Usefulness factors to predict the continuance intention using mobile payment, case study: GO-Pay, OVO, Dana

Main Article Content

Cholilah Lateefa
Ryanis Naufalia
Danendra Yassar


The advancement of information technology continues to grow in line with the increasing years. The benefits gained from the advancement of information technology make all aspects of human life today can not be separated from information technology and also ikut encouragethe emergence ofinnovations in thedevelopment of informationtechnology,  sepertinya payment is no longer conventionally  nal but with mobile payment. This study aims to find out what useful factors influence the continuation of the intention to use  mobile payment  in  the go-pay, OVO,  and DANA case studies.  Analysis of factors that influenced this study include:  Computer Self Efficacy  (CSE),  Enjoyment  (E),  Perceived Ease of Use  (PEOU),  Perceived Usefulness  (PU),  Confirmation  (CON),  Perceived Value  (PV), Technical System Quality  (TSQ),  Satisfaction  (SAT),and  Continuance Intention  (CI). This study uses random sampling techniques  by collecting data utilizing  google form containing 45 statements using five Likert-scale distributed  online. The sample used in this study was 117 respondents. The statistical analysis techniques used in this study are Structural Equation Modeling (SEM) and use SMARTPLS 3.0 application as a tool to analyze the data. The results  obtained are that Computer-Self Efficacy  (CSE),  Perceived Ease of Use  (PEOU),  and  Perceived Usefulness  (PU) has no significant effect on  Continuance Intention  (CI). While  Satisfaction  (SAT), has a significant influence on  Continuance Intention  (CI).


Download data is not yet available.

Article Details

How to Cite
C. Lateefa, R. Naufalia, and D. Yassar, “Usefulness factors to predict the continuance intention using mobile payment, case study: GO-Pay, OVO, Dana”, JOSCEX, vol. 2, no. 2, pp. 115-126, Sep. 2021.


R. R. Suryono, B. Purwandari, and I. Budi, “Peer to peer (P2P) lending problems and potential solutions: A systematic literature review,” Procedia Comput. Sci., vol. 161, pp. 204–214, 2019.

S. Halilovic and M. Cicic, “Antecedents of information systems user behaviour-extended expectation-confirmation model,” Behav. Inf. Technol., vol. 32, no. 4, pp. 359–370, 2013.

B. Perwira, E. Yulianto, and S. Kumadji, “Pengaruh E-Service Quality dan Perceived Value Terhadap Kepuasaan Pelanggan dan Loyalitas Pelanggan (Survei pada Mahasiswa S1 Universitas Brawijaya yang Melakukan Transaksi Pembelian Online dengan Mobile Application Tokopedia),” J. Adm. Bisnis S1 Univ. Brawijaya, vol. 38, no. 2, pp. 46–54, 2016.

I. A. Brohi et al., “Near field communication enabled payment system adoption: A proposed framework,” 2017 IEEE 3rd Int. Conf. Eng. Technol. Soc. Sci. ICETSS 2017, vol. 2018-January, pp. 1–5, 2018.

S. H. Kim, J. H. Bae, and H. M. Jeon, “Continuous intention on accommodation apps: Integrated value-based adoption and expectation-confirmation model analysis,” Sustain., vol. 11, no. 6, pp. 1–17, 2019.

L. Y. K. Wang, S. L. Lew, S. H. Lau, and M. C. Leow, “Usability factors predicting continuance of intention to use cloud e-learning application,” Heliyon, vol. 5, no. 6, p. e01788, 2019.

T. C. Lin, S. Wu, J. S. C. Hsu, and Y. C. Chou, “The integration of value-based adoption and expectation-confirmation models: An example of IPTV continuance intention,” Decis. Support Syst., vol. 54, no. 1, pp. 63–75, 2012.

H. Mohammadi, “Investigating users’ perspectives on e-learning: An integration of TAM and IS success model,” Comput. Human Behav., vol. 45, pp. 359–374, 2015.

S. Yuan, Y. Liu, R. Yao, and J. Liu, “An investigation of users’ continuance intention towards mobile banking in China,” Inf. Dev., vol. 32, no. 1, pp. 20–34, 2016.

F. D. Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” Manag. Inf. Syst. Res. Center, Univ. Minnesota, vol. 13, no. 3, pp. 319–340, 1989.

H. Lai, C. Chen, and Y. Chang, “Expectation-Confirmation Model of Information System Continuance : A Meta-Analysis,” Int. J. Soc. Behav. Educ. Econ. Bus. Ind. Eng., vol. 10, no. 7, pp. 2162–2167, 2016.

D. Gefen, D. Straub, and M.-C. Boudreau, “Structural Equation Modeling and Regression: Guidelines for Research Practice,” Commun. Assoc. Inf. Syst., vol. 4, no. October, 2000.

K. Bastani, E. Asgari, and H. Namavari, “Wide and deep learning for peer-to-peer lending,” Expert Syst. Appl., vol. 134, pp. 209–224, 2019.

J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” J. Acad. Mark. Sci., vol. 43, no. 1, pp. 115–135, 2015.