Laurenzia Juvelin, Fenika Wulani, Andi Anugerah Amrullah


In this era, digitalization becomes the trend on which everything goes digital and this includes paying through application. The users’ intention to continue using the application is very important. Based on the extended model of information technology continuance, the factors that influence the application continuance intention are post-usage usefulness, self-efficacy and satisfaction. This research aimed to analyze the influence of Post-Usage Usefulness, Self-Efficacy and Satisfaction Towards the Application Continuance Intention of T-Cash. This research is a causal study and uses a non-probability purposive sampling technique. The total number of respondents is 150 respondents who have T-Cash application and had experiences in using T-Cash application for payment in the last three months. The data were processed and analyzed by using Structural Equation Modeling technique through LISREL software. This research proved that Post-Usage Usefulness has a significant positive effect on the Application Continuance Intention and Satisfaction, Self-Efficacy has a significant positive effect on the Application Continuance Intention and Satisfaction, and Satisfaction has a significant positive effect on the Application Continuance Intention. The suggestions for T-Cash are to add more features that are beneficial for making payments faster, designing an easy and interactive user interface, and providing users with easy to follow instructions.

Save to Mendeley

Full Text:



Akhter, S. H. (2014). Privacy concern and online transactions: The impact of Internet self- efficacy and Internet involvement. Journal of Consumer Marketing 31 (2):118–25.

Al-Maghrabi, T., and C. Dennis. (2011). What drives consumers’ continuance intention to e- shopping? Conceptual framework and managerial implications in the case of Saudi Arabia. International Journal of Retail & Distribution Management 39 (12):899–926.

Anderson, J. C., and D. W. Gerbing. (1982). Some Methods for Respecifying Measurement Models to Obtain Unidimensional Construct Measurement. Journal of Marketing Research 19:453–60.

Bagozzi, T. P., Y. Yi, and L. W. Phillips. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly 36:421-58.

Bellman, S., R. F. Potter, S. Treleaven-Hassard, J. A. Robinson and D. Varra. (2011). The effectiveness of branded mobile phone apps. Journal of Interactive Marketing 25 (4):191-200.

Bhattacherjee, A. (2001). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201-214.

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation- confirmation model. MIS Quarterly 25 (3):351-70.

Bhattacherjee, A., Perols, J., and Sanford, C. (2008). Information technology continuance: A theoretic extension and empirical test. Journal of Computer Information Systems, 49(1), 17-26.

Crabbe, M., C. Standing, S. Standing, and H. Karjaluoto. (2009). An adoption model for mobile banking in Ghana. International Journal of Mobile Communications 7 (5):515–43.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13:319-40.

Durianto, D. and Sugiarto. (2001). Strategi Menaklukkan Pasar Melalui Riset Ekuitas dan Perilaku Merek. Jakarta: Gramedia.

Flavian, C., M. Guinaliu, and R. Gurrea. (2006). The role played by perceived usability, satisfaction and consumer trust on website loyalty. Information & Management 43 (1):1-14.

Grewal, D., A. L. Roggeveen, L.D. Compeau, and M. Levy. (2012). Retail value-based pricing strategies: New times, new technology, new consumers. Journal of Retailing 88 (1): 1-6.

Hsu, C.-L., and J. C.-C. Lin. (2015). What drives purchase intention for paid mobile apps? An expectation confirmation model with perceived value. Electronic Commerce Research and Applications 14:46-57.

Hu, P. J. H., S. A. Brown, J. Y. Thong, F. K. Chan, and K. Y. Tam. (2009). Determinants of service quality and continuance intention of online services: The case of eTax. Journal of the American Society for Information Science and Technology 60 (2):292- 306.

Hung, S.-Y., C.-M. Chang, and T.-J. Yu. (2006). Determinants of user acceptance of the e- government services: The case of online tax filling and payment system. Government Information Quarterly 23 (1):97-122.

Igbaria, M., and J. Iivari. (1995). The effects of self-efficacy on computer usage. Omega 23 (6):587-605.

