Applying UTAUT Model to Understand Use of Behavior Health Applications User in Indonesia During the COVID-19

##plugins.themes.bootstrap3.article.main##

Gugun Geusan Akbar
Muchtar
Aji Abdul Wahid
Ikeu Kania
Annisa Deliana Putri
Dini Turipanam Alamanda

Abstract

This study examines the adoption of the PeduliLindungi application during the COVID-19 pandemic in Indonesia using the UTAUT paradigm. The data was submitted by 200 users of the PeduliLindungi app. SEM-PLS was used to analyze the relationships between the variables and assess several hypotheses. To enlarge the scope of the investigation, Importance-Performance Map Analysis (IPMA) and Multi-Group Analysis (MGA) were also looked at as moderating variables. The findings revealed that facilitating settings had no discernible impact on how people use the PeduliLindungi application, whereas performance expectation, effort expectation, and social influence all positively affect behavioral intention and usage behavior. Second, the proposed model was not significantly impacted by gender, age, or educational attainment. Third, social influence is more significant in behavioral intention but less significant in use behavior components, performance expectation is more significant in use behavior, and effort expectancy is more prominent than other dimensions in behavioral intention. The contribution of this research is to provide a broader perspective in understanding the adoption of PeduliLindungi application so that it can convey a review of the policy implications for technology adoption in Indonesia.

##plugins.themes.bootstrap3.article.details##

How to Cite
Akbar, G. G., Muchtar, Aji Abdul Wahid, Ikeu Kania, Annisa Deliana Putri, & Dini Turipanam Alamanda. (2023). Applying UTAUT Model to Understand Use of Behavior Health Applications User in Indonesia During the COVID-19. Jurnal Ilmu Administrasi: Media Pengembangan Ilmu Dan Praktek Administrasi, 20(1), 29–45. https://doi.org/10.31113/jia.v20i1.900


Section
Articles

References

Akar, E., & Mardikyan, S. (2014). Analyzing factors affecting users ’ behavior intention to use social media : Twitter. International Journal of Business and Social Science, 5(11), 85–95.

Akbar, G. G., Kania, I., Ulumudin, A., Anggadwita, G., Harmanto, L. S., & Alamanda, D. T. (2019). Innovation in the Public Sector: The effectiveness of “LAPOR!†as one of the Smart City Programs in Bandung. Proceedings of the International Symposium on Social Sciences, Education, and Humanities (ISSEH 2018). https://doi.org/10.2991/isseh-18.2019.69

Al-Maroof, R. S., Salloum, S. A., Hassanien, A. E., & Shaalan, K. (2020). Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic. Interactive Learning Environments, 1–16. https://doi.org/10.1080/10494820.2020.1830121

Albanna, H., Alalwan, A. A., & Al-Emran, M. (2022). An integrated model for using social media applications in non-profit organizations. International Journal of Information Management, 63, 102452. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2021.102452

Allah Pitchay, A., Ganesan, Y., Zulkifli, N. S., & Khaliq, A. (2022). Determinants of customers’ intention to use online food delivery application through smartphone in Malaysia. British Food Journal, 124(3), 732–753. https://doi.org/10.1108/BFJ-01-2021-0075

Aminullah, E., & Erman, E. (2021). Policy innovation and emergence of innovative health technology: The system dynamics modelling of early COVID-19 handling in Indonesia. Technology in Society, 66(July), 101682. https://doi.org/10.1016/j.techsoc.2021.101682

Andone, I., Blaszkiewicz, K., Eibes, M., Trendafilov, B., Markowetz, A., & Montag, C. (2016). How age and gender affect smartphone usage. UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, September, 9–12. https://doi.org/10.1145/2968219.2971451

Bai, B., & Guo, Z. (2022). Understanding Users’ Continuance Usage Behavior Towards Digital Health Information System Driven by the Digital Revolution Under COVID-19 Context: An Extended UTAUT Model. Psychology Research and Behavior Management, Volume 15(September), 2831–2842. https://doi.org/10.2147/prbm.s364275

Carranza, R., Díaz, E., Martín-Consuegra, D., & Fernández-Ferrín, P. (2020). PLS–SEM in business promotion strategies. A multigroup analysis of mobile coupon users using MICOM. Industrial Management & Data Systems, 120(12), 2349–2374. https://doi.org/10.1108/IMDS-12-2019-0726

Chin, W. W., Peterson, R. A., & Brown, S. P. (2008). Structural Equation Modeling in Marketing: Some Practical Reminders. Journal of Marketing Theory and Practice, 16(4), 287–298. https://doi.org/10.2753/MTP1069-6679160402

Eutsler, L. (2018). Parents’ mobile technology adoption influences on elementary children’s use. The International Journal of Information and Learning Technology, 35(1), 29–42. https://doi.org/10.1108/IJILT-05-2017-0035

Ferreira, D., Dey, A. K., & Kostakos, V. (2011). Understanding Human-Smartphone Concerns: A Study of Battery Life BT - Pervasive Computing (K. Lyons, J. Hightower, & E. M. Huang (eds.); pp. 19–33). Springer Berlin Heidelberg.

