An integrated model combining ECM and UTAUT to explain users’ post-adoption behaviour towards mobile payment systems

  • Sindhu Singh K.J. Somaiya Institute of Management Studies & Research
Keywords: mobile payment systems, continuance, intention to use, UTAUT, ECM, post adoption, India


Technological progression in mobile phones has increased the popularity of mobile payments. Users can shop online through a mobile device, which is time saving and convenient. Mobile payment systems involve ongoing interactions between users and payment providers. The initial acceptance of mobile payment systems has been studied extensively, but few studies have attempted to understand users’ post-adoption behaviour. This study employs an integrated model with the unified theory of acceptance and use of technology (UTAUT) framework and the expectation confirmation model (ECM), along with two additional constructs: perceived security and trust. The empirical results show that the integrated model has a higher predictive power to explain continuance intentions for using mobile payment systems with significant factors of satisfaction, trust, performance expectancy, and effort expectancy. This study confirmed that the UTAUT model could be extended to explain post-adoption behaviour towards mobile payment systems. The study’s findings have theoretical and practical value to further the understanding of pre- and post-adoption behaviour towards mobile payment systems.


Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.

Anderson, R., & Srinivasan, S. (2003). E‐satisfaction and e‐loyalty: A contingency framework. Psychology & Marketing,20(2), 123-138.

Apanasevic, T., Markendahl, J., & Arvidsson, N. (2016). Stakeholders ' expectations of mobile payment in retail: lessons from Sweden. International Journal of Bank Marketing, 34(1), 37-61.

Baabdullah, A., Alalwan, A., Rana, N., Kizgin, H., & Patil, P. (2019). Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model. International Journal of Information Management, 44, 38-52.

Bailey, A. A., Pentina, I., Mishra, A. S., & Ben Mimoun, M. S. (2017). Mobile payments adoption by US consumers: an extended TAM. International Journal of Retail & Distribution Management, 45(6), 626-640. doi:10.1108/ijrdm-08-2016-0144

Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351.

Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). Routledge.

Cao, X., Yu, L., Liu, Z., Gong, M., & Adeel, L. (2018). Understanding mobile payment users’ continuance intention: a trust transfer perspective. Internet Research, 28(2), 456-476.

Cao, Q., & Niu, X. (2019). Integrating context-awareness and UTAUT to explain Alipay user adoption. International Journal of Industrial Ergonomics, 69, 9-13.

Chen, X., & Li, S. (2017). Understanding Continuance Intention of Mobile Payment Services: An Empirical Study. Journal Of Computer Information Systems, 57(4), 287-298.

Chin, W., Marcolin, B., & Newsted, P. (2003). A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study. Information Systems Research, 14(2), 189-217.

Cohen, J. (1988). Statisticalpower analysis for the behavioral sciences (2’EU.). HilIsUale, NJ: Lawrence Eribaum Associates.

Dahlberg, T., Mallat, N., Ondrus, J., & Zmijewska, A. (2008). Past, present and future of mobile payments research: A literature review. Electronic Commerce Research and Applications, 7(2), 165-181.

Delone, W., & McLean, E. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19(4), 9-30.

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2017). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers, 21(3), 719-734. doi:10.1007/s10796-017-9774-y

Fan, J., Shao, M., Li, Y., & Huang, X. (2018). Understanding users’ attitude toward mobile payment use. Industrial Management & Data Systems, 118(3), 524-540. doi:10.1108/imds-06-2017-0268

Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 39-50.

Garland, R. (1991). The mid-point on a rating scale: Is it desirable. Marketing Bulletin,2(1), 66-70.

Gerban, M. (2019). Mobile Wallet Trends (Rep.). Retrieved May 25, 2019, from Global Acceptance Transaction Engine(GATE) website:

Geisser, S. (1974). A Predictive Approach to the Random Effect Model. Biometrika,61(1), 101-107.

George, D., & Mallery, M. (2010). SPSS for Windows Step by Step: A Simple Guide and Reference, 17.0 update 10/e.

Ghezzi, A., Renga, F., Balocco, R., & Pescetto, P. (2010). Mobile payment applications: offer state of the art in the Italian market. Info, 12(5), 3-22.

Giovanis, A., Assimakopoulos, C., & Sarmaniotis, C. (2018). Adoption of mobile self-service retail banking technologies. International Journal of Retail & Distribution Management.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. Pearson Higher Ed.

Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. doi:10.1108/ebr-11-2018-0203.

Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566-584. doi:10.1108/ejm-10-2018-0665.

Hampshire, C. (2017). A mixed methods empirical exploration of UK consumer perceptions of trust, risk and usefulness of mobile payments. International Journal of Bank Marketing, 35(3), 354-369.

Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics, 101, 75-84.

Iman, N. (2018). Is mobile payment still relevant in the fintech era?. Electronic Commerce Research And Applications, 30, 72-82.

Isaac, J. T., & Sherali, Z. (2014). Secure mobile payment systems. IT Professional, 16(3), 36-43.

José Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2014). Role of gender on acceptance of mobile payment. Industrial Management & Data Systems, 114(2), 220-240.

Kang, J. (2018). Mobile payment in Fintech environment: Trends, security challenges, and services. Human-centric Computing and Information Sciences, 8(32), 1-16. doi:10.1186/s13673-018-0155-

Khalilzadeh, J., Ozturk, A., & Bilgihan, A. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers In Human Behavior, 70, 460-474.

Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310-322. doi:10.1016/j.chb.2009.10.013

Kline, R. B. (2011). Principles and practice of structural equation modeling. Guilford Press.

Kumar, A., Adlakaha, A., & Mukherjee, K. (2018). The effect of perceived security and grievance redressal on continuance intention to use M-wallets in a developing country. International Journal of Bank Marketing, 36(7), 1170-1189. doi:10.1108/ijbm-04-2017-0077

Kumar, R. R., Israel, D., & Malik, G. (2018). Explaining customer’s continuance intention to use mobile banking apps with an integrative perspective of ECT and Self-determination theory. Pacific Asia Journal of the Association for Information Systems,79-112.

Lee, J., Cho, C., & Jun, M. (2011). Secure quick response-payment (QR-Pay) system using mobile device. In 13th International Conference on In Advanced Communication Technology (ICACT) (pp. 1424-1427). IEEE.

Li, H., Liu, Y., & Heikkilä, J. (2014). Understanding the Factors Driving NFC-Enabled Mobile Payment Adoption: an Empirical Investigation. PACIS, 231-244.

Lu, J., Wei, J., Yu, C., & Liu, C. (2016). How do post-usage factors and espoused cultural values impact mobile payment continuation? Behaviour & Information Technology, 36(2), 140-164. doi:10.1080/0144929x.2016.1208773

Mayer, R., Davis, J., & Schoorman, F. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709-734.

Mcknight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and Validating Trust Measures for e-Commerce: An Integrative Typology. Information Systems Research, 13(3), 334-359.

Mobile Payments World. (2019). Retrieved May 19, 2019, from

Musa, A., Khan, H. U., & AlShare, K. A. (2015). Factors influence consumers' adoption of mobile payment devices in Qatar. International Journal of Mobile Communications, 13(6), 670. doi:10.1504/ijmc.2015.072100

Oghuma, A. P., Libaque-Saenz, C. F., Wong, S. F., & Chang, Y. (2016). An expectation-confirmation model of continuance intention to use mobile instant messaging. Telematics and Informatics,33(1), 34-47.

Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers In Human Behavior, 61, 404-414.

Oliver, R. L. (1997). Satisfaction a behavioral perspective on the consumer. Oxfordshire: Routledge.

Ondrus, J., & Pigneur, Y. (2006). Towards a holistic analysis of mobile payments: A multiple perspectives approach. Electronic Commerce Research and Applications, 5(3), 246-257.

Orr, G. (2010, April). A review of literature in mobile learning: Affordances and constraints. In 2010 6th IEEE International Conference on Wireless, Mobile, and Ubiquitous Technologies in Education (pp. 107-111). IEEE.

Phonthanukitithaworn, C., Sellitto, C., & Fong, M. (2015). User intentions to adopt mobile payment services: A study of early adopters in Thailand. Journal Of Internet Banking And Commerce, 20(1), 1-29.

Podsakoff, P., MacKenzie, S., Lee, J., & Podsakoff, N. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal Of Applied Psychology, 88(5), 879-903.

Ramadan, R., & Aita, J. (2018). A model of mobile payment usage among Arab consumers. International Journal of Bank Marketing, 36(7), 1213-1234. doi:10.1108/ijbm-05-2017-0080

Ringle, C. M., Silva, D. D., & Bido, D. D. (2014). Structural Equation Modeling with the Smartpls. Revista Brasileira De Marketing,13(02), 56-73.

Schierz, P. G., Schilke, O., & Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic Commerce Research and Applications, 9(3), 209-216. doi:10.1016/j.elerap.2009.07.005

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

Stone, M. (1974). Cross-Validatory Choice and Assessment of Statistical Predictions (With Discussion). Journal of the Royal Statistical Society: Series B (Methodological),38(1), 111-147.

Susanto, A., Chang, Y., & Ha, Y. (2016). Determinants of continuance intention to use the smartphone banking services. Industrial Management & Data Systems, 116(3), 508-525.

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,1-15.

Tellez Isaac, J., & Sherali, Z. (2014). Secure Mobile Payment Systems. IT Professional, 16(3), 36-43.

Teo, A., Tan, G., Ooi, K., Hew, T., & Yew, K. (2015). The effects of convenience and speed in m-payment. Industrial Management & Data Systems, 115(2), 311-331.

Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157-178.

Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425-478.

Wang, Y., Hahn, C., & Sutrave, K. (2016). Mobile payment security, threats, and challenges. 2016 Second International Conference on Mobile and Secure Services (MobiSecServ)IEEE, 1-5. doi:10.1109/mobisecserv.2016.7440226.

Yuan, S., Liu, Y., Yao, R., & Liu, J. (2014). An investigation of users’ continuance intention towards mobile banking in China. Information Development, 32(1), 20-34. doi:10.1177/0266666914522140

Zhang, H., Lu, Y., Gupta, S., & Gao, P. (2015). Understanding group-buying websites continuance. Internet Research, 25(5), 767-793.

Zhou, T. (2014). Understanding the determinants of mobile payment continuance usage. Industrial Management & Data Systems, 114(6), 936-948.

Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085-1091.

How to Cite
Singh, S. (2020). An integrated model combining ECM and UTAUT to explain users’ post-adoption behaviour towards mobile payment systems. Australasian Journal of Information Systems, 24.
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