Factors Influencing Acceptance of E-health: an Interpretive Structural Modeling

Document Type : Research Paper

Authors

1 Assistant Prof., Department of Information Technology Management, Faculty of Management and Strategic Planning, Imam Hossein University (IHU), Tehran, Iran.

2 Ph.D. Candidate, Department of Systems Management, Faculty of Management and Strategic Planning, Imam Hossein University (IHU), Tehran, Iran.

Abstract

The aim of this study is to analyze the factors affecting the acceptance of electronic health on the basis of the theory of planned behavior. E-health is a growing field of health communication that entails using medical informatics, public health, and trades. As a result, E-health facilitates the provision of health information and services through the internet and related technologies. In this regard, this study aims to explain the acceptance of e-health by its beneficiaries such as physicians, patients, and healthcare managers. The results have shown that the most important factors affecting the acceptance of e-health are:  1. Organizational related factors of e-health services; 2. Human-related factors of acceptors; 3. Environment-related factors; 4. Factors associated with financial sources and expenditures; 5. Technical and infrastructural factors. Taking advantage of interpretive structural modeling, we demonstrated these factors and determined the level of their reciprocal relations.

References

Agarwal, A., Shankar, R., Tiwari, M.K. (2006). Modeling agility of supply chain. Industrial Marketing Management, 36(4), 443- 457.
Agi, M.A.N., Nishant, R. (2017). Understanding influential factors on implementing green supply chain. Journal of Environmental Management, 188, 351– 363.
Ajzen, I., Fishbein, M. (1980). Understanding attitudes and predicting social Behaviour. Englewood Cliffs: Prentice-Hall.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2): 179-211.
Attri, R., Dev, N., Sharma, V. (2013). Interpretive Structural Modelling (ISM) approach: An Overview. Research Journal of Management Sciences, 2(2): 3- 8.
Bastani, P., Abolhasani, N., Shaarbafchizadeh, N. (2014). Electronic Health in Perspective of Healthcare Managers: A Qualitative Study in South of Iran. Iranian Journal of Public Health, 43(6): 809- 820.
Borade, A. B., Bansod, S. V. (2012). Interpretive structural modeling-based framework for VMI adoption in Indian industries. International Journal of Advanced Manufacturing Technology, 58(9- 12): 1227– 1242.
Canada Health Infoway. (2014- 2015). Summary Corporate Plan- Improving Health Care through Innovation. Retrieved from: https://www.infoway-inforoute.ca/en/ .
Chang, I. C., Hwang, H. G., Hung, M. C., Lin, M. H. and Yen, D. C. (2007). Factors affecting the adoption of electronic signature: Executives’ perspective of hospital information department. Decision Support Systems, 44(1): 350– 359.
Chang, M. Y., Pang, C., Tarn, J. M., Liu, T. S., Yen, D. C. (2015). Exploring user acceptance of an e-hospital service: An empirical study in Taiwan. Computer Standards & Interfaces, 38: 35– 43.
Chopra, V., McMahon, L. F., Hitech, J. R. (2011). Electronic health records, and Facebook: a health information trifecta. American Journal of Medicine, 124(6): 477- 479.
Chong, A.Y.L., Chan, F.T.S. (2012). Structural equation modeling for multistage analysis on Radio Frequency Identification (RFID) diffusion in the health care industry. Expert Systems with Applications, 39(10): 8645– 8654.
Cooper, D.R., Schindler, P.S. (2006). Business Research Methods. New York: McGraw- Hill.
Dale, L. P., Whittaker, R., Jiang, Y., Stewart, R., Rolleston, A., Maddison, R. (2015). Text message and Internet support for coronary heart disease self-management: Results from the text4heart randomized controlled trial. Journal of Medical Internet Research. 17(10): 1- 12.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly. 13(3): 318- 340.