Kang, Y. J., and Lee, W. J. (2014). Self-customization of online service environments by users and its effect on their continuance intention. Service Business 9(2): 321–342.

Kang, Y. S., and H. Lee. (2010). Understanding the role of an IT artifact in online service continuance: An extended perspective of user satisfaction. Computers in Human Behavior 26 (3):353–64.

Kuo, Y.-C., A. E. Walker, K. E. Schroder, and B. R. Belland. (2014). Interaction, Internet self- efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education 20:35–50.

Limayem, M., M. Khalifa, and A. Frini. (2000). What makes consumers buy from Internet? A longitudinal study of online shopping. IEEE Transactions on Systems, Man, and Cybernetics - Part A 30 (4):421-32.

Limayem, M., S. G. Hirt, and C. M. Cheung. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly 31 (4):705- 37.

Lin, K. M., Chen N. S., & Fang, K. (2011). Understanding e-learning continuance intention: A negative critical incidents perspective. Behaviour & Information Technology 30 (1):77-89.

McLeod, S. A. (2014). Questionnaire. Retrieved from naires.html.

Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research 17:460-69.

Reinartz, W. J., M. Haenlein, and J. Henseler. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing 26 (4):332–44.

Rezaei, S., Shahijan, M. K., Amin, M., & Ismail, W. K. W. (2016). Determinants of App Stores Continuance Behavior: A PLS Path Modelling Approach, Vol. 15, No. 4, 408- 440.

Riquelme, I. P., and S. Román. (2014). The influence of consumers’ cognitive and psychographic traits on perceived deception: A comparison between online and offline retailing contexts. Journal of Business Ethics 119 (3):405–22.

Salanova, M., R. M. Grau, E. Cifre, and S. Llorens. (2000). Computer training, frequency of usage and burnout: The moderating role of computer self-efficacy. Computers in Human Behavior 16 (6):575–90.

Sarstedt, M. (2008). A review of recent approaches for capturing heterogeneity in partial least squares path modelling. Journal of Modelling in Management 3 (2):140–61.

Setiawan, S. (2018). Tahun 2017, Pengguna Internet di Indonesia Mencapai 143,26 Juta Orang. Retrieved from tahun-2017-pengguna-internet-di-indonesia-mencapai-14326-juta-orang, 12-07-2018.

Shang, D., and Wu, W. (2017). Understanding mobile shopping consumers’ continuance intention. Industrial Management & Data Systems 117 (1):213-227.

Shank, D. B., and S. R. Cotten. (2014). Does technology empower urban youth? The relationship of technology use to self-efficacy. Computers & Education 70:184–93.

Song, J., J. Kim, D. R. Jones, J. Baker, and W. W. Chin. (2014). Application discoverability and user satisfaction in mobile application stores: An environmental psychology perspective. Decision Support Systems 59:37-51.

Teo, T. S., and V. K. Lim. (2001). The effects of perceived justice on satisfaction and behavioral intentions: The case of computer purchase. International Journal of Retail & Distribution Management 29 (2):109-25.

Vekiri, I., and A. Chronaki. (2008). Gender issues in technology use: Perceived social support, computer self-efficacy and value beliefs, and computer use beyond school. Computers & Education 51 (3):1392–404.

Widiartanto, Y. (2016). Pengguna Internet di Indonesia Capai 132 Juta. Retrieved from nesia.capai.132.juta, 12-07-2018.

Yamin, S., and Kurniawan, H. (2009). Structural Equation Modeling dengan Lisrel – PLS. Jakarta: Penerbit Salemba.

Https:// Accessed on 12-07-2018.

Https:// Accessed on 25-01-2019.

Https:// Accessed on 25-01-2019.

Https:// penyelenggara-berizin/Pages/default.aspx. Accessed on 12-07-2018.

Https:// Accessed on 25-01-2019.

Https:// hanya-35.html. Accessed on 12-07-2018.