Asia pacific journal of information systems

The Korea Society of Management Information Systems (한국경영정보학회)
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Exploring Barriers Affecting e-Health Service Continuance Intention in India: From the Innovation Resistance Theory Stance

  • Arghya Ray (Area of MIS and Analytics, International Management Institute Kolkata) ;
  • Pradip Kumar Bala (Area of Information Systems & Business Analytics, Indian Institute of Management Ranchi) ;
  • Yogesh K. Dwivedi (Digital Marketing and Innovation, Digital Futures for Sustainable Business & Society Research Group, School of Management, Swansea University, Bay Campus)
  • Received : 2022.05.25
  • Accepted : 2022.09.19
  • Published : 2022.12.31
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Abstract

Although existing studies on e-health have usually focused on e-health services adoption intention, there is a dearth of studies on the barriers that affect e-health services retention intention especially in India. Additionally, although studies have mostly focused on utilizing expectation-confirmation model to understand innovation related barriers, innovation resistance theory (IRT) has been overlooked. As Indian e-health service providers face stiff challenges due to customer's unwillingness to continue using the service, there is a need to bridge the research gap that exists in this context. This mixed-method study, based on responses received from 289 participants and 1154 online negative reviews from e-Health providers in India, examines the barriers from the IRT stance. Results of this study reveal a notable negative association between tradition, value and financial barrier and intention to continue using e-health services. Additionally, continuance intention affects recommendation. The study concludes with various implications and scope for future research.

Keywords

References

  1. Agnew, J. R., and Szykman, L. (2005). Asset allocation and information overload: The influence of information display, asset choice and investor experience. Journal of Behavioral Finance, 6(2), 57-70. https://doi.org/10.1207/s15427579jpfm0602_2
  2. Anderson, J. G. (2004). Consumers of e-Health. Social Science Computer Review, 22(2), 242-248. https://doi.org/10.1177/0894439303262671
  3. Al-Debei, M. M., Al-Lozi, E., and Papazafeiropoulou, A. (2013). Why people keep coming back to Facebook: Explaining and predicting continuance participation from an extended theory of planned behaviour perspective. Decision Support Systems, 55(1), 43-54. https://doi.org/10.1016/j.dss.2012.12.032
  4. Ariens, L. F., Schussler-Raymakers, F. M., Frima, C., Flinterman, A., Hamminga, E., Arents, B. W., Bruijnzeel-Koomen, C. A., de Bruin-Weller, M. S., and van Os-Medendorp, H. (2017). Barriers and facilitators to eHealth use in daily practice: Perspectives of patients and professionals in dermatology. Journal of Medical Internet Research, 19(9), e.300. https://doi.org/10.2196/jmir.7512
  5. Arnett, D. B., Laverie, D. A. and Meiers, A. (2003). Developing parsimonious retailer equity indexes using partial least squares analysis: a method and applications. Journal of Retailing, 79(3), 161-70. https://doi.org/10.1016/S0022-4359(03)00036-8
  6. Baabdullah, A. M., Alalwan, A. A., Rana, N. P., Kizgin, H., and Patil, P. (2019). Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model. International Journal of Information Management, 44, 38-52. https://doi.org/10.1016/j.ijinfomgt.2018.09.002
  7. Bajaj, A., and Nidumolu, S. R. (1998). A feedback model to understand information system usage. Information and Management, 33, 213-224. https://doi.org/10.1016/S0378-7206(98)00026-3
  8. Banks, J., Crawford, R., and Tetlow, G. (2015). Annuity choices and income drawdown: evidence from the decummulation phase of defined contribution pensions in England. Journal of Pension Economics and Finance, 14(4), 412-438.
  9. Barkhi, R., and Wallace, L. (2007). The impact of personality type on purchasing decisions in virtual stores. Information Technology and Management, 8, 313-330. https://doi.org/10.1007/s10799-007-0021-y
  10. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25, 351-370. https://doi.org/10.2307/3250921
  11. Bhattacherjee, A., Hikmet, N., Menachemi, N., Kayhan, V. O., and Brooks, R. G. (2006). The differential performance effects of healthcare information technology adoption. Information Systems Management, 24(1), 5-14.
