A Study on Adoption of E-Health Services : Developing an Integrated Framework in a Multinational Context
DOI:
https://doi.org/10.17010/ijom/2022/v52/i5/169415Keywords:
E-health, trialability, perceived ease of use, subjective norms, initial trust, intention to adopt, Saudi Arabia, India.Abstract
Purpose : This study focused on adopting e-health services in a global context. Researchers intended to address three primary research questions: How do the factors affect the prospective app users’ initial trust and adoption intention? Does initial trust mediate the relationship between trialability, perceived ease of use, subjective norms, and intention to adopt? Finally, how do Indian and Saudi Arabian consumers differ in their intention to adopt and the possible reasons?
Methodology : Four hundred twenty-six potential e-health services users from Saudi Arabia and India were considered as the sample. Structural equation modeling was used to analyze the same. Further, a multi-group analysis was undertaken to compare both samples.
Findings : The findings suggested that trialability (TB) had a positive and significant impact on both initial trust (IT) and intention to adopt (IA). Perceived ease of use (PEOU) had a significant and positive impact on IA; however, it was not valid for IT. Subjective norms (SN) positively and significantly impacted IT and IA. Further, IT substantially affected IA. The multi-group analysis found that in the case of India, TB had a significant and positive impact on both IT and IA. In contrast, it did not positively impact IT in the case of Saudi Arabia. PEOU had an insignificant impact on IT for both the samples, and multi-group analysis was confirmed. For both samples, SN had a significant and positive impact on IT. IT had a significant and positive impact on IA for both samples. Finally, IT was a good mediator between TB and SN with IA.
Originality : This study provided a unique futuristic e-health adoption framework tested in a multinational and emerging economy context. It combined four major theories in doing the same.
Downloads
Downloads
Published
How to Cite
Issue
Section
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Alharbe, N. (2021). Security and privacy of telehealth apps in the era of COVID-19 pandemic: Saudi Arabia perspective. In, iiWAS2021: The 23rd International Conference on Information Integration and Web Intelligence, 467– 472. https://doi.org/10.1145/3487664.3487729
Alhazri, W. A., & Bugis, B. A. (2022). Electronic healthcare applications and programs among healthcare workers in Riyadh and conflict management. Journal of Taibah University Medical Sciences. In Press,1–9. https://doi.org/10.1016/J.JTUMED.2021.11.016
Ali, S., Khalid, N., Javed, H. M. U., & Islam, D. M. (2021). Consumer adoption of online food delivery ordering (OFDO) services in Pakistan : The impact of the COVID-19 pandemic situation. Journal of Open Innovation: Technology, Market, and Complexity,7(10),1–23.https://doi.org.sdl.idm.oclc.org/10.3390/joitmc7010010
Alsharif, A. H. (2021). Cross sectional e-health evaluation study for telemedicine and m-health approaches in monitoring COVID-19 patients with chronic obstructive pulmonary disease (COPD). International Journal of Environ mental Research and Public Health,18(16),8513 . https://doi.org/10.3390/IJERPH18168513
Apidi, N. A., Murugiah, M. K., Muthuveloo, R., Soh, Y. C., Caruso, V., Patel, R., & Ming, L. C. (2017). Mobile medical applications for dosage recommendation, drug adverse reaction, and drug interaction : Review and comparison. Therapeutic Innovation & Regulatory Science,51(4),480–485 . https://doi.org/10.1177/2168479017696266
Bagozzi, R. P., Davis, F. D., & Warshaw, P. R. (1992). Development and test of a theory of technological learning and usage. Human Relations, 45(7), 659 – 686. https://doi.org/10.1177/001872679204500702
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. https://doi.org/10.1057/palgrave.ejis.3000717
Bhavani, B. G. (2021). A study to assess the knowledge and opinion of mid-level health providers of Chittoor District regarding m-health. In, S. Jyothi, D. M. Mamatha, Y. D. Zhang, & K. S. Raju (eds), Proceedings of the 2nd International Conference on Computational and Bio Engineering. Lecture Notes in Networks and Systems(Vol. 215, pp. 85 – 91). Springer. https://doi.org/10.1007/978-981-16-1941-0_10
Binyamin, S. S., & Zafar, B. A. (2021). Proposing a mobile apps acceptance model for users in the health area: A systematic literature review and meta-analysis. Health Informatics Journal, 27(1), 1–27. https://doi.org/10.1177/1460458220976737
Booday, M., & Albesher, A. (2022). Evaluating the usability of mobile applications: The case of Covid-19 apps in Saudi Arabia. In, 22nd International Arab Conference on Information Technology (ACIT), 1–7. https://doi.org/10.1109/ACIT53391.2021.9677200