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Head, B. W. (2010). Reconsidering evidence-based policy: Key issues and challenges. Policy and Society, 29(2), 77–94. https://doi.org/https://doi.org/10.1016/j.polsoc.2010.03.001

Henseler, J., Ringle, C. M., & 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

Herdiana, D. (2021). Aplikasi PeduliLindungi : Perlindungan Masyarakat Dalam Mengakses Fasilitas Publik Di Masa Pemberlakuan Kebijakan PPKM. Jurnal Inovasi Penelitian, 2(6), 1685–1694. https://doi.org/https://doi.org/10.47492/jip.v2i6.959

Hooda, A., Gupta, P., Jeyaraj, A., Giannakis, M., & Dwivedi, Y. K. (2022). The effects of trust on behavioral intention and use behavior within e-government contexts. International Journal of Information Management, 67, 102553. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2022.102553

Istiqoh, A. E., Nurmandi, A., Muallidin, I., Loilatu, M. J., & Kurniawan, D. (2023). The Successful Use of the PeduliLindungi Application in Handling COVID-19 (Indonesian Case Study) BT - Proceedings of Seventh International Congress on Information and Communication Technology (X.-S. Yang, S. Sherratt, N. Dey, & A. Joshi (eds.); pp. 353–363). Springer Nature Singapore.

Katoch, R., & Rana, A. (2023). Online spiritual meets (OSMs) and user behavior – A divine application of technology during COVID-19. Computers in Human Behavior, 139, 107514. https://doi.org/https://doi.org/10.1016/j.chb.2022.107514

Kock, N. (2014). One-tailed or two-tailed P values in PLS-SEM ? One-tailed or two-tailed P values in PLS-SEM ? ScriptWarp Systems . International Journal of E-Collaboration, 11(December), 1–7. https://doi.org/10.13140/2.1.3788.1929

Liu, L., & Miguel-Cruz, A. (2022). Technology adoption and diffusion in healthcare at onset of COVID-19 and beyond. Healthcare Management Forum, 35(3), 161–167. https://doi.org/10.1177/08404704211058842

Miranda Difini, G., Martins, M. G., & Barbosa, J. L. V. (2022). A Movement Analysis Application Using Human Pose Estimation and Action Correction. Proceedings of the Brazilian Symposium on Multimedia and the Web, 359–367. https://doi.org/10.1145/3539637.3557931

Putri, C. E., & Hamzah, R. E. (2021). APLIKASI PEDULILINDUNGI MITIGASI BENCANA COVID-19 DI INDONESIA. PUSTAKA KOMUNIKASI, 4(1), 66–78.

Ringle, C. M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results. Industrial Management & Data Systems, 116(9), 1865–1886. https://doi.org/10.1108/IMDS-10-2015-0449

Shahbaz, M., Gao, C., Zhai, L., Shahzad, F., & Arshad, M. R. (2020). Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare. Complexity, 2020. https://doi.org/10.1155/2020/2173765

Srinivasa Rao, A. S. R., & Vazquez, J. A. (2020). Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey when cities and towns are under quarantine. Infection Control and Hospital Epidemiology, 41(7), 826–830. https://doi.org/10.1017/ice.2020.61

Venkatesh, V., & Smith, R. H. (2003). User Acceptance of Information Technology: Toward a Unified View. Inorganic Chemistry Communications, 27(3). https://doi.org/10.2307/30036540

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

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428

Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206–215. https://doi.org/https://doi.org/10.1016/j.im.2006.12.005

Wei, M. F., Luh, Y. H., Huang, Y. H., & Chang, Y. C. (2021). Young generation’s mobile payment adoption behavior: Analysis based on an extended utaut model. Journal of Theoretical and Applied Electronic Commerce Research, 16(4), 1–20. https://doi.org/10.3390/jtaer16040037

Wu, X., Ramesh, M., & Howlett, M. (2018). Policy Capacity and Governance. Policy Capacity and Governance, 1–25. https://doi.org/10.1007/978-3-319-54675-9

Wut, T. M., Lee, S. W., & Xu, J. (. (2022). How do Facilitating Conditions Influence Student-to-Student Interaction within an Online Learning Platform? A New Typology of the Serial Mediation Model. In Education Sciences (Vol. 12, Issue 5). https://doi.org/10.3390/educsci12050337

Yoebrilianti, A., Nurhyani, N., & Ikhsan, K. (2022). M-Payment and Covid-19: Understanding the Determinants of Consumers Adopting and Recommending Digital Payment System. Jurnal Manajemen Dan Kewirausahaan, 10(1), 58–70. https://doi.org/10.26905/jmdk.v10i1.6614

Zeebaree, M., Agoyi, M., & Aqel, M. (2022). Sustainable Adoption of E-Government from the UTAUT Perspective. In Sustainability (Vol. 14, Issue 9). https://doi.org/10.3390/su14095370