What Drives Continuance Intention to Use Public Transportation Apps? Extending UTAUT2 for Post-Adoption Behavior

Murniasih Murniasih; Nurdin Sobari

doi:10.59261/inkubis.v8i3.403

Authors

Murniasih Murniasih Universitas Indonesia
Nurdin Sobari Universitas Indonesia

DOI:

https://doi.org/10.59261/inkubis.v8i3.403

Keywords:

Continuance Intention, Public Transportation Application, Perceived Trust, Perceived Risk, Perceived Trendiness, Transjakarta, UTAUT2

Abstract

Background: Digital transformation has led public transportation providers to develop mobile applications to improve service accessibility and operational efficiency. While continuance intention research has grown across digital service contexts, evidence on the factors driving sustained use of public transportation apps remains limited and inconclusive.

Objective: This study aims to examine the factors influencing continuance intention to use the TJ: Transjakarta app by extending the UTAUT2 through the integration of perceived trust, perceived risk, and perceived trendiness, and the mediating role of habit.

Methods: A quantitative approach was employed using a survey of 349 TJ: Transjakarta users. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).

Results: Habit was the strongest predictor of continuance intention, followed by perceived trendiness, perceived trust, and price value. Perceived risk negatively influenced continuance intention, while performance expectancy, effort expectancy, and social influence had no direct significant effects. Furthermore, price value positively influenced habit, and habit significantly mediated the relationship between price value and continuance intention to use.

Conclusion: Continued use of the TJ: Transjakarta app is shaped not only by utilitarian factors but also by behavioral and psychological mechanisms. These findings, contextualized within a single public transportation application, support the value of integrating perceived trust, perceived risk, and perceived trendiness into UTAUT2 to explain post-adoption behavior; generalization to other app contexts should be approached with caution.

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References

Abbasi, G. A., Goh, Y. N., Iranmanesh, M., & Liebana-Cabanillas, F. (2024). Determinants of continuous intention to use retail apps: A hybrid PLS-ANN approach. Journal of Marketing Theory and Practice, 32(3), 413–434. https://doi.org/10.1080/10696679.2023.2218098

Abdul-Halim, N.-A., Vafaei-Zadeh, A., Hanifah, H., Teoh, A. P., & Nawaser, K. (2022). Understanding the determinants of e-wallet continuance usage intention in Malaysia. Quality & Quantity, 56(5), 3413–3439. https://doi.org/10.1007/s11135-021-01276-7

Acheampong, R. A., Siiba, A., Okyere, D. K., & Tuffour, J. P. (2020). Mobility-on-demand: An empirical study of internet-based ride-hailing adoption factors, travel characteristics and mode substitution effects. Transportation Research Part C: Emerging Technologies, 115, 102638. https://doi.org/10.1016/j.trc.2020.102638

Agnihotri, P., & Chen, S. (2026). Exploring the determinants of continued bicycle-sharing usage: insights into habit, user experience, and health benefits through a technology continuance theory lens. Transportation Research Interdisciplinary Perspectives, 37, 101994. https://doi.org/10.1016/j.trip.2026.101994

Ahmed, W., Al-Sharafi, M. A., Raza, A., Al-Zaeemi, S. A. S., Al-Bashrawi, M. A., & Dwivedi, Y. K. (2026). A longitudinal big data approach to theorizing consumers’ continuance intention to use loyalty apps. Journal of Retailing and Consumer Services, 88, 104453. https://doi.org/10.1016/j.jretconser.2025.104453

Al Mulhem, A. A. (2025). A model for sustainable mobile education beyond the COVID-19 pandemic. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1657635

Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Algharabat, R. (2018). Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk. Journal of Retailing and Consumer Services, 40, 125–138. https://doi.org/10.1016/j.jretconser.2017.08.026

Back, S., Shon, M., & Hwang, J. (2025). Evaluating Korean commuters’ acceptance of MaaS through the UTAUT framework. Case Studies on Transport Policy, 20, 101464. https://doi.org/10.1016/j.cstp.2025.101464

