Factors influencing the continued intention to use mobile payment among generation Z: An extension of the expectation-confirmation model

Authors

  • Fenylia Nurshakira Putri Kusuma Department of Management, Faculty of Economics and Business, Universitas Indonesia, 16424, Depok, Indonesia
  • Riani Rachmawati Department of Management, Faculty of Economics and Business, Universitas Indonesia, 16424, Depok, Indonesia

DOI:

https://doi.org/10.61511/ghde.v2i1.2025.1965

Keywords:

mobile payment, UTAUT2, ECM, Generation Z, continued usage intention

Abstract

Background: The fintech industry in Indonesia has been growing rapidly, driven by digitalization acceleration during the pandemic and positive funding trends in the ASEAN fintech sector. As a developing country with a high unbanked population, mobile payment (m-payment) adoption has the potential to support financial inclusion by providing easy access to affordable and beneficial financial services. This study integrates the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Expectation-Confirmation Model (ECM) to understand the factors significantly influencing the continued usage intention of m-payment among Generation Z, a key potential customer segment in Indonesia. Methods: This research employs a quantitative approach by distributing structured questionnaires to Generation Z respondents. Structural Equation Modeling (SEM) was utilized to analyze the relationships between the variables. The key factors examined include Habit, Hedonic Motivation, Satisfaction, Facilitating Conditions, Performance Expectancy, Effort Expectancy, and Confirmation. Findings: The results reveal that continued intention to use m-payment is positively influenced by Habit, Hedonic Motivation, Satisfaction, and Facilitating Conditions. Furthermore, Performance Expectancy indirectly affects continued intention through the mediating role of Satisfaction. Additionally, Performance Expectancy mediates the relationship between Effort Expectancy and Confirmation with Satisfaction. Conclusion: The study highlights the crucial factors that drive the sustained use of m-payment among Generation Z in Indonesia. Understanding these factors is essential for financial service providers to enhance adoption and engagement. Novelty/Originality of this article: This study extends ECM by incorporating UTAUT2 constructs to provide a comprehensive understanding of continued m-payment adoption. The findings contribute to the literature on fintech adoption by offering empirical evidence from an emerging market perspective.

References

Boston Consulting Group. (2020). Southeast Asian consumers are driving a digital payment revolution. Boston Consulting Group.

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

Collis, J., & Hussey, R. (2013). Business research: A practical guide for undergraduate and postgraduate students. Palgrave Macmillan.

Dimock, M. (2019). Defining generations: Where millennials end and Generation Z begins. Pew Research Center, 17(1), 1–7. https://www.pewresearch.org/fact-tank/2019/01/17/where-millennials-end-and-generation-z-begins

Gen Z Social Media User Stats (2020–2025). (2023). Insider Intelligence. Insiderintelligence.com

United Overseas Bank. (2022). ASEAN Fintech Report 2022. UOB Group.

Hayashi, F., & Bradford, T. (2014). Mobile payments: Merchants’ perspectives. Economic Review, Second Quarter, 33–58. https://www.kansascityfed.org/documents/1374/2014

Ghozali, I., & Laten. (2015). Partial Least Square: Konsep, Teknik Dan Aplikasi Menggunkam Program Smart Pls 3.0 (2nd Ed). Universitas Diponegoro

Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis: A global perspective. Pearson Education International.

Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). An introduction to structural equation modeling. In Partial least squares structural equation modeling (PLS-SEM) using R (Classroom Companion: Business). Springer. https://doi.org/10.1007/978-3-030-80519-7_1

Hair, J., & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027

Hsu, C.-L., & Lin, J. C.-C. (2015). What drives purchase intention for paid mobile apps? An expectation-confirmation model with perceived value. Electronic Commerce Research and Applications, 14(1), 46–57. https://doi.org/10.1016/j.elerap.2014.11.003

Kline, R. B. (2005). Principle and practice of structural equation modeling (2nd ed.). Guilford Press.

Kim, Y., & Crowston, K. (2012). Technology adoption and use theory review for studying scientists' continued use of cyber-infrastructure. Proceedings of the American Society for Information Science and Technology. https://doi.org/10.1002/meet.2011.14504801197

Loehlin, J. C. (1998). Latent variable models: An introduction to factor, path, and structural analysis. Lawrence Erlbaum Associates.

