Health Seekers’ Acceptance and Adoption Determinants of Telemedicine in Emerging Economies


  • Khondker Mohammad Zobair Griffith Business School, Griffith University, Brisbane, Australia
  • Louis Sanzogni Griffith University, Brisbane, Australia
  • Luke Houghton Griffith University, Brisbane, Australia
  • Kuldeep Sandhu Griffith University, Brisbane, Australia
  • Md Jahirul Islam Griffith University, Brisbane, Australia



Acceptance, ICT, PLS-SEM, Rural and remote areas, Technology Acceptance Model, Telemedicine


This study investigates health seekers’ acceptance and adoption determinants of telemedicine services in a rural public hospital setting in an emerging economy using an adapted, extended Technology Acceptance Model. The present study pursued synthesising a plethora of existing literature and contextualised the significance of seven broad categories of potential determinants that significantly affect patients’ acceptance and adoption intentions: perceived usefulness, perceived ease of use, self-efficacy, service quality, privacy and data security, social influence, and facilitating conditions. The partial least square structural equation modeling technique was employed to test the conceptual model and research hypotheses. A cross-sectional survey was administered among 500 telemedicine users in randomly selected rural and remote areas of Bangladesh. Excluding self-efficacy and ease of use, five determinants expressively contributed to patients’ acceptance of telemedicine adoption, explaining 65% of the variance (R2) in behavioural Intention. The empirical findings have the quality of rigour obtained from rich data sets in health informatics and can contribute to build telemedicine into an institutionalised health infrastructure in Bangladesh and similar settings. Pertinent implications, limitations and future research directions were recommended to secure the long-term sustainability of telemedicine healthcare projects.


Agarwal, R., Sambamurthy, V., & Stair, R. M. (2000). The evolving relationship between general and specific computer self-efficacy—An empirical assessment. Information Systems Research, 11(4), 418-430.

Aggelidis, V. P., & Chatzoglou, P. D. (2009). Using a modified technology acceptance model in hospitals. International Journal of Medical Mnformatics, 78(2), 115-126.

Ahmed, T., Bloom, G., Iqbal, M., Lucas, H., Rasheed, S., Waldman, L., . . . Bhuiya, A. (2014). E-health and M-Health in Bangladesh: Opportunities and Challenges. Retrieved from,

Ajibade, P. (2018). Technology acceptance model limitations and criticisms: Exploring the practical applications and use in technology-related studies, mixed-method, and qualitative researches. Library Philosophy & Practice, 1941.

Akter, S., D’Ambra, J., & Ray, P. (2010). Service quality of mHealth platforms: development and validation of a hierarchical model using PLS. Electronic Markets, 20(3-4), 209-227.

Alam, M. Z., Hoque, M. R., Hu, W., & Barua, Z. (2020). Factors influencing the adoption of mHealth services in a developing country: A patient-centric study. International Journal of Information Management, 50, 128-143.

Ali, I., Ali, M., Badghish, S., & Baazeem, T. A. S. (2018). Examining the role of childhood experiences in developing altruistic and knowledge sharing behaviors among children in their later life: A partial least squares (PLS) path modeling approach. Sustainability, 10(2), 292.

Alshammari, S. H., & Rosli, M. S. (2020). A Review of Technology Acceptance Models and Theories. Innovative Teaching and Learning Journal (ITLJ), 4(2), 12-22.

Anderberg, P., Eivazzadeh, S., & Berglund, J. S. (2019). A Novel Instrument for Measuring Older People’s Attitudes Toward Technology (TechPH): Development and Validation. Journal of Medical Internet research, 21(5), e13951. doi: 10.2196/13951

Astrachan, C. B., Patel, V. K., & Wanzenried, G. (2014). A comparative study of CB-SEM and PLS-SEM for theory development in family firm research. Journal of Family Business Strategy, 5(1), 116-128.

Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.

Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, New Jersey, Prentice Hall, 1986.

Bandura, A., O'Leary, A., Taylor, C. B., Gauthier, J., & Gossard, D. (1987). Perceived Self-Efficacy and Pain Control: Opioid and Nonopioid Mechanisms. Journal of Personality and Social Psychology, 53(3), 563-571. doi:10.1037/0022-3514.53.3.563.

Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, 57(2), 103168.

