Determinants of Continuance Intention by Developing the Expectation Confirmation Model (ECM): A Study of OVO Fintech Application Users in Indonesia
DOI:
https://doi.org/10.18502/kss.v10i13.18968Keywords:
continuance intention, expectation confirmation model, OVO Fintech applicationAbstract
This research aims to explore the factors influencing the continued use of the OVO fintech application in Indonesia. As digital payment platforms become increasingly common in today’s digital era, OVO has emerged as one of the country’s leading fintech services. The study issues a quantitative approach with a sample of 500 respondents selected using purposive sampling, allowing for specific criteria to guide the inclusion of participants. Data were collected through an online survey and analyzed using the fractional minimum quadratic terms method. Of the 13 research hypotheses, 11 were found to significantly influence users’ intention to continue using the application. However, two hypotheses were not supported: perceived usefulness was found to have no significant effect on user satisfaction or continuance intention. These findings offer valuable insights for fintech developers in Indonesia, highlighting areas for improving user experience and boosting wider market adoption. This study is expected to inform future marketing strategies and feature development for the OVO fintech application, ultimately fostering greater user loyalty in an increasingly competitive fintech landscape.
References
[1] Bi.go.id. Informasi perizinan penyelenggara dan pendukung jasa sistem pembayaran [Licensing information for payment system service providers and supporters,Internet]. Jakarta: Bank Indonesia; 2020 [cited 2023 Jun 3]. Available from: https://www.bi.go.id/id/sistem-pembayaran/informasi-perizinan/uangelektronik/ penyelenggara-berizin/Pages/default.aspx. Indonesian
[2] Trends.google.co.id. Pertumbuhan mobile wallet 2018 [Mobile wallet growth 2018,Internet]. Google Trends Indonesia; 2020 [cited 2023 Jun 3]. Available from: https://www.trends.google.co.idcitizen6/pertumbuhan-mobile-wallet-2018. Indonesian
[3] Bhattacherjee A. Understanding information systems continuance: an expectationconfirmation model. Manage Inf Syst Q. 2001;25(3):351–70. DOI: https://doi.org/10.2307/3250921
[4] Hill RJ, Fishbein M, Ajzen I. Belief, attitude, intention and behavior: an introduction to theory and research. Contemp Sociol. 1977;6(2):244. DOI: https://doi.org/10.2307/2065853
[5] Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. Manage Inf Syst Q. 1989;13(3):319–40. DOI: https://doi.org/10.2307/249008
[6] Parasuraman A, Colby CL. Techno-ready marketing: How and why your customers adopt technology. USA: The Free Press; 2001.
[7] Mittal V, Huppertz JW, Khare A. Customer complaining: the role of tie strength and information control. J Retailing. 2008;84(2):195–204. DOI: https://doi.org/10.1016/j.jretai.2008.01.006
[8] Oliver RL. A cognitive model of the antecedents and consequences of satisfaction decisions. J Mark Res. 1980;17(4):460–9. DOI: https://doi.org/10.1177/002224378001700405
[9] Mowen JC, Minor M. Perilaku konsumen. Jakarta: Erlangga; 2002.
[10] Kotler P, Keller KL. Marketing management. 12th ed. New Jersey: Pearson Education; 2009.
[11] Arpaci I, Yardimci Cetin Y, Turetken O. Impact of perceived security on organizational adoption of smartphones. cyberpsychology, behavior, and social networking. 2015;18(10):602-608. https://doi.org/10.1089/cyber.2015.0243. DOI: https://doi.org/10.1089/cyber.2015.0243
[12] Kim DJ, Ferrin DL, Rao HR. A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decis Support Syst. 2008;44(2):544–64. DOI: https://doi.org/10.1016/j.dss.2007.07.001
[13] Ofori KS, Boateng H, Okoe AF, Gvozdanovic I. Examining customers’ continuance intentions towards internet banking usage. Mark Intell Plann. 2017;35(6):756–73. DOI: https://doi.org/10.1108/MIP-11-2016-0214
[14] Parasuraman A, Colby CL. An updated and streamlined technology readiness index: TRI 2.0. journal of service research. 2015;18(1):59-74. https://doi.org/10.1177/1094670514539730. DOI: https://doi.org/10.1177/1094670514539730
[15] Lin JS, Hsieh PL. The influence of technology readiness on satisfaction and behavioral intentions toward self-service technologies. Comput Human Behav. 2007;23(3):1597–615. DOI: https://doi.org/10.1016/j.chb.2005.07.006
[16] Humbani M, Wiese M. An integrated framework for the adoption and continuance intention to use mobile payment apps. Int J Bank Mark. 2019;37(2):646–64. DOI: https://doi.org/10.1108/IJBM-03-2018-0072
[17] Walczuch R, Lemmink J, Streukens S. The effect of service employees’ technology readiness on technology acceptance. Inf Manage. 2007;44(2):206–15. DOI: https://doi.org/10.1016/j.im.2006.12.005
[18] Esen M, Erdoğmuş N. Effects of technology readiness on technology acceptance in E-HRM: mediating role of perceived usefulnes. Bilgi Ekonomisi ve Yönetimi Dergisi. 2014;9(1):7–21.
