What Factors Aaffect Continuous Usage Intention In Health Applications?
Keywords:
perceived usefulness perceived ease of use customer satisfaction health consciousness EWOM, self-efficacy continuous usage intention health applications.Abstract
The Covid-19 pandemic is proven to cause an acceleration of digitization in the health sector, as shown by the surge in the use of health applications. The transition to post-pandemic will certainly affect one's intention regarding the continuous use of these health applications. This study aims to determine the relationship between perceived usefulness and perceived ease of use on customer satisfaction as well as variables of health consciousness, EWOM, and Self efficacy in influencing continuous usage intention of health applications in Jabodetabek. This research was conducted using a survey method with an online questionnaire to 200 respondents. Data analysis using Structural Equation Modeling (SEM) method with SPSS and SmartPLS software. The results showed that perceived usefulness, perceived ease of use and customer satisfaction have a positive effect on customer satisfaction. In addition, the variables of health consciousness, E-WOM and Self efficacy were also found to have a positive effect on continuous usage intention of health applications. From a managerial perspective, it is hoped that a manager can design content and features in a health application that are tailored to user needs so as to increase the user's desire to continue using the application.
Downloads
References
Akter, S., Ray, P., & D’Ambra, J. (2013). Continuance of mHealth services at the bottom of the pyramid: The roles of service quality and trust. Electronic Markets, 23(1), 29–47. https://doi.org/10.1007/S12525-012-0091-5/TABLES/10
Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28–44. https://doi.org/10.1016/J.IJINFOMGT.2019.04.008
Alharbi, N. S., Alghanmi, A. S., & Fahlevi, M. (2022). Adoption of Health Mobile Apps during the COVID-19 Lockdown: A Health Belief Model Approach. International Journal of Environmental Research and Public Health, 19(7). https://doi.org/10.3390/ijerph19074179
Anderson, R. E., & Srinivasan, S. S. (2003). E-Satisfaction and E-Loyalty: A Contingency Framework. Psychology and Marketing, 20(2), 123–138. https://doi.org/10.1002/mar.10063
Bauerová, R., & Klepek, M. (2018). Technology acceptance as a determinant of online grocery shopping adoption. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 66(3), 737–746. https://doi.org/10.11118/actaun201866030737
Bestsennyy, O., Gilbert, G., Harris, A., & Rost, J. (2021). Telehealth: A post-COVID-19 reality? | McKinsey. https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/telehealth-a-quarter-trillion-dollar-post-covid-19-reality
Bhattacherjee, A. (2001). Understandinignformatiosnystems Continuancea: An Expectation-Confirmatiom Model. MIS Quarterly, 25(3), 351–370.
Çelik, H. E., & Yilmaz, V. (2011). Extending the technology acceptance model for adoption of e-shopping by consumers in Turkey. Journal of Electronic Commerce Research, 12(2), 152–164.
Chiu, W., Cho, H., & Chi, C. G. (2020). Consumers’ continuance intention to use fitness and health apps: an integration of the expectation–confirmation model and investment model. Information Technology and People, 34(3), 978–998. https://doi.org/10.1108/ITP-09-2019-0463
Cho, J. (2016). The impact of post-adoption beliefs on the continued use of health apps. International Journal of Medical Informatics, 87, 75–83. https://doi.org/10.1016/j.ijmedinf.2015.12.016
Chu, S.-C., & Kim, J. (2018). The current state of knowledge on electronic word-of-mouth in advertising research. International Journal of Advertising, 37, 1–13. https://doi.org/10.1080/02650487.2017.1407061
Cucinotta, D., & Vanelli, M. (2020). WHO declares COVID-19 a pandemic. Acta Biomedica, 91(1), 157–160. https://doi.org/10.23750/abm.v91i1.9397
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology : A Comparison of Two Theoretical Models. July 2018.
Dutta-Bergman, M. J. (2004). Primary Sources of Health Information: Comparisons in the Domain of Health Attitudes, Health Cognitions, and Health Behaviors. Health Communication, 16(3), 273–288. https://doi.org/10.1207/S15327027HC1603_1
Dutta-Bergman, M. J. (2005). Developing a Profile of Consumer Intention to Seek Out Additional Information Beyond a Doctor: The Role of Communicative and Motivation Variables. Health Communication, 17(1), 1–16. https://doi.org/10.1207/s15327027hc1701_1
Dutta, M. J. (2007). Health Information Processing From Television: The Role of Health Orientation. Http://Dx.Doi.Org/10.1080/10410230701283256, 21(1), 1–9. https://doi.org/10.1080/10410230701283256
Erdoǧmuş, I. E., & Čiçek, M. (2011). Online group buying: What is there for the consumers? Procedia - Social and Behavioral Sciences, 24, 308–316. https://doi.org/10.1016/j.sbspro.2011.09.138
Goyette, I., Richard, L., Bergeron, J., & Marticotte, F. (2010). Word-of-mouth measurement scale for eservice context. Canadian Journal of Administrative Sciences, 27(1), 5–23.
