Skip to main content

Advertisement

SpringerLink
Log in

Intention to Use a Mobile-Based Information Technology Solution for Tuberculosis Treatment Monitoring – Applying a UTAUT Model

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

Abstract

There are 2.2 million cases of tuberculosis (TB) in India, with an economic burden of $3 billion per year. Efficient monitoring of TB treatment is critical and the Indian Government’s current reliance on a pen and paper system for monitoring patients’ adherence to treatment is neither effective nor scalable. Employing the unified theory of acceptance and use of technology model (UTAUT) as its theoretical foundation, this study investigates the factors influencing the acceptance and use of a mobile-based IT solution for TB treatment monitoring. Data was collected from a survey of healthcare professionals working in TB treatment clinics and analysed using partial least squares structural equation modelling. Four constructs in the UTAUT model, effort expectancy (EE), facilitating conditions (FC), performance expectancy (PE) and social influence (SI) – were found to significantly and positively influence healthcare professionals’ behavioral intention to use the proposed mobile-based IT solution, and explained 56% of the variance. Importantly, our study validates the predictive capabilities of the UTAUT model in public health service delivery context in a developing country.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  • Achanta, S., Jaju, J., & Kumar, A. (2013). Tuberculosis management practices by private practitioners in Andhra Pradesh, India. PloS One, 13, e71119.

    Article  Google Scholar 

  • Agarwal, R., Gao, G., Des Rodches, C., & Jha, A. K. (2010). The digital transformation of healthcare: Current status and the road ahead. Information Systems Research, 21(4), 796–809.

    Article  Google Scholar 

  • Ajzen, I. (1991). The theory of planned behaviour. Organizational Behaviour and Human Decision Processes, 50(2), 179–211.

    Article  Google Scholar 

  • Alawadhi, S., & Morris, A. (2009). Factors influencing the adoption of e-government services. Journal of Software, 4(6), 584–590.

    Article  Google Scholar 

  • Alvesson, M., & Karreman, D. (2007). Constructing mystery: empirical matters in theory development. Academy of Management Review, 3(4), 1265–1281.

    Article  Google Scholar 

  • Anna, V., Jose-Maria, B., Maria-Teresa, D., Caterina, G., Consolaxion, D., Dulcis, S., et al. (2004). The role of mobile phones in improving vaccination rates in travellers. Preventive Medicine, 38, 503–509.

    Article  Google Scholar 

  • Armstrong, J. S., & Overton, T. S. (1977). Estimating Nonresponse bias in Mail Surveys. Journal of Marketing Research, 14, 396–402.

    Article  Google Scholar 

  • Bagchi, K., Hart, P., & Peterson, M. (2004). National culture and information technology product adoption. Journal of Global Information Technology, 7(4), 29–46.

    Article  Google Scholar 

  • Barclay, D. W., Higgins, C. A., & Thompson, R. (1995). The partial least squares approach to causal modeling: personal computer adoption and use as illustration. Technology Studies, 2, 285–309.

    Google Scholar 

  • Bauer, S., Percevic, R., Okon, E., Meermann, R., & Kordy, H. (2003). Use of text messaging in the aftercare of patients with bulimia nervosa. European Eating Disorders Review, 11, 279–290.

    Article  Google Scholar 

  • Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: a baseline model test and extension incorporating household life cycle. MIS Quarterly, 29(3), 399–426.

    Article  Google Scholar 

  • Brown, S. A., Kelly, H., & Schwar, A. (2006). Reflections on the role of theoretical extensions in the IS discipline: special issue introduction. The Data Base for Advances in Information Systems, 37(2–3), 8–14.

    Article  Google Scholar 

  • Brown, S. A., Dennis, A. R., & Venkatesh, V. (2010). Predicting collaboration technology use: integrating technology adoption and collaboration research. Journal of Management Information Systems, 27(2), 9–53.

    Article  Google Scholar 

  • Burton-Jones, A., & Hubona, G. S. (2006). The mediation of external variables in technology acceptance model. Information Management, 43, 706–717.

    Article  Google Scholar 

  • Carstensen, L. L., Fung, H. H., & Charles, S. T. (2003). Socioemotional selectivity theory and the regulation of emotion in the second half of life. Motivation Emotion, 27(June 2), 103–123.

    Article  Google Scholar 

  • Chang, I. C., Hwang, H. G., Hung, M. C., Lin, M. H., & Yen, D. C. (2007). Factors affecting the adoption of electronic signature: executives’ perspective of hospital information department. Decision Support Systems, 44(91), 350–359.

    Article  Google Scholar 

  • Chau, P. Y. K., & Hu, P. J. (2002). Investigating healthcare professionals’ decisions to accept telemedicine technology: an empirical test of competing theories. Information Management, 39(4), 297–311.