Bettiga, D., Lamberti, L., & Lettieri, E. (2019). Individuals’ adoption of smart technologies for preventive health care: a structural equation modeling approach. Health Care Management Science, 23(2), 203-214. doi:10.1007/s10729-019-09468-2

Bhattacherjee, A., & Hikmet, N. (2007). Physicians' resistance toward healthcare information technology: a theoretical model and empirical test. European Journal of Information Systems, 16(6), 725-737.

Boscarino, J. A. (1992). The public's perception of quality hospitals II: Implications for patient surveys. Journal of Healthcare Management, 37(1), 13.

Bros, J. S., Poulet, C., Arnol, N., Deschaux, C., Gandit, M., & Charavel, M. (2018). Acceptance of Telemonitoring Among Patients with Obstructive Sleep Apnea Syndrome: How is the Perceived Interest by and for Patients? Telemedicine and e-Health, 24(5), 351-359.

Chang, H. (2015). Evaluation framework for telemedicine using the logical framework approach and a fishbone diagram. Healthcare Informatics Research, 21(4), 230-238.

Chang, Y.-Z., Ko, C.-Y., Hsiao, C.-J., Chen, R.-J., Yu, C.-W., Cheng, Y.-W., . . . Chao, C.-M. (2015). Understanding the determinants of implementing telehealth systems: a combined model of the theory of planned behavior and the technology acceptance model. Journal of Applied Sciences, 15(2), 277.

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

Chellappa, R. K., & Sin, R. G. (2005). Personalization versus privacy: An empirical examination of the online consumer’s dilemma. Information Technology and Management, 6(2-3), 181-202.

Chen, R.-F., & Hsiao, J.-L. (2012). An empirical study of physicians' acceptance of hospital information systems in Taiwan. Telemedicine and e-Health, 18(2), 120-125.

Cheng, T. L., Savageau, J. A., Sattler, A. L., & DeWitt, T. G. (1993). Confidentiality in health care: a survey of knowledge, perceptions, and attitudes among high school students. JAMA, 269(11), 1404-1407.

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.

Cimperman, M., Brenčič, M. M., & Trkman, P. (2016). Analyzing older users’ home telehealth services acceptance behavior—applying an Extended UTAUT model. International Journal of Medical Informatics, 90, 22-31.

Cresswell, J. W., & Clark, V. L. P. (2011). Designing and conducting mixed methods resaerch. (2nd edition). Thousand Oaks, California, SAGE, 2011.

Cronin Jr, J. J., Brady, M. K., & Hult, G. T. M. (2000). Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. Journal of Retailing, 76(2), 193-218.

Culnan, M. J., & Armstrong, P. K. (1999). Information Privacy Concerns, Procedural Fairness, and Impersonal Trust: An Empirical Investigation. Organization Science, 10(1), 104-115. doi:10.1287/orsc.10.1.104.

Darkwa, E. K., Newman, M. S., Kawkab, M., & Chowdhury, M. E. (2015). A qualitative study of factors influencing retention of doctors and nurses at rural healthcare facilities in Bangladesh. BMC Health Services Research, 15(1), 344. doi:10.1186/s12913-015-1012-z.

Davis, L.E., Harnar, J., LaChey-Barbee, L. A., Pirio Richardson, S., Fraser, A., & King, M. K. (2019). Using teleneurology to deliver chronic neurologic care to rural veterans: analysis of the first 1,100 patient visits. Telemedicine and e-Health, 25(4), 274-278.

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

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), 982-1003. doi:10.1287/mnsc.35.8.982.

Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9-30.

Dijkstra, T. K., & Henseler, J. (2015). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis, 81, 10-23.

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.

Dutot, V., Bergeron, F., Rozhkova, K., & Moreau, N. (2019). Factors Affecting the Adoption of Connected Objects in e-Health: A Mixed Methods Approach. Systèmes d'Information et Management, 23(4), 31-66.

Dwivedi, Y. K., Shareef, M. A., Simintiras, A. C., Lal, B., & Weerakkody, V. (2016). A generalised adoption model for services: A cross-country comparison of mobile health (m-health). Government Information Quarterly, 33(1), 174-187.

Esmaeilzadeh, P. (2019). The effects of public concern for information privacy on the adoption of Health Information Exchanges (HIEs) by healthcare entities. Health Communication, 34(10), 1202-1211.

Evans, P. R. (2015). An exploration of the perceptions of a US Midwest acute care hospital organization regarding the adoption and acceptance of telemedicine. (Doctoral dissertaion, Capella University), ProQuest Dissertations Publishing, 2015. 3700252.