Hair Jr et al. (2009). Multivariate Data Analysis. 1–761.
Handayani, P. W., Gelshirani, N. B., Azzahro, F., Pinem, A. A., & Hidayanto, A. N. (2020). The influence of argument quality, source credibility, and health consciousness on satisfaction, use intention, and loyalty on mobile health application use. Informatics in Medicine Unlocked, 20. https://doi.org/10.1016/j.imu.2020.100429
Hariyanti, D. (2020). Mengurai Tantangan Pengembangan Telemedis di Indonesia Artikel ini telah tayang di Katadata.co.id dengan judul “Mengurai Tantangan Pengembangan Telemedis di Indonesia” , https://katadata.co.id/saptopradityo/berita/5f5091d69ae59/mengurai-tantangan-pengemba. https://katadata.co.id/saptopradityo/berita/5f5091d69ae59/mengurai-tantangan-pengembangan-telemedis-di-indonesia
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing, 18(1), 38–52. https://doi.org/10.1002/dir.10073
Hsu, M. H., & Chiu, C. M. (2004). Internet self-efficacy and electronic service acceptance. Decision Support Systems, 38(3), 369–381. https://doi.org/10.1016/j.dss.2003.08.001
Ibrahim, A., Al-samarraie, H., Eldenfria, A., & Eva, J. (2020). Users’ intention to continue using mHealth services: A DEMATEL approach during the COVID-19 pandemic. January.
Ismagilova, E., Slade, E. L., Rana, N. P., & Dwivedi, Y. K. (2019). The Effect of Electronic Word of Mouth Communications on Intention to Buy: A Meta-Analysis. https://doi.org/10.1007/s10796-019-09924-y
Iversen, A. C., & Kraft, P. (2006). Does socio-economic status and health consciousness influence how women respond to health related messages in media? Health Education Research, 21(5), 601–610. https://doi.org/10.1093/HER/CYL014
KemKes. (2022). PPKM di Indonesia Resmi Dicabut. https://www.kemkes.go.id/article/view/22123100001/ppkm-di-indonesia-resmi-dicabut.html
Lin, H. F. (2007). Predicting consumer intentions to shop online: An empirical test of competing theories. Electronic Commerce Research and Applications, 6(4), 433–442. https://doi.org/10.1016/J.ELERAP.2007.02.002
Lin, J. S. C., & Hsieh, P. L. (2006). The role of technology readiness in customers’ perception and adoption of self-service technologies. International Journal of Service Industry Management, 17(5), 497–517. https://doi.org/10.1108/09564230610689795/FULL/XML
Lu, H.-H., Lin, W.-S., Raphael, C., & Wen, M.-J. (2022). A study investigating user adoptive behavior and the continuance intention to use mobile health applications during the COVID-19 pandemic era: Evidence from the telemedicine applications utilized in Indonesia. Asia Pacific Management Review. https://doi.org/10.1016/J.APMRV.2022.02.002
McLean, G., Al-Nabhani, K., & Wilson, A. (2018). Developing a Mobile Applications Customer Experience Model (MACE)- Implications for Retailers. Journal of Business Research, 85, 325–336. https://doi.org/10.1016/J.JBUSRES.2018.01.018
Meesala, A., & Paul, J. (2018). Service quality, consumer satisfaction and loyalty in hospitals: Thinking for the future. Journal of Retailing and Consumer Services, 40, 261–269. https://doi.org/10.1016/j.jretconser.2016.10.011
Mehyar, H., Saeed, M., Al-Ja’afreh, H. B. A., & Al-Adaileh, R. (2020). The impact of electronic word of mouth on consumers purchasing intention. Journal of Theoretical and Applied Information Technology, 98(2), 183–193.
Melorose, J., Perroy, R., & Careas, S. (2015). TRUST AND TAM IN ONLINE SHOPPING: AN INTEGRATED MODEL1 By: Statewide Agricultural Land Use Baseline 2015, 1(1), 51–90.