Lisana, L. (2021). Factors influencing the adoption of mobile payment systems in Indonesia. International Journal of Web Information Systems, 17(3), 1–10. https://doi.org/10.1108/IJWIS-01-2021-0004

Lisana, L. (2022). Understanding the key drivers in using mobile payment among Generation Z. Journal of Science and Technology Policy Management. https://doi.org/10.1108/JSTPM-08-2021-0118

MDI & Mandiri Sekuritas. (2019). Mobile payments in Indonesia: Race to big data domination. Retrieved February 1, 2023, from www.mdi.vc/mobilepaymentindonesia.pdf

Lin, H. H., & Wang, Y. S. (2006). An examination of the determinants of customer loyalty in mobile commerce contexts. Information & Management, 43(3), 271–282. http://dx.doi.org/10.1016/j.im.2005.08.001

Malhotra, N., & Birks, D. (2007). Marketing research: An applied approach (3rd ed.). Pearson Education.

McKinsey & Company. (2022). Mind the gap: Playing the Gen Z game. McKinsey.com

Puriwat, W., & Tripopsakul, S. (2021). Customer engagement with digital social responsibility in social media: A case study of COVID-19 situation in Thailand. Journal of Asian Finance, Economics and Business, 8(2), 475–483. https://doi.org/10.13106/jafeb.2021.vol8.no2.0475

Pramana, E. (2021). The mobile payment adoption: A systematic literature review. 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT), IEEE, 265–269. http://dx.doi.org/10.1109/EIConCIT50028.2021.9431846

Gupta, S., Yang, S., Lu, Y., Cao, Y., & Zhang, R. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Computers in Human Behavior, 28(1), 129–142. https://doi.org/10.1016/j.chb.2011.08.019

Seethamraju, R., Garg, S., & Diatha, K. (2018). Intention to use a mobile-based information technology solution for tuberculosis treatment monitoring: Applying a UTAUT model. Information Systems Frontiers. https://doi.org/10.1007/s10796-017-9801-z

Selya, A. S., Rose, J. S., Dierker, L. C., Hedeker, D., & Mermelstein, R. J. (2012). A practical guide to calculating Cohen’s f², a measure of local effect size, from PROC MIXED. Frontiers in Psychology, 3, 111. https://doi.org/10.3389/fpsyg.2012.00111

Simamora, V., & Saputra, B. D. (2023). Differentiation moderation effects: Experiential marketing and green marketing on the purchasing intention of motorcycle scooters. Jurnal Ekonomi Teknologi & Bisnis (JETBIS), 2(4). https://jetbis.al-makkipublisher.com/index.php/al/article/download/45/103

Singh, S., & Srivastava, R. K. (2018). Predicting the intention to use mobile banking in India. International Journal of Bank Marketing, 36(2), 357–378. http://dx.doi.org/10.1108/IJBM-12-2016-0186

Sleiman, K. A. A., Juanli, L., Lei, H., Liu, R., Ouyang, Y., & Rong, W. (2021). User trust levels and adoption of mobile payment systems in China: An empirical analysis. Sage Open, 11(4), https://doi.org/10.1177/21582440211056599

Tam, C., Santos, D., & Oliveira, T. (2018). Exploring the influential factors of continuance intention to use mobile apps: Extending the expectation-confirmation model. Information Systems Frontiers. https://doi.org/10.1007/s10796-018-9864-5

Wang, L., & Dai, X. (2020). Exploring factors affecting the adoption of mobile payment at physical stores. International Journal of Mobile Communications, 18(1), 67–82. http://dx.doi.org/10.1504/IJMC.2020.104420

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), 618–637. https://doi.org/10.3390/jtaer16040037

Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: Service robots in the frontline. Journal of Service Management, 29(5), 907–931. https://www.emerald.com/insight/content/doi/10.1108/josm-04-2018-0119/full/html

Xu, C., Peak, D., & Prybutok, V. (2015). A customer value, satisfaction, and loyalty perspective of mobile application recommendations. Decision Support Systems.

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Published

2025-02-28

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