Ferrer-Roca, O., Garcia-Nogales, A., & Pelaez, C. (2010). The impact of telemedicine on quality of life in rural areas: the Extremadura model of specialized care delivery. Telemedicine and e-Health, 16(2), 233-243.

Gefen, D., Straub, D., & Boudreau, M.-C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(1), 7.

Greenberg, A. S., Steinway, C., Wu, K., Thomas, B., Chuo, J., DiGiovine, M., & Jan, S. J. J. o. A. H. (2019). 80. Role of Telemedicine in the Transition From Pediatric To Adult Care. 64(2), S43.

Hair, J., Hair, J., Hollingsworth, C. L., Hollingsworth, C. L., Randolph, A. B., Randolph, A. B., . . . Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442-458.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1-2), 1-12.

Hair Jr, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101-110.

Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM): Los Angeles, California, Sage Publications.

Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM): Los Angeles, California, Sage Publications.

Hale, T. M., & Kvedar, J. C. (2014). Privacy and security concerns in telehealth. Virtual Mentor, 16(12), 981.

Hall, J. L., & McGraw, D. (2014). For telehealth to succeed, privacy and security risks must be identified and addressed. Health Qffairs, 33(2), 216-221.

Henseler, J., Hubona, G., & Ray, P. A. (2017). Partial least squares path modeling: Updated guidelines. In Partial Least Squares Path Modeling (pp. 19-39): Cham, Switzerland Springer. 10.1007/978-3-319-64069-3_2

Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing (pp. 277-319): Bingley, Emerald Group Publishing Limited. doi: 10.1108/S1474-7979(2009)0000020014

Hill, M. H., & Doddato, T. (2002). Relationships among patient satisfaction, intent to return, and intent to recommend services provided by an academic nursing center. Journal of Cultural Diversity, 9(4), 108-112.

Holden, R. J., & Karsh, B.-T. (2010). The technology acceptance model: its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159-172.

Hoque, Bao, Y., & Sorwar, G. (2017). Investigating factors influencing the adoption of e-Health in developing countries: A patient’s perspective. Informatics for Health and Social Care, 42(1), 1-17.

Hoque, & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics, 101, 75-84. doi:10.1016/j.ijmedinf.2017.02.002.

Hossain, A., Quaresma, R., & Rahman, H. (2019). Investigating factors influencing the physicians’ adoption of electronic health record (EHR) in healthcare system of Bangladesh: An empirical study. International Journal of Information Management, 44, 76-87.

Hsia, T.-L., Chiang, A.-J., Wu, J.-H., Teng, N. N., & Rubin, A. D. (2019). What Drives E-Health Usage? Integrated Institutional Forces and Top Management Perspectives. Computers in Human Behavior.

Hu, P. J., Chau, P. Y., 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.

Hur, I., Cousins, K. C., & Stahl, B. C. (2019). A critical perspective of engagement in online health communities. European Journal of Information Systems, 28(5), 523-548.

Islam, F., Rahman, A., Halim, A., Eriksson, C., Rahman, F., & Dalal, K. (2015). Perceptions of health care providers and patients on quality of care in maternal and neonatal health in fourteen Bangladesh government healthcare facilities: a mixed-method study. BMC Health Services Research, 15(1), 1-9. doi: 10.1186/s12913-015-0918-9

Ivatury, G., Moore, J., & Bloch, A. (2009). A doctor in your pocket: health hotlines in developing countries. Innovations: Technology, Governance, Globalization, 4(1), 119-153.

Jansen-Kosterink, S., Dekker-van Weering, M., & van Velsen, L. (2019). Patient acceptance of a telemedicine service for rehabilitation care: A focus group study. International Journal of Medical Informatics, 125, 22-29.

Jewer, J. (2018). Patients’ intention to use online postings of ED wait times: A modified UTAUT model. International Journal of Medical Informatics 112, 34-39.

Johnston, A. C., & Warkentin, M. (2010). Fear appeals and information security behaviors: an empirical study. MIS Quarterly, 34(3), 549-566.

Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212.

Karahanna, E., & Straub, D. W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information & Management, 35(4), 237-250.

Kettinger, W. J., & Lee, C. C. (1997). Pragmatic Perspectives on the Measurement of Information Systems Service Quality. MIS Quarterly, 21(2), 223-240.

Kim, & Park, H.-A. (2012). Development of a health information technology acceptance model using consumers’ health behavior intention. Journal of Medical Internet Research, 14(5), e133. doi: 10.2196/jmir.2143

Kim, K.-H., Kim, K.-J., Lee, D.-H., & Kim, M.-G. (2019). Identification of critical quality dimensions for continuance intention in mHealth services: Case study of onecare service. International Journal of Information Management, 46, 187-197.