    Article  Google Scholar 

  • Chaudhry, B., Wang, J., Wu, S., Maglione, M., Mojica, W., Roth, E., Morton, S. C., & Shekelle, P. G. (2006). Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Annals of Internal Medicine, 144, 742–752.

    Article  Google Scholar 

  • Chin, W. W. (2010). How to write up and report PLS analysis. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications in marketing and related fields (pp. 655–690). Berlin: Springer.

    Chapter  Google Scholar 

  • Cimperman, M., Brencic, M. M., & Trkman, P. (2016). Analysing older users’ home telehealth services acceptance behaviour-applying an Extended UTAUT model. International Journal of Medical Informatics, 90, 22–31.

    Article  Google Scholar 

  • Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.

    Article  Google Scholar 

  • Compeau, D. R., & Higgins, C. A. (1995). Application of social cognitive theory to training for computer skills. Information Systems Research, 6(2), 118–143.

    Article  Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

    Article  Google Scholar 

  • 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), 982–1002.

    Article  Google Scholar 

  • De Vaus, D. A. (2014). Surveys in social research (6th ed.). Sydney: Allen and Unwin.

    Google Scholar 

  • Downer, S. R., Meara, J. G., & Da Costa, A. C. (2005). Use of SMS text messaging to improve outpatient attendance. The Medical Journal of Australia, 183(7), 366–368.

    Google Scholar 

  • Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2017). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers. https://doi.org/10.1007/s10796-017-9774-y published online on 8 June 2017.

  • Fitchett, J. R., MacPherson, P., & Corbett, E. L. (2016). Implementing the end TB strategy and the intersection with the sustainability development goals, 2016-2030. Transactions Royal Society for Tropical Medicine & Hygiene, 110(3), 145–147.

    Article  Google Scholar 

  • Foon, Y. S., & Fah, B. C. Y. (2011). Internet banking in Kuala Lumpur: An application of UTAUT model. International Journal of Business and Management, 6(4), 161–167.

    Google Scholar 

  • Gagnon, M.-P., Ngangue, P., Gagnon, J.-P., & Desmartis, M. (2016). m-Health adoption by healthcare professionals: a systematic review. Journal of American Medical Information Association, 23, 212–220.

    Article  Google Scholar 

  • Garg, S., & Mony, P. K. (2013). Electronic data capture for health surveys in developing countries: use of a mobile phone based application in Southern India. Indian Journal of Medical Informatics, 7(2), 84–93.

    Google Scholar 

  • Gefen, D., Rigdon, E. E., & Straub, D. (2011). Editor’s comments: an update and extension to SEM guidelines for administrative and social science research. MIS Quarterly, 35(2), A1–A7.

    Article  Google Scholar 

  • Goodhue, D.L., Lewis, W., Thompson, R. (2006). PLS, Small Sample Size, and Statistical Power in MIS Research. Proceedings of the 39th Hawaii International Conference on System Sciences.

  • Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Does PLS have advantages for small sample size or non-normal data? MIS Quarterly, 36(3), 981–1001.

    Google Scholar 

  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–151.

    Article  Google Scholar 

  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modelling in marketing research. Journal of the Academy of Marketing Science, 40, 414–433.

    Article  Google Scholar 

  • Hair Jr., J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modelling. Thousand Oaks: Sage Publications.

    Google Scholar 

  • Holden, R., & Karsh, B. Z. (2010). The Technology Acceptance Model: its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159–172.

    Article  Google Scholar 

  • Hu, P. J., Chau, P. Y. K., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the Technology Acceptance Model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16(2), 91–112.

    Article  Google Scholar 

  • Jarosawlski, S., & Pai, M. (2012). Why are inaccurate tuberculosis serological tests widely used in the Indian private healthcare sector? A root-cause analysis. Journal of Epidemial Global Health, 2, 39–50.

    Article  Google Scholar 

  • Johns, G. (2006). The essential impact of context on organizational behavior. Academy of Management Review, 31(2), 386–408.

    Article  Google Scholar 

  • Khechine, H., Lakhal, S., & Ndjambou, P. (2016). A meta-analysis of the UTAUT model: eleven years later. Canadian Journal of Administrative Sciences, 33, 138–152.

    Article  Google Scholar 

  • 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 of Medical Informatics, 78(6), 404–410.

    Article  Google Scholar 

  • Kim, S., Lee, K.-H., Hwang, H., & Yoo, S. (2016). Analysis of the factors influencing healthcare professionals’ adoption of mobile electronic medical record (EMR) using the unified theory of acceptance and use of technology (UTAUT) in a tertiary hospital. BMC Medical Informatcis and Decision Making, 16, 12. https://doi.org/10.1186/s12911-016-0249-8.