  12. Cashen, M. S., Dykes, P., and Gerber, B. (2004). eHealth technology and internet resources: Barriers for vulnerable populations. The Journal of Cardiovascular Nursing, 19(3), 209-214. https://doi.org/10.1097/00005082-200405000-00010
  13. Cassel, C., Hackl, P. and Westlund, A. H. (1999). Robustness of partial least-squares method for estimating latent variable quality structures. Journal of Applied Statistics, 26(4), 435-46. https://doi.org/10.1080/02664769922322
  14. Chan, C. V., Mirkovic, J., Furniss, S., and Kaufman, D. R. (2015). eHealth literacy demands and cognitive processes underlying barriers in consumer health information seeking. Knowledge Management and E-Learning, 7(4), 550-575.
  15. Chauhan, S., and Jaiswal, M. (2017). A meta-analysis of e-health applications acceptance: Moderating impact of user types and e-health application types. Journal of Enterprise Information Management, 30(2), 295-319. https://doi.org/10.1108/JEIM-08-2015-0078
  16. Chea, S., and Luo, M. M. (2008). Post-adoption behaviors of e-service customers: The interplay of cognition and emotion. International Journal of Electronic Commerce, 12(3), 29-56.
  17. Chen, P. Y., and Hitt, L. M. (2002). Measuring switching costs and the determinants of customer retention in internet-enabled businesses: A study of the online brokerage industry. Information Systems Research, 13(3), 255-274. https://doi.org/10.1287/isre.13.3.255.78
  18. Chen, X. F., Slau, K., and Nah, F. F. H. (2008). Adoption of 3-D Virtual Worlds for Education. ICIS 2008 Proceedings. Association for Information Systems AIS Electronic Library (AISeL).
  19. Chen, H. S., Tsai, B. K., and Hsieh, C. M. (2018). The effects of perceived barriers on innovation resistance of hydrogen-electric motorcycles. Sustainability, 10(6), e.1933. https://doi.org/10.3390/su10061933
  20. Chin, W.W. (1998). Issues and opinions on structural equation modeling. MIS Quarterly, 22(1), 7-16.
  21. Chin, T. (2004). Technology valued, but implementing it into practice is slow. American Medical News, Retrieved from http://www.amednews.com
  22. Chiu, W., Cho, H., and Chi, C. G. (2021). Consumers' continuance intention to use fitness and health apps: An integration of the expectation-confirmation model and investment model. Information Technology & People, 34(3), 978-998. https://doi.org/10.1108/ITP-09-2019-0463
  23. Choi, N. (2009). How loyal are you? Continuance intention and word of mouth in free/libre open source software. Paper presented at the AMCIS 2009 Doctoral Consortium.
  24. Chouk, I., and Mani, Z. (2019). Factors for and against resistance to smart services: role of consumer lifestyle and ecosystem related variables. Journal of Services Marketing, 33(4), 449-462. https://doi.org/10.1108/JSM-01-2018-0046
  25. Cruz, P., Barretto, F.N.L., Munoz Gallego, P., and Laukkanen, T. (2010). Mobile banking rollout in emerging markets: Evidence from Brazil. International Journal of Bank Marketing, 28(5), 342-371. https://doi.org/10.1108/02652321011064881
  26. Davidson, E., and Heslinga, D. (2006). Bridging the IT adoption gap for small physician practices: An action research study on electronic health records. Information Systems Management, 24(1), 15-28. https://doi.org/10.1080/10580530601036786
  27. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319-340. https://doi.org/10.2307/249008
  28. Dharaiya, S., Soneji, B., Kakkad, D., and Tada, N. (2020). Generating positive and negative sentiment word clouds from e-commerce product reviews. 2020 International Conference on Computational Performance Evaluation (ComPE), 2020, 459-463. https://doi.org/10.1109/ComPE49325.2020.9200056.