Badi, S., Khoury, S., Yasin, K., & Al Marri, K. (2026). The role of pro-environment and pro-technology attitudes in MaaS adoption in the UAE: An extended UTAUT2 model. Research in Transportation Business & Management, 64, 101556. https://doi.org/10.1016/j.rtbm.2025.101556

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model1. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921

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

Chiu, C.-M., Hsu, M.-H., Lai, H., & Chang, C.-M. (2012). Re-examining the influence of trust on online repeat purchase intention: The moderating role of habit and its antecedents. Decision Support Systems, 53(4), 835–845. https://doi.org/10.1016/j.dss.2012.05.021

Cho, J., & Erin Lee, H. (2017). Contextualization of motivations determining the continuance intention to use smart devices among people with physical disabilities. Telematics and Informatics, 34(1), 338–350. https://doi.org/10.1016/j.tele.2016.05.011

Deepa, M., & Venkatesan, D. (2026). Equity and accessibility in digital service applications: an extended UTAUT2 analysis of users’ continuous intention. Frontiers in Sustainable Cities, 8. https://doi.org/10.3389/frsc.2026.1777207

Enache, A., Friberg, R., & Wiklander, M. (2024). Customer Lifetime Value Applied to Mobile Apps <br> https://doi.org/10.2139/ssrn.4928535

Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3

Fu, H., Mensah, I. K., Wang, R., Gui, L., Wang, J., & Xiao, Z. (2024). The predictors of mobile government services adoption through social media: A case of Chinese citizens. Information Development, 40(3), 376–397. https://doi.org/10.1177/02666669221114649

Gao, W., Jiang, N., & Guo, Q. (2025). Facial recognition payment is cool: coolness, inspiration, and customer continuance intention to use facial recognition payment. Financial Innovation, 11(1), 31. https://doi.org/10.1186/s40854-024-00697-1

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in Online Shopping: An Integrated Model1. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519

Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.

Hasselwander, M. (2025). Who wants to use transport super apps? Insights from combined PLS-SEM and NCA methods. Transportation Research Part F: Traffic Psychology and Behaviour, 113, 1–13. https://doi.org/10.1016/j.trf.2025.04.018

He, M., Qin, J., Wen, M., & Chen, W. (2021). Sustaining Consumer Trust and Continuance Intention by Institutional Mechanisms: An Empirical Survey of DiDi in China. IEEE Access, 9, 158185–158203. https://doi.org/10.1109/ACCESS.2021.3130890

Herzallah, F., Abosamaha, A. J., Abu-Siam, Y., Amer, M., Sajjad, U., & Hamid, K. (2025). Determinants of mobile wallet usage among Gen Z: Extending the UTAUT2 model with moderating effects of personal innovativeness and gender. International Journal of Information Management Data Insights, 5(1), 100336. https://doi.org/10.1016/j.jjimei.2025.100336

Ho, C. Q., & Tirachini, A. (2024). Mobility-as-a-Service and the role of multimodality in the sustainability of urban mobility in developing and developed countries. Transport Policy, 145, 161–176. https://doi.org/10.1016/j.tranpol.2023.10.013

Javadinasr, M., Asgharpour, S., Rahimi, E., Choobchian, P., Mohammadian, A. K., & Auld, J. (2022). Eliciting attitudinal factors affecting the continuance use of E-scooters: An empirical study in Chicago. Transportation Research Part F: Traffic Psychology and Behaviour, 87, 87–101. https://doi.org/10.1016/j.trf.2022.03.019

Jiménez-Barreto, J., Loureiro, S. M. C., Rubio, N., & Romero, J. (2022). Service brand coolness in the construction of brand loyalty: A self-presentation theory approach. Journal of Retailing and Consumer Services, 65, 102876. https://doi.org/10.1016/j.jretconser.2021.102876