Meng, F., Guo, X., Peng, Z., Zhang, X., & Vogel, D. (2019a). The routine use of mobile health services in the presence of health consciousness. Electronic Commerce Research and Applications, 35, 100847. https://doi.org/https://doi.org/10.1016/j.elerap.2019.100847
Meng, F., Guo, X., Peng, Z., Zhang, X., & Vogel, D. (2019b). The routine use of mobile health services in the presence of health consciousness. Electronic Commerce Research and Applications, 35(April), 100847. https://doi.org/10.1016/j.elerap.2019.100847
Mohammad, M., Alam, D., Zahedul, M., & Abidur, S. (2020). Factors influencing mHealth adoption and its impact on mental well-beingduring COVID-19 pandemic: A SEM-ANN approach. January. https://doi.org/10.1016/j.jbi.2021.103722
Monaghesh, E., & Hajizadeh, A. (2020). The role of telehealth during COVID-19 outbreak: A systematic review based on current evidence. BMC Public Health, 20(1), 1–9. https://doi.org/10.1186/s12889-020-09301-4
Nguyen-Viet, H., Tuyet-Hanh, T. T., Unger, F., Dang-Xuan, S., & Grace, D. (2017). Food safety in Vietnam: Where we are at and what we can learn from international experiences. Infectious Diseases of Poverty, 6(1), 1–6. https://doi.org/10.1186/s40249-017-0249-7
Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers and Education, 109, 56–73. https://doi.org/10.1016/j.compedu.2017.02.005
Park, S. Y. A. analysis of the technology acceptance model in understanding university students’ behavioral intention to use. (2014). An analysis of the technology acceptance model in understanding students’ behavioral intention to use university’s social media. In Proceedings - 2014 IIAI 3rd International Conference on Advanced Applied Informatics, IIAI-AAI 2014 (Issue July 2009). https://doi.org/10.1109/IIAI-AAI.2014.14
Peña-García, N., Gil-Saura, I., Rodríguez-Orejuela, A., & Siqueira-Junior, J. R. (2020). Purchase intention and purchase behavior online: A cross-cultural approach. Heliyon, 6(6). https://doi.org/10.1016/j.heliyon.2020.e04284
Roggeveen, A. L., & Sethuraman, R. (2020). How the COVID-19 Pandemic May Change the World of Retailing. Journal of Retailing, 96(2), 169–171. https://doi.org/10.1016/j.jretai.2020.04.002
Smith, A. C., Thomas, E., Snoswell, C. L., Haydon, H., Mehrotra, A., Clemensen, J., & Caffery, L. J. (2020). Telehealth for global emergencies: Implications for coronavirus disease 2019 (COVID-19). Journal of Telemedicine and Telecare, 26(5), 309–313. https://doi.org/10.1177/1357633X20916567
Tojib, D., & Tsarenko, Y. (2012). Post-adoption modeling of advanced mobile service use. Journal of Business Research, 65(7), 922–928. https://doi.org/10.1016/j.jbusres.2011.05.006
Tsarenko, Y., & Strizhakova, Y. (2013). Coping with service failures. European Journal of Marketing, 47(1/2), 71–92. https://doi.org/10.1108/03090561311285466
Turbat, B., Sharavyn, B., & Tsai, F. J. (2022). Attitudes towards mandatory occupational vaccination and intention to get COVID-19 vaccine during the first pandemic wave among Mongolian healthcare workers: A cross-sectional survey. International Journal of Environmental Research and Public Health, 19(1). https://doi.org/10.3390/ijerph19010329
Venkatesh, V., & Davis, F. D. (2000). Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
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. https://doi.org/10.2307/41410412
Walrave, M., Waeterloos, C., & Ponnet, K. (2020). Adoption of a contact tracing app for containing COVID-19: A health belief model approach. JMIR Public Health and Surveillance, 6(3), 1–10. https://doi.org/10.2196/20572
Wang, W. T., Ou, W. M., & Chen, W. Y. (2019). The impact of inertia and user satisfaction on the continuance intentions to use mobile communication applications: A mobile service quality perspective. International Journal of Information Management, 44(October 2018), 178–193. https://doi.org/10.1016/j.ijinfomgt.2018.10.011
Warganegara, D. L., & Hendijani, R. B. (2022). Factors That Drive Actual Purchasing of Groceries through E-Commerce Platforms during COVID-19 in Indonesia. Sustainability (Switzerland), 14(6), 1–22. https://doi.org/10.3390/su14063235
Wu, P., Zhang, R., Zhu, X., & Liu, M. (2022). Factors Influencing Continued Usage Behavior on Mobile Health Applications. Healthcare (Switzerland), 10(2). https://doi.org/10.3390/healthcare10020208
Xiao, N., Sharman, R., Rao, H. R., & Upadhyaya, S. (2014). Factors influencing online health information search: An empirical analysis of a national cancer-related survey. Decision Support Systems, 57, 417–427. https://doi.org/https://doi.org/10.1016/j.dss.2012.10.047
Yan, M., Filieri, R., Raguseo, E., & Gorton, M. (2021). Mobile apps for healthy living: Factors influencing continuance intention for health apps. Technological Forecasting and Social Change, 166, 120644. https://doi.org/10.1016/J.TECHFORE.2021.120644
Zhang, Xi, 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
Zhang, Xiaofei, 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. https://doi.org/10.1080/17538157.2016.1200053