  29. Dickinger, A., Arami, M., and Meyer, D. (2008). The role of perceived enjoyment and social norm in the adoption of technology with network externalities. European Journal of Information Systems, 17, 4-11. https://doi.org/10.1057/palgrave.ejis.3000726
  30. Eden, K. B., Totten, A. M., Kassakian, S. Z., Gorman, P. N., McDonagh, M. S., Devine, B., Pappas, M., Daeges, M., Woods, S., and Hersh, W. R. (2016). Barriers and facilitators to exchanging health information: A systematic review. International Journal of Medical Informatics, 88, 44-51.
  31. Esmaeilzadeh, P. (2019). Consumers' perceptions of using health information exchanges (HIEs) for research purposes. Information Systems Management, 36(1), 57-77. https://doi.org/10.1080/10580530.2018.1553649
  32. Featherman, M. S., and Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59, 451-474.
  33. Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50. https://doi.org/10.1177/002224378101800104
  34. Franke, G., and Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, 29(3), 430-447. https://doi.org/10.1108/IntR-12-2017-0515
  35. Freudenheim, M. (2004). Many hospitals resist computerized patient care. New York Times, Retrieved from http://www.nytimes.com/2004/04/06/technology/06errors.html
  36. Gao, Z., Yada, S., Wakamiya, S., and Aramaki, E. (2020). NAIST COVID: Multilingual COVID-19 Twitter and Weibo Dataset, Retrieved from https://arxiv.org/abs/2004.08145
  37. Gefen, D., Straub, D., and Boudreau, M. (2000). Structural equation modeling and regression guidelines for research practice. Communications of the Association of Information Systems, 4, 1-79.
  38. Gerpott, J. T., Rams, W., and Schindler, A. (2001). Customer retention, loyalty, and satisfaction in the German mobile cellular telecommunications market. Telecommunications Policy, 25, 249-269. https://doi.org/10.1016/S0308-5961(00)00097-5
  39. Goodhue, D. L., Lewis, W., and Thompson, R. (2012). Does PLS have advantages for small sample size or non-normal data? MIS Quarterly, 36(3), 981-1001. https://doi.org/10.2307/41703490
  40. Gopstein, D., Fayard, A. L., Apel, S., and Cappos, J. (2020). Thinking aloud about confusing code: a qualitative investigation of program comprehension and atoms of confusion. ESEC/FSE 2020: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 605-616. https://doi.org/10.1145/3368089.3409714
  41. Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360-1380. https://doi.org/10.1086/225469
  42. Gregorio, J., Ferreira, T. L., Cavaco, A., da Silva, M. M., Lovis, C., and Lapao, L. V. (2013). Community pharmacies and eHealth services: Barriers and opportunities for real Primary Healthcare integration. Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems.
  43. Griebel, L., Pobiruchin, M., and Wiesner, M. (2017). Report on the MIE workshop: Consumer health informatics - barriers and facilitators of eHealth usage among consumers. MIE 2015 Workshop.
  44. Gustafsson, A., Johnson, M. D., and Roos, I. (2005). The effects of customer satisfaction, relationship commitment dimensions, and triggers on customer retention. Journal of Marketing, 69, 210-218. https://doi.org/10.1509/jmkg.2005.69.4.210
  45. Gyamfi, S. A., Koranteng, F. N., Apau, R., and Ansong-Gyimah, K. (2020). Predicting engagement on collaborative learning systems: Perceptions of postgraduate students. Proceedings of the 2020 9th International Conference on Educational and Information Technology, 102-107.
  46. Hair, Jr., Anderson, R. E., Tatham, R. L., and Black, W. C. (1998). Multivariate data analysis with readings (5th ed.). Prentice Hall, Enlgewood Cliffs, NJ.