Kilani, A. A.-H., Kakeesh, D. F., Al-Weshah, G. A., & Al-Debei, M. M. (2023). Consumer post-adoption of e-wallet: An extended UTAUT2 perspective with trust. Journal of Open Innovation: Technology, Market, and Complexity, 9(3), 100113. https://doi.org/10.1016/j.joitmc.2023.100113

Kopplin, C. S., Brand, B. M., & Reichenberger, Y. (2021). Consumer acceptance of shared e-scooters for urban and short-distance mobility. Transportation Research Part D: Transport and Environment, 91, 102680. https://doi.org/10.1016/j.trd.2020.102680

Lembcke, T.-B., Herrenkind, B., Nastjuk, I., & Brendel, A. B. (2021). Promoting Business Trip Ridesharing with Green Information Systems: A Blended Environment Perspective. Transportation Research Part D: Transport and Environment, 94, 102795. https://doi.org/10.1016/j.trd.2021.102795

Li, X., Wang, C., & Ma, Z. (2026). Influencing factors of user acceptance and adoption of e-government AI assistants: An empirical study based on the extended UTAUT2 model. Telematics and Informatics Reports, 21, 100293. https://doi.org/10.1016/j.teler.2026.100293

Liu, J. Y. W., Sorwar, G., Rahman, M. S., & Hoque, M. R. (2023). The role of trust and habit in the adoption of mHealth by older adults in Hong Kong: A healthcare technology service acceptance (HTSA) model. BMC Geriatrics, 23(1), 73. https://doi.org/10.1186/s12877-023-03779-4

Liébana-Cabanillas, F., Molinillo, S., & Ruiz-Montañez, M. (2019). To use or not to use, that is the question: Analysis of the determining factors for using NFC mobile payment systems in public transportation. Technological Forecasting and Social Change, 139, 266–276. https://doi.org/10.1016/j.techfore.2018.11.012

Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance1. MIS Quarterly, 31(4), 705–737. https://doi.org/10.2307/25148817

Lu, K., Hu, J., Shang, T., & Xu, Y. (2026). To be integrated or not? Understanding continuance behavioral intention towards integrated ride-hailing services: Empirical evidence from Nanjing, China. Sustainable Cities and Society, 137, 107159. https://doi.org/10.1016/j.scs.2026.107159

Madaki, A. S., Ahmad, K., & Singh, D. (2024). IT integration implementation in e-government public sector in developing countries: A systematic literature review and model development. Transforming Government: People, Process and Policy, 18(3), 451–472. https://doi.org/10.1108/TG-02-2024-0043

Qatanani, N. O., Alghababsheh, M., Aburayya, A., & Nasaj, M. (2026). Factors influencing m-government adoption in the developing world: a UTAUT-based model integrating trust, risk and trendiness. VINE Journal of Information and Knowledge Management Systems, 56(3), 797–828. https://doi.org/10.1108/VJIKMS-10-2023-0255

PT Transportasi Jakarta. (2025). Aplikasi TJ: Transjakarta Bawa Pulang Penghargaan Indonesia Digital Sustainability Awards 2025. https://transjakarta.co.id/berita/aplikasi-tj-transjakarta-bawa-pulang-penghargaan-indonesia-digital-sustainability-awards-2025

PT Transportasi Jakarta. (2026). Transjakarta Cetak Rekor: Layani 413 Juta Pelanggan di 2025 dengan Efisiensi Subsidi yang Signifikan. https://transjakarta.co.id/berita/transjakarta-cetak-rekor-layani-413-juta-pelanggan-di-2025-dengan-efisiensi-subsidi-yang-signifikan

Ramadan, E., Abdalla, S., & Elshaiekh, N. E. (2025). Evaluating public insight on smartphone applications for sustainable urban mobility in the GCC States. Transportation Research Interdisciplinary Perspectives, 30, 101377. https://doi.org/10.1016/j.trip.2025.101377

