Continue Use Intention Analysis Using The Integration of The Unified Theory of Acceptance and Use of Technology (UTAUT) 2 and Delone & Mclean (D&M) Models Modified in The My TelU Mobile Student Account Application

Singgih   Darmawan; Rina Djunita   Rina Djunita Pasaribu

doi:10.46729/ijstm.v5i5.1182

Singgih Darmawan Economic and Business Faculty, Telkom University Bandung, Indonesia
Rina Djunita Rina Djunita Pasaribu Economic and Business Faculty, Telkom University Bandung, Indonesia

DOI: https://doi.org/10.46729/ijstm.v5i5.1182

Keywords: Super App, Unified Theory of Acceptance and Use of Technology (UTAUT) 2, Delone & McLean (D&M), Partial Least Square (PLS)

Abstract

My TelU Mobile is a learning support application released by Telkom University on May 25, 2021. This application can be downloaded on the Play Store and App Store and has become a super app that has quite a high number of downloads. However, its user traffic is not yet stable, as seen in the decreasing usage traffic during the lecture period. The purpose of this study was to determine the factors that influence the acceptance and use of the My TelU Mobile application using an integration of the UTAUT-2 and Delone & McLean models. This study uses a quantitative method with descriptive and causal research types. The sampling technique uses a non-probability sampling technique of the purposive sampling type with a sample size of 400 respondents. The data analysis technique uses descriptive analysis and PLS. The findings of this study are that effort expectancy, information quality, service quality, user satisfaction have a significant effect on continue use intention and system quality, information quality, service quality have a significant effect on user satisfaction. However, performance expectancy, social influence, facilitating condition, habit, hedonic motivation, and system quality do not have a significant effect on continue use intention of My TelU Mobile application.

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References

We Are Social And Hootsuite (2023). Digital 2023 Indonesia : The Essentsial Guide to The Latest Connected Behaviors. (Diakses tanggal 5 Januari 2024 dari https://wearesocial.com/id/blog/2023/01/digital-2023/)

Statista (2023). Market Share of Mobile Operating System in Indonesia from January 2019 to August 2023 (Diakses tanggal 5 Januari 2024 dari https://www.statista.com/statistics/262205/market-share-held-by-mobile-operating-systems-in-indonesia//)

Forbes (2022). The Journey of A Super App Through The Lens of Uptime and Experience (Diakses tanggal 5 Januari 2024 dari https://www.forbes.com/sites/forbestechcouncil/2022/11/30/the-journey-of-a-super-app-through-the-lens-of-uptime-and-experience/?sh=4ef78fda7d79)

Aksara (2021). Mengulas Aplikasi My TelU (Diakses tanggal 5 Januari 2024 dari https://aksarapers.com/mengulas-aplikasi-my-tel-u/)

Jogiyanto, H.M. (2007). Sistem Informasi Keperilakuan. Yogyakarta: Andi Offset.

Haryono. (2017). Metode SEM untuk Penelitian Manajemen AMOS LISREL PLS. Jakarta Timur: Luxima.

Eneh, A. H., Udanor, C. N., Ossai, N. I., Aneke, S. O., Ugwoke, P. O., Obayi, A. A., Ugwuishiwu, C. H., & Okereke, G. E. (2023). Towards an improved internet of things sensors data quality for a smart aquaponics system yield prediction. MethodsX, 11. https://doi.org/10.1016/j.mex.2023.102436

Engel, D. W., Dalton, A. C., Anderson, K. K., Sivaramakrishnan, C., & Lansing, C. (2012). Development of Technology Readiness Level (TRL) Metrics and Risk Measures (PNNL-21737). Pacific Northwest National Lab. (PNNL), Richland, WA (United States). https://doi.org/10.2172/1067968

Hemal, M. M., Rahman, A., Nurjahan, Islam, F., Ahmed, S., Kaiser, M. S., & Ahmed, M. R. (2024). An Integrated Smart Pond Water Quality Monitoring and Fish Farming Recommendation Aquabot System. Sensors, 24(11),. https://doi.org/10.3390/s24113682

Islam, M., Kashem, M., Alyami, S., & Moni, M. (2023). Monitoring water quality metrics of ponds with IoT sensors and machine learning to predict fish species survival. Microprocessors and Microsystems, 102, 104930. https://doi.org/10.1016/j.micpro.2023.104930

Jiang, Y., & Yan, F. (2022). Aquaculture Prediction Model Based on Improved Water Quality Parameter Data Prediction Algorithm under the Background of Big Data. Journal of Applied Mathematics, 2022(1), 2071360.

Maricar, M. A. (2019). Analisa Perbandingan Nilai Akurasi Moving Average dan Exponential Smoothing untuk Sistem Peramalan Pendapatan pada Perusahaan XYZ. Jurnal Sistem Dan Informatika (JSI), 13(2).

Mueller, J. P., & Massaron, L. (2021). Artificial Intelligence For Dummies. John Wiley & Sons.

Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Ben Taieb, S., Bergmeir, C., Bessa, R. J., Bijak, J., Boylan, J. E., Browell, J., Carnevale, C., Castle, J. L., Cirillo, P., Clements, M. P., Cordeiro, C., Cyrino Oliveira, F. L., De Baets, S., Dokumentov, A., … Ziel, F. (2022). Forecasting: Theory and practice. International Journal of Forecasting, 38(3), 705–871. https://doi.org/10.1016/j.ijforecast.2021.11.001