  47. Hair, J. F., Ringle, C. M. and Sarstedt, M. (2013). Partial least squares structural equation modeling: rigorous applications, better results and higher acceptance. Long Range Planning, 46, 1-12. https://doi.org/10.1016/j.lrp.2013.01.001
  48. Heidenreich, S., and Handrich, M. (2015). What about passive innovation resistance? Investigating adoption-related behavior from a resistance perspective. Journal of Product Innovation Management, 32(6), 878-903. https://doi.org/10.1111/jpim.12161
  49. Henseler, J., Ringle, C. M., and Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
  50. Hew, J. T., Tan, G. W. H., Lin, B., and Ooi, K. B. (2017). Generating travel-related contents through mobile social tourism: Does privacy paradox persist? Telematics and Informatics, 34(7), 914-935. https://doi.org/10.1016/j.tele.2017.04.001
  51. Hung, F. (2010). Intention of students in less developed cities in China to opt for undergraduate education abroad: Does this vary as their perceptions of the attractions of overseas study change? International Journal of Educational Development, 30(2), 213-223. https://doi.org/10.1016/j.ijedudev.2009.03.003
  52. IBEF (2018). Healthcare Industry in India. India, Retrieved from https://www.ibef.org/industry/healthcare-india.aspx/
  53. Ihli, H. J., Gassner, A., and Musshoff, O. (2018). Experimental insights on the investment behaviour of small-scale coffee farmers in central Uganda under risk and uncertainty. Journal of Behavioral and Experimental Economics, 75, 31-44. https://doi.org/10.1016/j.socec.2018.04.011
  54. Jaroslawski, S., and Saberwal, G. (2014). In eHealth in India today, the nature of work, the challenges and the finances: An interview-based study. BMC Medical Informatics and Decision Making, 14(1), 1-12. https://doi.org/10.1186/1472-6947-14-1
  55. Javed, M., Tuckova, Z., and Jibril, A. B. (2020). The role of social media on tourists' behavior: An empirical analysis of millennials from the Czech Republic. Sustainability, 12(18), e.7735.
  56. Jensen, T. B., and Aanestad, M. (2006). How healthcare professionals "make sense" of an electronic patient record adoption. Information Systems Management, 24(1), 29-42. https://doi.org/10.1080/10580530601036794
  57. Jiang, J. J., Muhanna, W. A., and Klein, G. (2000). User resistance and strategies for promoting acceptance across system types. Information & Management, 37(1), 25-36.
  58. Joachim, V., Spieth, P., and Heidenreich, S. (2018). Active innovation resistance: An empirical study on functional and psychological barriers to innovation adoption in different contexts. Industrial Marketing Management, 71, 95-107.
  59. Kaur, P., Dhir, A., Bodhi, R., Singh, T., and Almotairi, M. (2020). Why do people use and recommend m-wallets?. Journal of Retailing and Consumer Services, 56, 102091. https://doi.org/10.1016/j.jretconser.2020.102091
  60. Kautsch, M., Lichon, M., and Matuszak, N. (2016). Development of publicly funded eHealth in Poland: Barriers and opportunities. Economics and Sociology, 9(3), 28-40. https://doi.org/10.14254/2071-789X.2016/9-3/3
  61. Kim, H. H., Maurer, R., and Mitchell, O. S. (2016). Time is money: Rational life cycle inertia and the delegation of investment management. Journal of Financial Economics, 121(2), 427-447. https://doi.org/10.1016/j.jfineco.2016.03.008
  62. Kumar, K. A., and Natarajan, S. (2020). An extension of the expectation confirmation model (ECM) to study continuance behavior in using e-Health services. Innovative Marketing, 16(2), 15-28. https://doi.org/10.21511/im.16(2).2020.02
  63. Lam, W. (2005), Barriers to e government integration. Journal of Enterprise Information Management, 18(5), 511-530. https://doi.org/10.1108/17410390510623981
  64. Landi, H. (2018). Study: Healthcare Lags Other Industries in Digital Transformation, Customer Engagement Tech. Healthcare Innovation, Retrieved from https://www.hcinnovationgroup.com/population-health-management/news/13030021/study-healthcare-lags-other-industries-in-digital-transformation-customer-engagement-tech
  65. Landis-Lewis, Z., Manjomo, R., Gadabu, O. J., Kam, M., Simwaka, B. N., Zickmund, S. L., Chimbwandira, F., Douglas, G. P., and Jacobson, R. S. (2015). Barriers to using eHealth data for clinical performance feedback in Malawi: A case study. International Journal of Medical Informatics, 84(10), 868-875.