Koceska, N., Komadina, R., Simjanoska, M., Koteska, B., Strahovnik, A., Jošt, A., . . . Trontelj, J. (2019). Mobile wireless monitoring system for prehospital emergency care. European Journal of Trauma and Emergency Surgery, 46(6), 1301-1308.

Kock, N. (2018). Should bootstrapping be used in PLS-SEM? Toward stable P-Value calculation methods. Journal of Applied Structural Equation Modeling, 2(1), 1-12.

Kuruvilla, S., Mays, N., Pleasant, A., & Walt, G. (2006). Describing the impact of health research: a Research Impact Framework. BMC Health Services Research, 6(1), 1-18.

Lankton, N. K., & McKnight, H. D. (2012). Examining two expectation disconfirmation theory models: assimilation and asymmetry effects. Journal of the Association for Information Systems, 13(2), 1. DOI: 10.17705/1jais.00285

Lankton, N. K., & Wilson, E. V. (2007). Factors influencing expectations of e-health services within a direct-effects model of user satisfaction. E-Service Journal, 5(2), 85-112.

Lending, D., & Dillon, T. W. (2007). The effects of confidentiality on nursing self-efficacy with information systems. International Journal of Healthcare Information Systems and Informatics (IJHISI), 2(3), 49-64.

LeRouge, C., & Garfield, M. J. (2013). Crossing the telemedicine chasm: have the US barriers to widespread adoption of telemedicine been significantly reduced? International Journal of Environmental Research and Public Health, 10(12), 6472-6484.

LeRouge, C. M., Gupta, M., Corpart, G., & Arrieta, A. (2019). Health System Approaches Are Needed to Expand Telemedicine Use Across Nine Latin American Nations. Health Affairs, 38(2), 212-221.

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.

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(4), 657-678.

Lin, H.-C., & Chang, C.-M. (2018). What motivates health information exchange in social media? The roles of the social cognitive theory and perceived interactivity. Information & Management, 55(6), 771-780.

Lu, J., Yao, J. E., & Yu, C.-S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245-268.

Macedo, I. M. (2017). Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Computers in Human Behavior, 75, 935-948.

Maillet, É., Mathieu, L., & Sicotte, C. (2015). Modeling factors explaining the acceptance, actual use and satisfaction of nurses using an Electronic Patient Record in acute care settings: An extension of the UTAUT. International Journal of Medical Informatics, 84(1), 36-47.

Muthupoltotage, U. P., & Gardner, L. (2018). Analysing the Relationships Between Digital Literacy and Self-Regulated Learning of Undergraduates—A Preliminary Investigation. In Advances in Information Systems Development (pp. 1-16): Cham, Switzerland, Springer.

Or, C. K., Karsh, B.-T., Severtson, D. J., Burke, L. J., Brown, R. L., & Brennan, P. F. (2011). Factors affecting home care patients' acceptance of a web-based interactive self-management technology. Journal of the American Medical Informatics Association, 18(1), 51-59.

Otter, V., & Beer, L. (2021). Alley cropping systems as Ecological Focus Areas: A PLS-analysis of German farmers’ acceptance behaviour. Journal of Cleaner Production, 280, 123702.

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. The Journal of Marketing, 49(4), 41-50.

Ramkumar, M., Schoenherr, T., Wagner, S. M., & Jenamani, M. (2019). Q-TAM: A quality technology acceptance model for predicting organizational buyers’ continuance intentions for e-procurement services. International Journal of Production Economics, 216, 333-348.

Rho, M. J., young Choi, I., & Lee, J. (2014). Predictive factors of telemedicine service acceptance and behavioral intention of physicians. International Journal of Medical Informatics, 83(8), 559-571.

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.

Roldán, J. L., & Sánchez-Franco, M. J. (2012). Variance-based structural equation modeling: guidelines for using partial least squares. Research methodologies, innovations and philosophies in software systems engineering and information systems, (pp.193-221). IGI Global. doi: 10.4018/978-1-4666-0179-6.ch010

Rosenstock, I. M., Strecher, V. J., & Becker, M. H. (1988). Social learning theory and the health belief model. Health Education Quarterly, 15(2), 175-183.

Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018). Partial least squares path modeling using ordinal categorical indicators. Quality & Quantity, 52(1), 9-35.

