CFP last date
20 June 2024
Reseach Article

Employing Technology Acceptance Model (TAM) to Determine the Acceptance of Diagnostic System for Cervical Cancer Screening in Developing Countries

by Shade Kuyoro, Kasali Funmilayo
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 127 - Number 13
Year of Publication: 2015
Authors: Shade Kuyoro, Kasali Funmilayo

Shade Kuyoro, Kasali Funmilayo . Employing Technology Acceptance Model (TAM) to Determine the Acceptance of Diagnostic System for Cervical Cancer Screening in Developing Countries. International Journal of Computer Applications. 127, 13 ( October 2015), 11-16. DOI=10.5120/ijca2015906574

@article{ 10.5120/ijca2015906574,
author = { Shade Kuyoro, Kasali Funmilayo },
title = { Employing Technology Acceptance Model (TAM) to Determine the Acceptance of Diagnostic System for Cervical Cancer Screening in Developing Countries },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 127 },
number = { 13 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 11-16 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2015906574 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:19:48.456849+05:30
%A Shade Kuyoro
%A Kasali Funmilayo
%T Employing Technology Acceptance Model (TAM) to Determine the Acceptance of Diagnostic System for Cervical Cancer Screening in Developing Countries
%J International Journal of Computer Applications
%@ 0975-8887
%V 127
%N 13
%P 11-16
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

In recent times, contemporary hospitals continue to become smart by automating their administrative processes using up to date equipment and incorporating latest technological principles into their activities. It has been seen over the years that the area of medical diagnosis systems require the use of diagnostic systems as they have been proven to have led to increased diagnostic accuracy and relieve experts from routine tasks. The easiest way to prevent women from suffering and dying from Cancer of the cervix is through early detection of the Human Papilloma Virus hence the recommendation of Visual Inspection with Acetic acid (VIA) to be done in developing countries by the World Health Organization. There is need for systems that can assist health workers in confirmation of results gotten after VIA tests has been done on patients to reduce misdiagnosis and overtreatment but such systems need to be developed by putting users need into consideration. Evaluating users’ acceptance of such systems is one of the most important metrics in ensuring the success of such systems as it helps to predict users’ willingness to accept or reject them. The Technology Acceptance Model (TAM) was used to evaluate the level of eagerness of users to use such systems and the measuring instrument was analyzed using SPSS version 21.0. A total of 150 respondents participated in this study with a response rate of 86%. From the analysis, it was realized that a total of 80.7% of the sampled population subscribed to the use of diagnostic expert systems, 89.1% believed that the use of such systems will have a positive impact and 87.6% were willing to use it. The results of TAM indicated the willingness of users to use such systems, the need to repeat the study after executing the system in real life was suggested as users intention could change, and also to identify factual usage of the system. The work brought to light the impact of putting users’ needs into consideration first since this increases user acceptability which could eventually lead to the success of such diagnostic systems at large.