  66. Laukkanen, P., Sinkkonen, S., and Laukkanen, T. (2008). Consumer resistance to Internet banking: Postponers, opponents and rejecters. The International Journal of Bank Marketing, 26(6), 440-455. https://doi.org/10.1108/02652320810902451
  67. Laukkanen, T. (2016). Consumer adoption versus rejection decisions in seemingly similar service innovations: The case of the internet and mobile banking. Journal of Business Research, 69(7), 2432-2439. https://doi.org/10.1016/j.jbusres.2016.01.013
  68. Leckenby, D., and Hesse-Biber, S. N. (2007). Feminist Approaches to Mixed-Methods Research. In S. N. Hesse-Biber and P. Lina (Eds.), Feminist research practice: A Primer. Chapter 9. pp.249. Sage Publications Inc.
  69. Letchumanan, M., and Muniandy, B. (2013). Migrating to e-book: A study on perceived usefulness and ease of use. Library Hi Tech News., 30(7), 10-16. https://doi.org/10.1108/LHTN-05-2013-0028
  70. Lian, J. W., and Yen, D. C. (2013). To buy or not to buy experience goods online: Perspective of innovation adoption barriers. Computers in Human Behavior, 29(3), 665-672.
  71. Lian, J. W., and Yen, D. C. (2014). Online shopping drivers and barriers for older adults: Age and gender differences. Computers in Human Behavior, 37, 133-143. https://doi.org/10.1016/j.chb.2014.04.028
  72. Li, H., and Liu, Y. (2011). Post-adoption behaviour of e-service users: An empirical study on Chinese. In Proceedings of the ECIS, 2011.
  73. Ma, L., and Lee, C. S. (2018). Understanding the barriers to the use of MOOCs in a developing country: An innovation resistance perspective. Journal of Educational Computing Research, 57(3), 571-590.
  74. Mani, Z., and Chouk, I. (2018). Consumer resistance to innovation in services: Challenges and barriers in the internet of things era. Journal of Product Innovation Management, 35(5), 780-807.
  75. Maqableh, M., Jaradat, M., and Azzam, A. (2021). Exploring the determinants of students' academic performance at university level: The mediating role of internet usage continuance intention. Education and Information Technologies, 26, 4003-4025. https://doi.org/10.1007/s10639-021-10453-y
  76. Mazurek, C., and Stroinski, M. (2010). Innovative ICT Platform for Emerging eHealth Services: Towards Overcoming Technical and Social Barriers and Solving Grand Challenges in Medicine. 2010 Second International Conference on eHealth, Telemedicine, and Social Medicine.
  77. Nakada, T., Shin, K., and Managi, S. (2016). The effect of demand response on purchase intention of distributed generation: Evidence from Japan. Energy Policy, 94, 307-316. https://doi.org/10.1016/j.enpol.2016.04.026
  78. Natsiavas, P., Kakalou, C., Votis, K., Tzovaras, D., Maglaveras, N., Komnios, I., and Koutkias, V. (2017). Identification of Barriers and Facilitators for eHealth Acceptance: The KONFIDO Study. IFMBE Proceedings, 81-85.
  79. Nijeweme-d'Hollosy, W. O., van Velsen, L., Huygens, M., and Hermens, H. (2015). Requirements for and barriers towards interoperable eHealth technology in primary care. IEEE Internet Computing, 19(4), 10-19.
  80. Parasuraman, A., Zeithaml, V. A., and Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions. Journal of Retailing, 64(1), 12-40.
  81. Parry, S. (2010). Fit Statistics commonly reported for CFA and SEM. Cornell Statistical Consulting Unit, Retrieved from https://www.cscu.cornell.edu/news/Handouts/SEM_fit.pdf
  82. Ram, S. (1987). A Model of Innovation Resistance. In NA - Advances in Consumer Research Volume 14, eds. Melanie Wallendorf and Paul Anderson, Provo, UT: Association for Consumer Research, 208-212.