[19] Fu XM, Zhang JH, Chan FT. Determinants of loyalty to public transit: A model integrating satisfaction-loyalty theory and expectation-confirmation theory. Transp Res Part A Policy Pract. 2018;113:476–90. DOI: https://doi.org/10.1016/j.tra.2018.05.012
[20] Susanto A, Chang Y, Ha Y. Determinants of continuance intention to use the smartphone banking services: an extension to the expectation-confirmation model. Ind Manage Data Syst. 2016;116(3):508–25. DOI: https://doi.org/10.1108/IMDS-05-2015-0195
[21] Zhou W, Tsiga Z, Li B, Zheng S, Jiang S. What influence users’ e-finance continuance intention? The moderating role of trust. Ind Manage Data Syst. 2018;118(8):1647–70. DOI: https://doi.org/10.1108/IMDS-12-2017-0602
[22] Oghuma AP, Libaque-Saenz CF, Wong SF, Chang Y. An expectation-confirmation model of continuance intention to use mobile instant messaging. Telemat Inform. 2016;33(1):34–47. DOI: https://doi.org/10.1016/j.tele.2015.05.006
[23] Zhang H, Lu Y, Gupta S, Gao P. Understanding group-buying websites continuance: an extension of expectation confirmation model. Internet Res. 2015;25(5):767–93. DOI: https://doi.org/10.1108/IntR-05-2014-0127
[24] Yu J, Zo H, Choi MK, Ciganek AP. User acceptance of location-based social networking services: an extended perspective of perceived value. Online Inf Rev. 2013;37(5):711–30. DOI: https://doi.org/10.1108/OIR-12-2011-0202
[25] Chang HH, Wang HW. The moderating effect of customer perceived value on online shopping behaviour. Online Inf Rev. 2011;35(3):333–59. DOI: https://doi.org/10.1108/14684521111151414
[26] Hong MK, Kwahk KY. The effects of perceived equity on satisfaction and continuance intention in openmarket. Journal of the Korean Operations Research and Management Science Society. 2010;35(3):1–24.
[27] Kumar A, Adlakaha A, Mukherjee K. The effect of perceived security and grievance redressal on continuance intention to use M-wallets in a developing country. Int J Bank Mark. 2018;36(7):1170–89. DOI: https://doi.org/10.1108/IJBM-04-2017-0077
[28] Kim B. An empirical investigation of mobile data service continuance: incorporating the theory of planned behavior into the expectation–confirmation model. Expert Syst Appl. 2010;37(10):7033–9. DOI: https://doi.org/10.1016/j.eswa.2010.03.015
[29] Wang YS. Assessing e-commerce systems success: A respecification and validation of the DeLone and McLean model of IS success. Inf Syst J. 2008;18(5):529–57. DOI: https://doi.org/10.1111/j.1365-2575.2007.00268.x
[30] Gong X, Lee MK, Liu Z, Zheng X. Examining the role of tie strength in users’ continuance intention of second-generation mobile instant messaging services. Inf Syst Front. 2020;22(1):149–70. DOI: https://doi.org/10.1007/s10796-018-9852-9
[31] Sheth JN, Newman BI, Gross BL. Why we buy what we buy: A theory of consumption values. J Bus Res. 1991;22(2):159–70. DOI: https://doi.org/10.1016/0148-2963(91)90050-8
[32] Cardozo RN. An experimental study of customer effort, expectation, and satisfaction. J Mark Res. 1965;2(3):244–9. DOI: https://doi.org/10.1177/002224376500200303
[33] Amin M, Rezaei S, Abolghasemi M. User satisfaction with mobile websites: the impact of perceived usefulness (PU), perceived ease of use (PEOU) and trust. Nankai Bus Rev Int. 2014;5(3):258–74. DOI: https://doi.org/10.1108/NBRI-01-2014-0005
[34] Pikkarainen T, Pikkarainen K, Karjaluoto H, Pahnila S. Consumer acceptance of online banking: an extension of the technology acceptance model. Internet Res. 2004;14(3):224–35. DOI: https://doi.org/10.1108/10662240410542652
[35] Zhou T. Understanding continuance usage of mobile sites. Ind Manage Data Syst. 2013;113(9):1286–99. DOI: https://doi.org/10.1108/IMDS-01-2013-0001
[36] Libaque-Sáenz CF, Wong SF, Chang Y, Ha YW, Park MC. Understanding antecedents to perceived information risks: an empirical study of the Korean telecommunications market. Inf Dev. 2016;32(1):91–106. DOI: https://doi.org/10.1177/0266666913516884
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Amma Fazizah, Mochammad Al Musadieq, Andriani Kusumawati, Mohammad Iqbal

This work is licensed under a Creative Commons Attribution 4.0 International License.