Faber, S., Geenhuizen, M. V., Reuver, M. D. (2017). eHealth adoption factors in medical hospitals: A focus on the Netherlands Sander. International Journal of Medical Informatics, 100: 77– 89.
Faisal, M. N., Banwet, D. K., Shankar, R. (2006). Supply chain risk mitigation & modeling the enablers. Business Process Management Journal, 12(4): 535- 552.
Fishbein, M., Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Boston: Addison- Wesley.
Fisher, E. (2011). What practitioners consider to be the skills and behaviors of an effective people project manager. International Journal of Project Management, 29: 994– 1002.
Freitas, H., Oliveira, M., Jenkins, M., Popjoy, O. (1998). The Focus Group, a qualitative research method. ISRC, Merrick School of Business, University of Baltimore.
Gholamhosseini, L., Ayatollahi, H. (2016), The design and application of an e-health readiness assessment tool. Health Information Management Journal, 46(1): 32- 41.
Gücin, N. Ö., Berk, Ö. S. (2015). Technology Acceptance in Health Care: An Integrative Review of Predictive Factors and Intervention Programs. Procedia- Social and Behavioral Sciences, 195: 1698- 1704.
Hilty, D. (2016). The impact of E-Mental Health on prevention and early detection of illness. European Psychiatry, 33: S28.
HIMSS Analytics. (2017). HIMSS Analytics– EMRAM. available at: https://app.himssanalytics.org/emram/ emram.aspx.
Hsia, T., Chiang, A. J., Wu, J. H., Teng, N. H., Rubin, A. D. (2019). What Drives E-Health Usage? Integrated Institutional Forces and Top Management Perspectives. Computers in Human Behavior, 97: 260- 270.
Hsieh, P. J. (2015). Healthcare professionals” use of health clouds: Integrating technology acceptance and status quo bias perspectives. International Journal of Medical Informatics, 84(7): 512– 523.
Huang, S. M., Hung, Y. C., Yen, D. C. (2005). A study on decision factors in adopting an online stock trading system by brokers in Taiwan. Decision Support Systems, 40: 315– 328.
Oh, H., Rizo, C., Enkin, M., Jadad, A., Powell, J., Pagliari, C. (2005). What is eHealth (3): a systematic review of published definitions. Journal of Medical Internet Research, 7(1): 1- 11.
Salge, T. O., Kohli, R., Barrett, M. (2015). Investing in information systems: On the behavioral and institutional search mechanisms underpinning hospital’s IS investment decisions. MIS Quarterly, 39(1): 61- 89.
Schmidt, Isabel. (2015). The adoption of e-health services: Comprehensive analysis of the adoption setting from the user׳s perspective. Health Policy and Technology, 4(3): 286– 293.
Schweitzer, J., Synowiec, C. (2012). The Economics of eHealth and mHealth. Journal of Health Communication, 17(1): 73- 81.
Thakkar, J., Deshmukh, S.G., Gupta, A.D., Shankar, R. (2007). Development of a balanced scorecard An integrated approach of Interpretive Structural Modeling (ISM) and Analytic Network Process (ANP). International Journal of Productivity and Performance Management, 56(1): 25- 59.
Kaiser, F. G. (2006). A moral extension of the theory of planned behaviour: Norms and anticipated feelings of regret in conservationism. Personality and Individual Differences, 41(1): 71– 81.
Kelly, L., Ziebland, S., Jenkinson, C. (2015). Measuring the effects of online health information: Scale validation for the e-Health Impact Questionnaire. Patient Education and Counseling, 98(11): 1418– 1424.
Kummervold, P.E., Chronaki, C.E., Lausen, B., Prokosch, H.U., Rasmussen, J., Santana, S., Staniszewski, A., Wangberg, S.C. (2008). E Health trends in Europe 2005- 2007: a population-based survey. Journal of Medical Internet Research, 10(4): 42.
Kuo, A. M. H. (2011). Opportunities and challenges of cloud computing to improve health care services. Journal of Medical Internet Research, 13(3): 1- 34.