  83. Ram, S., and Sheth, J. N. (1989). Consumer resistance to innovations: The marketing problem and its solutions. Journal of Consumer Marketing, 6(2), 5-14.
  84. Ranaweera, C., and Prabhu, J. (2003). On the relative importance of customer satisfaction and trust as determinants of customer retention and positive word of mouth. Journal of Targeting, Measurement and Analysis for Marketing, 12, 82-90. https://doi.org/10.1057/palgrave.jt.5740100
  85. Ray, A., and Bala, P. K. (2021). User generated content for exploring factors affecting intention to use travel and food delivery services. International Journal of Hospitality Management, 92, 102730. https://doi.org/10.1016/j.ijhm.2020.102730
  86. Ray, A., Bala, P. K., and Rana, N. P. (2021). Exploring the drivers of customers' brand attitudes of online travel agency services: A text-mining based approach. Journal of Business Research, 128, 391-404. https://doi.org/10.1016/j.jbusres.2021.02.028
  87. Ray, A., and Bala, P. K. (2019). Use of NLP and SEM in determining factors for e-service adoption. In Y. Akgul (Eds), Structural equation modeling approaches to e-service adoption (pp. 38-47).
  88. Ray, A., Bala, P. K., and Dasgupta, S. A. (2019). Role of authenticity and perceived benefits of online courses on technology based career choice in India: A modified technology adoption model based on career theory. International Journal of Information Management, 47, 140-151.
  89. Ray, A., Bala, P. K., and Dwivedi, Y. K. (2020). Exploring values affecting e-Learning adoption from the user-generated-content: A consumption-value-theory perspective. Journal of Strategic Marketing, 1-23. https://doi.org/10.1080/0965254x.2020.1749875
  90. Ray, A., Bala, P. K., Dasgupta, S. A. (2020). Psychological analytics based technology adoption model for effective educational marketing. In Rana et al. (Eds.), Digital and Social Media Marketing. Advances in Theory and Practice of Emerging Markets. Springer, Cham. https://doi.org/10.1007/978-3-030-24374-6_12
  91. Ray, A., Bala, P. K., Chakraborty, S., and Dasgupta, S. A. (2021). Exploring the impact of different factors on brand equity and intention to take up online courses from e-Learning platforms. Journal of Retailing and Consumer Services, 59, 102351. https://doi.org/10.1016/j.jretconser.2020.102351
  92. Ray, A., Bala, P. K., and Jain, R. (2022). How can topic-modelling of user-reviews reshape market surveys? Exploring factors influencing usage intention of e-learning services through a novel multi-method approach. International Journal of Business Information Systems, 40(2), 259-284. https://doi.org/10.1504/IJBIS.2022.123646
  93. Reichheld, F. F., and Sasser, W. E. (1990). Zero defections: Quality comes to services. Harvard Business Review, 68, e.105.
  94. Ringle, C. M., Wende, S., and Becker, J. M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH, http://www.smartpls.com.
  95. Rust, R. T., and Zahorik, A. J. (1993). Customer satisfaction, customer retention, and market share. Journal of Retailing, 69, 193-215. https://doi.org/10.1016/0022-4359(93)90003-2
  96. Saleem, J. J., Plew, W. R., Speir, R. C., Herout, J., Wilck, N. R., Ryan, D. M., Cullen, T. A., Scott, J. M., Benne, M. S., and Phillips, T. (2015). Understanding barriers and facilitators to the use of Clinical Information Systems for intensive care units and Anesthesia Record Keeping: A rapid ethnography. International Journal of Medical Informatics, 84(7), 500-511. https://doi.org/10.1016/j.ijmedinf.2015.03.006
  97. She, L., Ma, L., Jan, A., Sharif Nia, H., and Rahmatpour, P. (2021). Online learning satisfaction during COVID-19 pandemic among Chinese university students: The serial mediation model. Frontiers in Psychology, 12, 743936. https://doi.org/10.3389/fpsyg.2021.743936
  98. She, L., Rasiah, R., Turner, J. J., Guptan, V., and Nia, H. S. (2021). Psychological beliefs and financial well-being among working adults: The mediating role of financial behaviour. International Journal of Social Economics, 49(2), 190-209.