Schubring, S., Lorscheid, I., Meyer, M., & Ringle, C. M. (2016). The PLS agent: Predictive modeling with PLS-SEM and agent-based simulation. Journal of Business Research, 69(10), 4604-4612.

Shankar, V., Smith, A. K., & Rangaswamy, A. (2003). Customer satisfaction and loyalty in online and offline environments. International Journal of Research in Marketing, 20(2), 153-175.

Taylor, J., Coates, E., Wessels, B., Mountain, G., & Hawley, M. S. (2015). Implementing solutions to improve and expand telehealth adoption: participatory action research in four community healthcare settings. BMC Health Services Research, 15(1), 529.

Tsai, C.-H. (2014). Integrating social capital theory, social cognitive theory, and the technology acceptance model to explore a behavioral model of telehealth systems. International Journal of Environmental Research and Public Health, 11(5), 4905-4925.

Venkatesh, V. (2000a). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365.

Venkatesh, V. (2000b). Determinants of perceived ease of use: Integrating perceived behavioral control, computer anxiety and enjoyment into the technology acceptance model. Information Systems Research, 11(4), 342-365.

Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315.

Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481.

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

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.

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

Vries, H. D., Backbier, E., Kok, G., & Dijkstra, M. (1995). The Impact of Social Influences in the Context of Attitude, Self‐Efficacy, Intention, and Previous Behavior as Predictors of Smoking Onset. Journal of Applied Social Psychology, 25(3), 237-257.

Whitten, P., Holtz, B., & Nguyen, L. (2010). Keys to a successful and sustainable telemedicine program. International Journal of Technology Assessment in Health Care, 26(2), 211-216. doi:

Wilkowska, W., & Ziefle, M. (2011). Perception of privacy and security for acceptance of E-health technologies: Exploratory analysis for diverse user groups. In 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshop (pp. 593-600). IEEE. doi: 10.4108/icst.pervasivehealth.2011.246027

Williams, K., & Bond, M. (2002). The roles of self-efficacy, outcome expectancies and social support in the self-care behaviours of diabetics. Psychology, Health & Medicine, 7(2), 127-141.

Wilson, B. (2010). Using PLS to investigate interaction effects between higher order branding constructs. In Handbook of Partial Least Squares (pp. 621-652): Berlin, Heidelberg, Germany, Springer. doi: 10.1007/978-3-540-32827-8_28

Woo, K., & Dowding, D. (2018). Factors Affecting the Acceptance of Telehealth Services by Heart Failure Patients: An Integrative Review. Telemedicine and e-Health, 24(4), 292-300.

Wu, J.-H., Shen, W.-S., Lin, L.-M., Greenes, R. A., & Bates, D. W. (2008). Testing the technology acceptance model for evaluating healthcare professionals' intention to use an adverse event reporting system. International Journal for Quality in Health Care, 20(2), 123-129.

Xu, Z. (2019). An empirical study of patients' privacy concerns for health informatics as a service. Technological Forecasting and Social Change, 143, 297-306.

Yarbrough, A. K., & Smith, T. B. (2007). Technology acceptance among physicians: a new take on TAM. Medical Care Research and Review, 64(6), 650-672.

Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31-46.

Zhang, X., Han, X., Dang, Y., Meng, F., Guo, X., & Lin, J. (2017). User acceptance of mobile health services from users’ perspectives: The role of self-efficacy and response-efficacy in technology acceptance. Informatics for Health and Social Care, 42(2), 194-206.

Zhou, M., Zhao, L., Kong, N., Campy, K. S., Qu, S., & Wang, S. (2019). Factors influencing behavior intentions to telehealth by Chinese elderly: An extended TAM model. International Journal of Medical Informatics, 126, 118-127.

Zobair, K. M., Sanzogni, L., & Sandhu, K. (2019). Expectations of telemedicine health service adoption in rural Bangladesh. Social Science & Medicine, 238, 112485.

Zobair, K. M., Sanzogni, L., Sandhu, K., & Islam, M. J. (2020). Telemedicine Adoption Opportunities and Challenges in the Developing World. In Evaluating Challenges and Opportunities for Healthcare Reform (pp. 167-193): Hershey, PE, USA, IGI Global. doi: 10.4018/978-1-7998-8052-3.ch003




How to Cite

Zobair, K. M., Sanzogni, L., Houghton, L., Sandhu, K., & Islam, M. J. . (2021). Health Seekers’ Acceptance and Adoption Determinants of Telemedicine in Emerging Economies. Australasian Journal of Information Systems, 25.



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