Leung, L., Chen, C. (2019), “E-health/m-health adoption and lifestyle improvements: Exploring the roles of technology readiness, the expectation-confirmation model, and health-related information activities. Telecommunications Policy, 43(6): 563- 575.
Lin, A., Chen, N. C. (2012). Cloud computing as an innovation: Perception, attitude, and adoption. International Journal of Information Management, 32(6): 533– 540.
McEachan, R. R. C., Conner, M., Taylor, N. J., Lawton, R.J. (2011). Prospective prediction of health-related behaviours with the theory of planned behaviour; A meta-analysis. Health Psychology Review, 5(2): 97-144.
Morgan, D. L. (1988). Focus groups the qualitative research. Beverly Hills: SAGE Publications.
NPS Survey. (2014). National Physician Survey”, College of Family Physicians of Canada, Canadian Medical Association, Royal College of Physicians and Surgeons of Canada, available at: http://nationalphysiciansurvey.ca/surveys/2014-survey.
Razmak, J., B´elanger, C.H., Farhan, W. (2018). Development of a techno- humanist model for e-health adoption of innovative technology. International Journal of Medical Informatics, 120: 62- 76.
Reis, S., Visser, A., Frankel, R. (2013). Health information and communication technology in healthcare communication: the good, the bad, and the transformative. Patient Education and Counseling, 93(3): 359- 362.
Rogers, E.M. (2003). Diffusion of innovations (3rd edition).New York: Free Press.
Roham, M., Gabrielyan, A., Archer, N.P. (2012). Predicting the impact of hospital health information technology adoption on patient satisfaction. Artificial Intelligence in Medicine, 56(2): 123– 135. 
Sarkis, J., Gonzalez-Torre, P., Adenso-Diaz, B. (2010). Stakeholder pressure and the adoption of environmental practices: The mediating effect of training. Journal of Operations Management, 28(2): 163- 176.
Simpson, A. T. (2013). A Brief History of NASA’s Contributions to Telemedicine. available at: https://www.nasa.gov/content/a-brief-history-of-nasa-s-contributions-to-telemedicine .
Sultan, N. (2014). Making use of cloud computing for healthcare provision: Opportunities and challenges. International Journal of Information Management, 34(2): 177- 184.
Sultan, N., and Sultan, Z. (2012). The application of utility ICT in healthcare management and life science research: A new market for a disruptive innovation? EURAM 12 conference. June 7th 2012. Rotterdam, Netherlands.
Swendeman, D., Comulada, W.C., Ramanathan, N., Lazar, M., Estrin, D. (2014), Reliability and validity of daily self-monitoring by smartphone application for health-related quality-of-life, antiretroviral adherence, substance use, and sexual behaviors among people living with HIV. Aids and Behavior, 18(12): 1- 11.
Thong, J.Y.L. (1999). An integrated model of information systems adoption in small businesses. Journal of Management Information Systems, 15(4):187– 214.
World Health Organization Regional Office for the Eastern Mediterranean. (2008). What is e-health? available at: http://www.emro.who.int/his/e-Health/AboutE-Health.htm .
Yazdanpanah, M., Forouzani, M. (2015). Application of the Theory of Planned Behaviour to predict Iranian students’ intention to purchase organic food. Journal of Cleaner Production, 107: 342- 352.
Yusof, M.M., Papazafeiropoulou, A., Paul, R.J. and Stergioulas, L.K. (2008). Investigating evaluation frameworks for health information systems. International Journal of Medical Informatics, 77(6): 377- 385.
Zhu, K., Kraemer, K., Xu, S. (2006). The process of innovation assimilation by firms indifferent countries: a technology diffusion perspective on E-business. Management Science, 52(10): 1557– 1576.
Journal of Information Technology Management
Volume 10, Issue 3
2018
Pages 106-126

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