    Article  Google Scholar 

  • Kock, N., & Hadaya, P. (2016). Minimum sample size estimation in PLS-SEM: the inverse square root and gamma-exponential methods. Information Systems Journal. https://doi.org/10.1111/isj.12131.

  • Lapointe, L., & Rivard, S. (2006). Getting physicians to accept new information technology: insights from case studies. Canadian Medical Association Journal, 174(11), 1573–1578.

    Article  Google Scholar 

  • Lee, A. S., & Baskerville, R. L. (2003). Generalizing generalizability in information systems research. Information Systems Research, 14(3), 221–243.

    Article  Google Scholar 

  • Lewis, W., Agarwal, R., & Sambamurthy, V. (2003). Sources of influence on beliefs about information technology use: an empirical study of knowledge workers. MIS Quarterly, 27, 657–678.

    Article  Google Scholar 

  • Melas, C. D., Zampetakis, L. A., Dimopoulou, A., & Moustakis, V. (2011). Modeling the acceptance of clinical information systems among hospital medical staff: an extended TAM model. Journal of Biomed Informatics, 44(4), 553–564.

    Article  Google Scholar 

  • Morris, M. G., Venkatesh, V., & Ackerman, P. L. (2005). Gender and age in technology adoption and usage decisions: toward the emergence of a unisex work force. IEEE Transactions on Engineering Management, 52(1), 69–84.

    Article  Google Scholar 

  • Nunnaly, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw Hill.

    Google Scholar 

  • Nysveen, H., & Pedersen, P. E. (2016). Consumer adoption of RFID-enabled services. Applying an extended UTAUT model. Information Systems Frontiers, 18(2), 293–314.

    Article  Google Scholar 

  • Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. The Electronic Journal of Information Systems Evaluation, 14(1), 110–121.

    Google Scholar 

  • Pai, M., Yadav, P., & Anupindi, R. (2014). Tuberculosis control needs a complete and patient-centric solution. The Lancet Infectious Diseases, 2, e189–e190. https://doi.org/10.1016/s2214-109X(14)70198-6 Accessed 10 July 2014.

    Google Scholar 

  • Pai, M., Daftary, A., & Satyanarayana, S. (2016). TB control: challenges and opportunities for India. Transactions Royal Society for Tropical Medicine & Hygiene, 110(3), 158–160.

    Article  Google Scholar 

  • Peng, D. X., & Lai, F. (2012). Using partial least squares in operations management research: a practical guideline and summary of past research. Journal of Operations Management, 30(6), 467–480.

    Article  Google Scholar 

  • Reinartz, W., Haenlein, M., & Henseler, J. (2009). An Empirical Comparison of the Efficacy of Covariance-Based and Variance-Based SEM. International Journal of Research in Marketing, 26(4), 332–344.

    Article  Google Scholar 

  • Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor’s comments: a critical look at the use of PLS-SEM in MIS Quarterly. MIS Quarterly, 36(1), iii–xiv.

    Google Scholar 

  • Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS3. Boenningstedt: SmartPLS GmgH http://www.smartpls.com.

    Google Scholar 

  • Rogers, E. M. (1995). Diffusion of innovations. New York: The Free Press.

    Google Scholar 

  • Sarstedt, M., Henseler, J., & Ringle, C. M. (2011). Multigroup analysis in partial least squares (PLS) path modelling: alternative methods and empirical results. In M. Sarstedt, M. Schwaiger, & C. R. Taylor (Eds.), Measurement and research methods in international marketing (advances in international marketing, volume 22) (pp. 195–218). Bradford: Emerald.

    Chapter  Google Scholar 

  • Seeman, E., & Gibson, S. (2009). Predicting acceptance of electronic medical records: is the Technology Acceptance Model enough? SAM Advanced Management Journal, 74(4), 21–26.

    Google Scholar 

  • Seethamraju, R. (2015). Adoption of Software as a Service (SaaS) Enterprise Resource Planning (ERP) systems in Small and Medium Sized Enterprises (SMEs). Information Systems Frontiers, 17(3), 475–492.

    Article  Google Scholar 

  • Soper, D.S. (2017). A-priori sample size calculator for Structural Equation Models [Software]. Available from http://www.danielsoper.com/statcalc. Acessed 6 Feb 2017.

  • Sun, J., & Qu, Z. (2015). Understanding health information technology adoption: A synthesis of literature from an activity perspective. Information Systems Frontiers, 17(5), 1177–1190.

    Article  Google Scholar 

  • Tanimura, T., Jaramillo, E., Weil, D., Raviglione, M., & Lonnroth, K. (2014). Financial burden for tuberculosis patients in low and middle income countries: a systematic review. European Respiratory Journal. https://doi.org/10.1183/09031936.00193413.

  • Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: toward a conceptual model of utilization. MIS Quarterly, 15(1), 124–143.

    Article  Google Scholar 

  • Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington: Lexington Books.

    Google Scholar 

  • Udwadia, Z. F. (2012). MDR, XDR, TDR tuberculosis: ominous progression. Thorax, 67, 286–288.

    Article  Google Scholar 

  • Udwadia, Z. F., Pinto, L. M., & Uplekar, M. W. (2010). Tuberculosis management by private practitioners in Mumbai, India: has anything changed in two decades? PloS One, 5, e12023.

    Article  Google Scholar 

  • van Gemert-Pijnen, J. E., Nijland, N., van Limburg, M., Ossebaard, H. C., Kelders, S. M., Eysenbach, G., et al. (2011). A holistic framework to improve the uptake and impact of eHealth technologies. Journal of Medical Internet Research, 13(4), e111.

    Article  Google Scholar 

  • Vandan, N., Ali, M., Prasad, R., & Kuroiwa, C. (2009). Assessment of doctors’ knowledge regarding tuberculosis management in Lucknow, India: a public–private sector comparison. Public Health, 123, 484–489.

    Article  Google Scholar 

  • Venkatesh, V. (2010). Technology acceptance, summary of technology acceptance models. http://www.vvenkatesh.com/IT/organizations/Theoretical_Models.asp. Accessed 10 Nov 2015.

  • Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(3), 273–315.

    Article  Google Scholar 

  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186–204.

    Article  Google Scholar 

  • Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behaviour. MIS Quarterly, 24(1), 115–139.

    Article  Google Scholar 

  • Venkatesh, V., & Zhang, X. (2010). Culture and technology adoption: theory and empirical test of the Unified Theory of Acceptance and Use of Technology (UTAUT) in the US vs China. Journal of Global Information Technology Management, 13(1), 5–27.

    Article  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.

    Article  Google Scholar 

  • Venkatesh, V., Zhang, X., & Sykes, T. A. (2011a). Doctors do too little technology: a longitudinal field study of an electronic healthcare. Journal Information Systems Research, 22(3), 523–546.

    Article  Google Scholar 

  • Venkatesh, V., Sykes, T. A., & Zhang, X. (2011b). Just what the doctor ordered: a revised UTAUT for EMR system adoption and use by doctors. Proceedings of the 44 th Hawaii International Conference on System Sciences (pp. 1–10).

  • Venkatesh, V., Thong, J. Y. L., & 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.

    Google Scholar 

  • Wang, Y.-S., & Shih, Y.-W. (2009). Why do people use information Kiosks? A validation of the Unified Theory of Acceptance and Use of Technology. Government Information Quarterly, 26, 158–165.

    Article  Google Scholar 

  • Warshaw, P. R. (1980). A new model for predicting behavioural intentions: an alternative to Fishbein. Journal of Marketing Research, 17(2), 153–172.

    Article  Google Scholar 

  • WHO. (2007). Tuberculosis surveillance and monitoring – Report of an intercountry consultation. World Health Organization (WHO): New Delhi.

    Google Scholar 

  • WHO (2011). The global plan to stop TB. www.stoptb.org/global/plan/. Accessed 6 Sept 2016.

  • WHO. (2014). Global Tuberculosis Report 2013. Geneva: World Health Organization (WHO).

    Google Scholar 

  • WHO (2015). Global tuberculosis report 2015, 20th edition. http://www.who.int/tb/publications/global_report/en. Accessed 8 Sept 2016.

  • Wong, K. K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24, Technical Note 1, (pp. 1–32). Downloaded from http://marketing-bulletin.massey.ac.nz. Accessed 14 Feb 2017.

  • Zhang, N., Guo, X., & Chen, G. (2011). Why adoption and use behaviour of IT/IS cannot last? – two studies in China. Information Systems Frontiers, 13(2), 381–395.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The University of Sydney Business School, Sydney, NSW, 2006, Australia

    Ravi Seethamraju

  2. Indian Institute of Management Bangalore, Bangalore, India

    Krishna Sundar Diatha & Shashank Garg

Authors
  1. Ravi Seethamraju
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Krishna Sundar Diatha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shashank Garg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ravi Seethamraju.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Seethamraju, R., Diatha, K.S. & Garg, S. Intention to Use a Mobile-Based Information Technology Solution for Tuberculosis Treatment Monitoring – Applying a UTAUT Model. Inf Syst Front 20, 163–181 (2018). https://doi.org/10.1007/s10796-017-9801-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-017-9801-z

Keywords

Search

Navigation