  1. Aggelidis, V. P., Chatzoglou, P. D. (2009). Using a modified technology acceptance model in hospitals: International Journal of Medical Informatics, Volume 78, Issue 2, Pg. 115-126
  2. Anorlu, R. I. (2008). Cervical cancer: the sub-Saharan African perspective; Reproductive health matter: Elsevier journal, PII: S0968-8080(08)32415-X, Vol 16, Issue 39, Pg. 44.
  3. Babu, D., Kasthuri, G. (2013). Applying Technology Acceptance (TAM) model to determine the acceptance of Nursing Information System (NIS) for Computer Generated Nursing Care Plan among nurses: International Journal of Computer Trends and Technology (IJCTT), Vol. 4, Issue 8, ISSN: 2231-2803,
  4. Bilal, A., Mohd, S. A., and Wan, R. (2011). Overcoming challenges to use Electronic Medical Records System (EMRs) in Jordan Hospitals: IJCSNS International Journal of Computer Science and Network Security, Vol. 11, Issue 8
  5. Chuttur, M.Y. (2009). Overview of the Technology Acceptance Model: Origins, Developments and Future Directions," Indiana University, USA .Sprouts: Working Papers on Information Systems, Vol. 9, Issue 37,
  6. Davis, F.D., Bagozzi, R.P., Warshaw, P. R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Journal of Applied Social Psychology, Vol. 22, Issue14, Pg. 1111- 1132.
  7. Dillon, A. & Morris, M. (1996). User acceptance of new information technology: theories and models in M.Williams (ed) Annual Review of information Science and Technology, Vol. 31, Medford NJ: Information Today, Pg. 3-32
  8. Emmanuel, C. O., Adekunle, Y. A. (2013). Basic Concepts of Expert System Shells and an Efficient Model for Knowledge Acquisition: International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064 Vol 2 Issue 4, Pg. 554.
  9. Fred, D. (1991). User acceptance of information technology: system characteristics, user perceptions and behavioural impacts, Academic Press Limited, International Man-machine Studies Vol. 38, Pg. 475-487, ISBN 0020-7373.
  10. Hossain, L, de Silva, A. (2009). Exploring user acceptance of technology using social networks: Journal of High Technology Research, Vol. 20, Issue 1, Pg. 1-18
  11. Kai, Z. (2006). Design, Implementation, User Acceptance, and Evaluation of a Clinical Decision Support System for Evidence- Based Medicine Practice Carnegie Mellon University H. John Heinz III School of Public Policy and Management Pittsburgh, Pennsylvania 15213
  12. Kijsanayotin, B., Pannarunothai, S., Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand‘s community health centres: Applying the UTAUT model, International Journal Medical Informatics, Issue 79, Pg. 404-416.
  13. King, W. R., He, J. (2006). A meta-analysis of the technology acceptance model. Information and Management, Issue 43, Pg. 740-755
  14. Linda, M. G., Cynthia, I. & Gloria, M. (2010). Exploring the technology adoption needs of patients using e-health: IGI Global, document available online at www.
  15. Lynette, D., Michael, Q., Sankaranarayanan, R. (2006). Screening for cervical cancer in developing countries,Rlsevier Science direct Vaccine 24S3 S3/71–S3/77
  16. Mathieson, (1991). Predicting user intention: comparing the technology acceptance model with theory of planned behaviour. Information Systems Research, Vol 2, Issue 3, Pg. 173-191
  17. National Cancer Institute. (2013). what is Cancer? Retrieved from Retrieved on 1/7/2014
  18. Obstfelder, A., Engeseth, K. H., Wynn, R. (2007). Characteristics of successfully implemented telemedical applications, Implement Sci. Vol. 2, Issue 25.
  19. Odetola, T. D., Ekpo, K. (2012). Nigerian Women’s Perceptions about Human Papilloma Virus Immunisations, Journal of Community Medicine Health Education, Vol. 2, Issue 11, Pg. 1, ISSN: 2161-0711, doi:10.4172/2161-0711.100019
  20. Pouyan, E., Murali, S., Naresh, K., Hossein, N. (2013). The Effect of Knowledge Sharing on Technology Acceptance among Physicians, Global Advanced Research Journal of Engineering, Technology and Innovation, ISSN: 2315-5124, Vol. 2, Issue 2, Pg. 048-057, Available online at
  21. Priyanka, S., Ashok, K. (2013). Understanding the Evolution of Technology Acceptance Model: Computer Science and Management Research paper ISSN: 2321-7782, Vol 1, Issue 6,
  22. Sheng, O. R. L. (2000). Decision support for healthcare in a new information age, Decision Support Systems Vol. 30, Pg. 101–103
  23. Succi, M. J., Walter, Z. D. (1999). Theory of user acceptance of information technologies: An examination of health care professionals, 32nd Hawaii International Conference on System Sciences, Hawaii, IEEE Computer Society
  24. Thomas, A. H., Bengisu, T., Brian, H., Jacqueline, B. (2004). Use of Online Systems in Clinical Medical Assessments: An Analysis of Physician Acceptance of Online Disability Evaluation Systems, Proceedings of the 37th Hawaii International Conference on System Sciences
  25. Turban, E. & Aronson, J. E. (2001). Decision support systems and intelligent systems, sixth Edition (6th Ed). Hong Kong: Prentice International Hall
  26. TopTenReviews, Sample size calculator, Retrieved from
  27. West, D., Mangiameli, P. B., Rampal, R., West, V., (2003). Ensemble strategies for a medical diagnostic decision support system: A breast cancer diagnosis application, European Journal of Operational Research 162532–551
Index Terms

Computer Science
Information Sciences


Expert System Technology Acceptance Model Cervical cancer