by 70 · see citing works
van Heerden, A., Norris, S., Tollman, S., Richter, L., & Rotheram-Borus, M. J. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2207 · Cited by 66 · see citing works
Siedner, M. J., Santorino, D., Haberer, J. E., & Bangsberg, D. R. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.3859 · Cited by 51 · see citing works
Self-citationSiedner, M., Santorino, D., Lankowski, A., Kanyesigye, M., Bwana, M., Haberer, J., & Bangsberg, D. (2015) · BMC Medicine · doi:10.1186/s12916-015-0397-1 · Cited by 49 · see citing works
Self-citationLai, T.-Y., Larson, E., Rockoff, M. L., & Henry, S. B. (2007) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2481 · Cited by 49 · see citing works
Horvath, K. J., Alemu, D., Danh, T., Baker, J. V., & Carrico, A. W. (2016) · JMIR mHealth and uHealth · doi:10.2196/mhealth.5287 · Cited by 43 · see citing works
Mitchell, J. W., Torres, M. B., Joe, J., Danh, T., Gass, B. V., & Horvath, K. J. (2016) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6178 · Cited by 39 · see citing works
Schnall, R., & Henry, S. B. (2011) · Informatics for Health and Social Care · doi:10.3109/17538157.2011.584998 · Cited by 35 · see citing works
Campbell, J. I., & Haberer, J. (2015) · Current HIV/AIDS Reports · doi:10.1007/s11904-015-0282-8 · Cited by 27 · see citing works
Self-citationSafren, S. A., Kumarasamy, N., Hosseinipour, M. C., Harwood, M. M., Hoffman, I., McCauley, M., Jumbe, A. N., Nyirenda, C., Mimiaga, M. J., Solomon, S., et al. (2006) · AIDS and Behavior · doi:10.1007/s10461-006-9094-6 · Cited by 21 · see citing works
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Hicks, J. P., Allsop, M. J., Akaba, G. O., Yalma, R. M., Dirisu, O., Okusanya, B., Tukur, J., Okunade, K., Akeju, D., Ajepe, A., et al. (2021) · JMIR mHealth and uHealth · doi:10.2196/24182 · Cited by 26 · see citing works
Musyoka, C. M., Mbwayo, A., Donovan, D. M., & Mathai, M. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000177 · Cited by 11 · see citing works
- Authors
- Campbell, J. I., Aturinda, I., Mwesigwa, E., Burns, B., Santorino, D., Haberer, J. E., Bangsberg, D. R., Holden, R. J., Ware, N. C., & Siedner, M. J.
- Affiliations
- Boston Medical Center (US)
- Mbarara University of Science and Technology (UG)
- Massachusetts General Hospital (US)
- Harvard University (US)
- Portland State University (US)
- Indiana University Indianapolis (US)
- Corresponding author
- Siedner, Mark J.
- ORCID
- Venue
- AIDS and Behavior
- Published
- 2017-04-18
- Volume
- 21
- Issue
- 11
- Pages
- 3129-3140
- ISSN
- 1090-7165 (ISSN-L)
- 1573-3254
- PMC
- PMC5763567
- OpenAlex
- W2606947362
- Cited by
- 105
- References
- 54
- Citations by year
- 2026: 10 · 2025: 8 · 2024: 14 · 2023: 14 · 2022: 11 · 2021: 14 · 2020: 21 · 2019: 5 · 2018: 8
- FWCI
- 15.01
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- AIDS and Behavior (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- PDXScholar (Portland State University) (submittedVersion)
- Purdue University Indianapolis (Indiana University) (submittedVersion)
- Funders
- National Institutes of Health (R24 TW007988, T32 AI007433, K23 MH099916)
- Harvard Global Health Insitute
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Computer Science › Information Systems › ICT in Developing Communities
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- technology acceptance model, mobile health, resource-limited settings, low-literacy users, people living with HIV, text messaging intervention, perceived ease of use, perceived usefulness, confidentiality, HIV stigma, disclosure, rural Uganda, conceptual framework
- MeSH terms
- Literacy, Ambulatory Care Facilities, Confidentiality, Female, Health Resources, Humans, Interviews as Topic, Patient Acceptance of Health Care, Perception, Rural Population, Uganda, HIV Infections, Telemedicine, Biomedical Technology, Disclosure
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