  99. Sivathanu, B. (2018). Adoption of digital payment systems in the era of demonetization in India. Journal of Science and Technology Policy Management, 10(1), 143-171. https://doi.org/10.1108/jstpm-07-2017-0033
  100. Srivastava, S., Pant, M., and Agarwal, N. (2014). Indian eHealth services: A study. Proceedings of the Third International Conference on Soft Computing for Problem Solving, 801-813. https://doi.org/10.1007/978-81-322-1768-8_69
  101. Tandon, P. (2015). eHealth is a key pillar of Digital India, Retrieved from https://economictimes.indiatimes.com/industry/healthcare/biotech/pharmaceuticals/why-ehealth-is-a-key-pillar-of-digital-india/articleshow/48069561.cms?from=mdr
  102. TheHansIndia. (2018). India's e-health initiatives, Retrieved from https://www.thehansindia.com/posts/index/Hans/2018-05-30/Indias-e-health-initiatives/385197
  103. Treskes, R. W., Wildbergh, T. X., Schalij, M. J., and Scherptong, R. W. C. (2018). Expectations and perceived barriers to widespread implementation of e-Health in cardiology practice: Results from a national survey in the Netherlands. Netherlands Heart Journal, 27(1), 18-23.
  104. Venkatesh, V., and Goyal, S. (2010). Expectation disconfirmation and technology adoption: Polynomial modeling and response surface analysis. MIS Quarterly, 34, 281-303. https://doi.org/10.2307/20721428
  105. Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425-478. https://doi.org/10.2307/30036540
  106. Walsh, C., O'Reilly, P., Gleasure, R., McAvoy, J., and O'Leary, K. (2021). Understanding manager resistance to blockchain systems. European Management Journal, 39(3), 353-365.
  107. Weimann, G. (1983). The strength of weak conversational ties in the flow of information and influence. Social Networks, 5(3), 245-267. https://doi.org/10.1016/0378-8733(83)90027-8
  108. Wood, L. (2020). India eHealth Market Report 2020: A $16 Billion Opportunity by FY 2025, Retrieved from https://www.globenewswire.com/news-release/2020/10/05/2103408/0/en/India-eHealth-Market-Report-2020-A-16-Billion-Opportunity-by-FY-2025.html
  109. Yang, H. D., and Yoo, Y. (2004). It's all about attitude: Revisiting the technology acceptance model. Decision Support Systems, 38, 19-31. https://doi.org/10.1016/S0167-9236(03)00062-9
  110. Zafiropoulos, K., Karavasilis, I., and Vrana, V. (2012). Assessing the adoption of e-Government services by teachers in Greece. Future Internet, 4(2), 528-544. https://doi.org/10.3390/fi4020528
  111. Zhang, X., Yan, X., Cao, X., Sun, Y., Chen, H., and She, J. (2018). The role of perceived e-health literacy in users' continuance intention to use mobile healthcare applications: An exploratory empirical study in China. Information Technology for Development, 24(2), 198-223. https://doi.org/10.1080/02681102.2017.1283286
  112. Zhou, S., Yu, X., and Luo, C. (2018). Understanding WeChat User's Intention to Use Various Functions: from Social Cognitive Perspective. WHICEB 2018 Proceedings. Association for Information Systems AIS Electronic Library (AISeL).
  113. Zhou, T. (2018). Understanding online knowledge community user continuance: A social cognitive theory perspective. Data Technologies and Applications, 52(3), 445-458. https://doi.org/10.1108/DTA-10-2017-0077
  114. Zibrik, L., Khan, S., Bangar, N., Stacy, E.,, Novak Lauscher, H., and Ho, K. (2015). Patient and community centered eHealth: Exploring eHealth barriers and facilitators for chronic disease self-management within British Columbia's immigrant Chinese and Punjabi seniors. Health Policy and Technology, 4(4), 348-356.