Rasheed Gaber, H., & Elsamadicy, A. M. (2021). What drives customers to continue using ride-sharing apps during the COVID-19 pandemic? The case of Uber in Egypt. Cogent Business & Management, 8(1). https://doi.org/10.1080/23311975.2021.1944009

Rymang, A. A., Sihombing, P. E., Naurah Anvilen, R. A., Handayani, P. W., & Harahap, N. C. (2026). User intention model on Indonesian High-Speed railway mobile application. Transportation Research Interdisciplinary Perspectives, 36, 101855. https://doi.org/10.1016/j.trip.2026.101855

Sasongko, D. T., Handayani, P. W., & Satria, R. (2022). Analysis of factors affecting continuance use intention of the electronic money application in Indonesia. Procedia Computer Science, 197, 42–50. https://doi.org/10.1016/j.procs.2021.12.116

Savitha, B., Hawaldar, I. T., & Kumar K, N. (2022). Continuance intentions to use FinTech peer-to-peer payments apps in India. Heliyon, 8(11), e11654. https://doi.org/10.1016/j.heliyon.2022.e11654

Shaikh, A. A., Alamoudi, H., Alharthi, M., & Glavee-Geo, R. (2023). Advances in mobile financial services: a review of the literature and future research directions. International Journal of Bank Marketing, 41(1), 1–33. https://doi.org/10.1108/IJBM-06-2021-0230

Sharma, N., & Fatima, J. K. (2024). Influence of perceived value on omnichannel usage: Mediating and moderating roles of the omnichannel shopping habit. Journal of Retailing and Consumer Services, 77, 103627. https://doi.org/10.1016/j.jretconser.2023.103627

Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189

Song, Y., Natori, T., & Yu, X. (2024). Tracing the Evolution of E-Government: A Visual Bibliometric Analysis from 2000 to 2023. Administrative Sciences, 14(7), 133. https://doi.org/10.3390/admsci14070133

Statista. (2025). Retention rate on day 30 of mobile Android app installs worldwide in 3rd quarter 2024, by category. https://www.statista.com/statistics/259329/ios-and-android-app-user-retention-rate/

Sthapit, E., Del Chiappa, G., Coudounaris, D. N., & Bjork, P. (2019). Determinants of the continuance intention of Airbnb users: consumption values, co-creation, information overload and satisfaction. Tourism Review, 75(3), 511–531. https://doi.org/10.1108/TR-03-2019-0111

Tamilmani, K., Rana, N. P., Wamba, S. F., & Dwivedi, R. (2021). The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): A systematic literature review and theory evaluation. International Journal of Information Management, 57, 102269. https://doi.org/10.1016/j.ijinfomgt.2020.102269

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view1. MIS Quarterly, 27(3), 425–478. 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 Technology1. MIS Quarterly, 36(1), 157–178.

Veronika, S., Lee, M. S. W., Lang, B., & Putra, P. (2025). A systematic review and future agenda on continuance intentions in mobile apps. International Journal of Information Management Data Insights, 5(2), 100352. https://doi.org/10.1016/j.jjimei.2025.100352

Wang, T., Wang, W., Liang, J., Nuo, M., Wen, Q., Wei, W., Han, H., & Lei, J. (2022). Identifying major impact factors affecting the continuance intention of mHealth: a systematic review and multi-subgroup meta-analysis. Npj Digital Medicine, 5(1), 145. https://doi.org/10.1038/s41746-022-00692-9

Wu, P., Zhang, R., Zhu, X., & Liu, M. (2022). Factors Influencing Continued Usage Behavior on Mobile Health Applications. Healthcare, 10(2), 208. https://doi.org/10.3390/healthcare10020208

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

Zaid, K., A. A.-H., Kakeesh, D. F., Al-Weshah, G. A., & Al-Debei, M. M. (2023). Consumer post-adoption of e-wallet: An extended UTAUT2 perspective with trust. Journal of Open Innovation: Technology, Market, and Complexity, 9(3), 100113. https://doi.org/10.1016/j.joitmc.2023.100113