Chakraborty, D. (2018). Impact of celebrity endorsed advertisements on rural consumers' purchase intention towards mobile SIM cards: A study conducted in West Bengal. Indian Journal of Marketing, 48(10), 52 – 63. https://doi.org/10.17010/ijom/2018/v48/i10/132335
Chakraborty, D. (2021). Factors influencing passengers' purchase intention towards app-cab services in metro cities of India: A study on smartphone users. Indian Journal of Marketing , 51 (1), 41–54. https://doi.org/10.17010/ijom/2021/v51/i1/156933
Chakraborty, D., & Altekar, S. (2021a). What drives people to use grocery apps ? The moderating & mediating role of customer involvement and trust. Indian Journal of Marketing , 51 (11),23–37 . https://doi.org/10.17010/ijom/2021/v51/i11/166734
Chakraborty, D., & Altekar, S. (2021b). Work from home (WFH), COVID-19, and its impact on women. Prabandhan: Indian Journal of Management, 14(9), 22 – 29. https://doi.org/10.17010/pijom/2021/v14i9/166294
Chakraborty, D., & Dash, G. (2022). 'The New Normal' and the strategies of marketers to attract consumers. Indian Journal of Marketing, 52(1), 58 – 62. https://doi.org/10.17010/ijom/2022/v52/i1/167652
Chakraborty, D., Bhatnagar, S. B., Biswas, W., & Khatua, A. K. (2022). What drives people to adopt grocery apps ? The moderating role of household size. Business Perspectives and Research, 1–23. https://doi.org/10.1177/22785337221091640
Chakraborty, D., Biswas, W., & Dash, G. (2021). Marching toward “heart workâ€: Connecting in new ways to thrive amidst COVID-19 crisis. Conflict Resolution Quarterly,39(1),7–27 . https://doi.org/10.1002/crq.21313
Chakraborty, D., Siddiqui, A., Siddiqui, M., Rana, N. P., & Dash, G. (2022). Mobile payment apps filling value gaps : Integrating consumption values with initial trust and customer involvement. Journal of Retailing and Consumer Services, 66, 102946. https://doi.org/10.1016/j.jretconser.2022.102946
Chen, Y., Yang, L., Zhang, M., & Yang, J. (2018). Central or peripheral? Cognition elaboration cues' effect on users' continuance intention of mobile health applications in the developing markets. International Journal of Medical Informatics, 116, 33 – 45. https://doi.org/10.1016/j.ijmedinf.2018.04.008
Cheung, G. W., & Lau, R. S. (2008). Testing mediation and suppression effects of latent variables : Bootstrapping with structural equation models. Organizational Research Methods, 11(2),296–325 . https://doi.org/10.1177/1094428107300343
Dash, G., & Paul, J. (2021). CB-SEM vs. PLS-SEM methods for research in social sciences and technology forecasting. Technological Fore casting and Social Change,173, 121092 . https://doi.org/10.1016/j.techfore.2021.121092
Dash, G. (2022). Pandemic induced e-learning and the impact on the stakeholders: Mediating role of satisfaction and moderating role of choice. Athens Journal of Education,9,1–22 . https://www.athensjournals.gr/education/2021-4492-AJE-Dash-05.pdf
Dash, G., & Chakraborty, D. (2021). Transition to e-learning: By choice or by force – A cross-cultural and trans-nationalassessment.Prabandhan: Indian Journal of Management, 14(3),8–23 . https://doi.org/10.17010/pijom/2021/v14i3/158151
Dash, G., Kiefer, K., & Paul, J. (2021). Marketing-to-millennials: Marketing 4.0, customer satisfaction and purchase intention. Journal of Business Research, 122, 608 – 620. https://doi.org/10.1016/j.jbusres.2020.10.016
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319 – 340. https://doi.org/10.2307/249008
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 . https://doi.org/10.1080/07421222.2003.11045748
Deng, Z., Hong, Z., Ren, C., Zhang, W., & Xiang, F. (2018). What predicts patients' adoption intention toward mHealth services in China : An empirical study. JMIRmHealthandu Health,6(8),1–14 . https://doi.org/10.2196/mhealth.9316
Fan, W., Liu, J., Zhu, S., & Pardalos, P. M. (2020). Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Annals of Operations Research, 294(1/2), 567–592. https://doi-org.sdl.idm.oclc.org/10.1007/s10479-018-2818y
Gao, L., Wang, S., Li, J., & Li, H. (2017). Application of the extended theory of planned behavior to understand individual's energy saving behavior in workplaces. Resources, Conservation and Recycling, 127, 107–113. https://doi.org/10.1016/j.resconrec.2017.08.030
Gupta, A., Dhiman, N., Yousaf, A., & Arora, N. (2020). Social comparison and continuance intention of smart fitness wearables: An extended expectation confirmation theory perspective. Behaviour & Information Technology, 4(2), 1–14. https://doi-org.sdl.idm.oclc.org/10.1080/0144929X.2020.1748715
Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate data analysis: A global perspective (Vol. 7). Pearson Education.
Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research, 62(5), 565 – 571. https://doi.org/10.1016/j.jbusres.2008.06.016
Izahar, S., Lean, Q. Y., Hameed, M. A., Murugiah, M. K., Patel, R. P., Al-Worafi, Y. M., Wong, T. W., & Ming, L. C. (2017). Content analysis of mobile health applications on diabetes mellitus. Frontiers in Endocrinology, 8, 1 – 8. https://doi.org/10.3389/fendo.2017.00318
Johnston, A. C., & Warkentin, M. (2010). Fear appeals and information security behaviors: An empirical study. MIS Quarterly, 34(3), 549 – 566. https://doi.org/10.2307/25750691
Kim, H. - B., Kim, T. T., & Shin, S. W. (2009). Modeling roles of subjective norms and eTrust in customers' acceptance of airline B2Ce Commerce websites. Tourism Management, 30(2) , 266–277 . https://doi.org/10.1016/j.tourman.2008.07.001
Lin, J., Carter, L., & Liu, D. (2021). Privacy concerns and digital government: Exploring citizen willingness to adopt the COVIDSafe app. European Journal of Information Systems, 30 (4), 389–402. https://doi.org/10.1080/0960085X.2021.1920857
Lin, T. T., & Bautista, J. R. (2017). Understanding the relationships between mhealth apps' characteristics, trialability, and m health literacy. Journal of Health Communication,22(4), 346–354 . https://doi-org.sdl.idm.oclc.org/10.1080/10810730.2017.1296508
Malaquias, R. F., & Hwang, Y. (2016). An empirical study on trust in mobile banking: A developing country perspective. Computers in Human Behavior, 54, 453 – 461. https://doi.org/10.1016/j.chb.2015.08.039
Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common method variance in IS research : A comparison of alternative approaches and a reanalysis of past research. Management Science, 52(12), 1865–1883. https://doi.org/10.1287/mnsc.1060.0597
McKnight, D. H. (2005). Trust in information technology. In G. B. Davis (ed.), The Blackwell encyclopedia of management. Management information systems(pp. 329 – 331). Blackwell.
Mehra, A., Rajput, S., & Paul, J. (2022). Determinants of adoption of latest version smartphones : Theory and e v i d e n c e . Technological Fore casting and Social Change,175,121410 . https://doi.org/10.1016/j.techfore.2021.121410
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research , 2 (3), 192–222. https://doi.org/10.1287/isre.2.3.192
Mortazavi, M., Rahim Esfidani, M., & Barzoki, A. S. (2014). Influencing VSN users' purchase intentions : The roles of flow, trust and eWOM. Journal of Research in Interactive Marketing, 8(2), 102–123. https://doi.org/10.1108/JRIM-08-2013-0057
Nguyen, H. Q., Carrieri-Kohlman, V., Rankin, S. H., Slaughter, R., & Stulbarg, M. S. (2004). Supporting cardiac recovery through eHealth technology. The Journal of Cardiovascular Nursing, 19(3), 200 – 208. https://doi.org/10.1097/00005082-200405000-00009
Orfali, R., Perveen, S., Aati, H. Y., & Al-Taweel, A. M. (2021). nCOVID-19 outcomes on curfews and lockdown: Precautionary decisions in Saudi Arabia. Health Policy and Technology, 10(3), 100538. https://doi.org/10.1016/j.hlpt.2021.100538
Park, H. S. (2000). Relationships among attitudes and subjective norms : Testing the theory of reasoned action across cultures. Communication Studies, 51(2), 162 – 175. https://doi.org/10.1080/10510970009388516
Premazzi, K., Castaldo, S., Grosso, M., Raman, P., Brudvig, S., & Hofacker, C. F. (2010). Customer information sharing with e-vendors: The roles of incentives and trust. International Journal of Electronic Commerce, 14(3), 63 – 91. https://doi.org/10.2753/JEC1086-4415140304
Rogers, E. M. (2003). Diffusion of innovations(5th ed.). Simon and Schuster.
Talwar, S., Dhir, A., Khalil, A., Mohan, G., & Islam, A. N. (2020). Point of adoption and beyond. Initial trust and mobile-payment continuation intention. Journal of Retailing and Consumer Services, 55,102086. https://doi.org/10.1016/j.jretconser.2020.102086
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. https://doi.org/10.2307/30036540
Wang, E. S. (2014). Perceived control and gender difference on the relationship between trialability and intent to playne wonline games . Computers in Human Behavior , 3 0 , 3 1 5 – 3 2 0 . https://doi.org/10.1016/j.chb.2013.09.016
Wei, T. T., Marthandan, G., Chong, A. Y. - L., Ooi, K. - B., & Arumugam, S. (2009). What drives Malaysian m-commerce adoption? An empirical analysis. Industrial Management & Data Systems, 109(3), 370 – 388. https://doi.org/10.1108/02635570910939399
Yadav, S. K., Mishra, A., & Mishra, S. K. (2021). Telemedicine : History and success story of remote surgical education in India. Indian Journal of Surgery, 1–5. https://doi.org/10.1007/S12262-021-03020-9/FIGURES/1
Zhang, X., Yan, X., Cao, X., Sun, Y., Chen, H., & 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
