Factors Affecting the Intention to Continue Using Online Payment Applications of SMEs at Viet Nam
Abstract
This study aims to identify the factors influencing the intention to continue using online payment applications among small and medium enterprises (SMEs) in Vietnam. The research employs a mixed-methods approach, combining qualitative and quantitative techniques. A theoretical framework was developed based on existing studies, which was subsequently refined to suit the context of SMEs in Vietnam. Data were collected through a Google Forms survey targeting 388 businesses familiar with online payment technologies such as Banking, Momo, Zalo Pay, AirPay, and ViettelPay. The data were analyzed using Smart PLS 4 software. Findings: The analysis reveals six key factors affecting the intention to continue using online payment applications: privacy security, social influence, perceived risk, perceived usefulness, information technology knowledge, and trust. This study contributes to the limited body of research focusing on SMEs in Vietnam and their adoption of online payment technologies. It provides a context-specific evaluation of factors driving continued usage intentions, offering insights for both academia and practice. The study is limited to SMEs in Vietnam, which may affect the generalizability of the findings to other contexts. Future research could explore these factors across different industries or regions. The study offers management strategies for enhancing SMEs’ trust and perceived usefulness of online payment technologies, ultimately fostering continued adoption. The findings can contribute to the broader adoption of digital payment systems, promoting a cashless society and improving economic efficiency in Vietnam.
References
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[34] Kwon, K. N., & Lee, J. (2003). Concerns about payment security of Internet purchases: a perspective on current on-line shoppers. Clothing and Textiles Research Journal, 21(4): 174-184.
[35] Lee, T., & Jun, J. (2007). Contextual perceived value? Investigating the role of contextual marketing for customer relationship management in a mobile commerce context. Business Process Management Journal, 13(6): 798-814.
[36] Liao, C., Palvia, P., & Chen, J. L. (2009). Information technology adoption behavior life cycle: Toward a Technology Continuance Theory (TCT). International Journal of Information Management, 29(4): 309-320.
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[40] Nguyen, G. D., & Dao, T. H. T. (2024). Factors influencing continuance intention to use mobile banking: an extended expectation-confirmation model with moderating role of trust. Humanities and Social Sciences Communications, 11(1): 1-14.
[41] Nguyen, G. D., & Thi Dao, T. H. (2024). The Moderating Role of Flow Experience on Mobile Commerce Continuance Intention: The Integrative View of User Adaptation, Expectation-Confirmation, and Task-Technology Models. SAGE Open, vol. 14, no. 2.
[42] Nguyen, P. T., Phung, M. T., Pham, T. M. L., & Vo, T. L. T. (2024). Factors affecting Vietnamese consumers’ intention to continue using e-wallet: A case study of MoMo. The VMOST Journal of Social Sciences and Humanities, 66(1): 29-44.
[43] Nur, T., & Panggabean, R. R. (2021). Factors influencing the adoption of mobile payment method among generation Z: the extended UTAUT approach. Journal of Accounting Research, Organization and Economics, 4(1): 14-28.
[44] Puriwat, W., & Tripopsakul, S. (2021). Explaining an adoption and continuance intention to use contactless payment technologies: during the COVID-19 pandemic. Emerging Science Journal, 5(1): 85-95.
[45] Quan, N.H. (2021). Factors In Online Payment that Affect Purchasing Intention and Payment Decision in B2C E-Commerce: A Case Study in Hanoi. International Journal Of Management And Economics, vol. 138.
[46] Quynh, N.T.N. & Anh, P.T.N. (2021). Determinants of behavior of using mobile payment services- The case of Banking University Ho Chi Minh City. Journal of Banking Science & Training, 235: 37-51.
[47] Rahadi, R. A. R. A., et al. (2022). Towards a cashless society: Use of electronic payment devices among generation z. International Journal of Data and Network Science, 6(1): 137-146.
[48] Rahadia, R. A., et al. (2022). Determining the factors influencing residential property price: A comparative study between Indonesia and Malaysia. Decision Science Letters, 11: 485–496.
[49] Raman, A., & Viswanathan, A. (2011). Web services and e-shopping decisions: A study on malaysian e-consumer. Wireless Information Networks & Business Information System, 2(5): 54-60.
[50] Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long range planning, 45(5-6): 341-358.
[51] Sang, N.M. (2023). Factors affecting Gen Z’s intention to use QR Pay in Vietnam after Covid-19. Innovative Marketing, vol. 19, no. 3.
[52] Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS quarterly, pp. 553-572.
[53] Susanto, E., Solikin, I., & Purnomo, B. S. (2022). A review of digital payment adoption in Asia. Advanced International Journal of Business, Entrepreneurship and SMEs, 4(11): 1-15.
[54] Swaminathan, V., Lepkowska-White, E., & Rao, B. P. (1999). Browsers or buyers in cyberspace? An investigation of factors influencing electronic exchange. Journal of computer-mediated communication, vol. 5, no. 2.
[55] Tan, S. H., Chong, L. L., & Ong, H. B. (2024). Continuance usage intention of e-wallets: Insights from merchants. International Journal of Information Management Data Insights, vol. 4, no. 2.
[56] Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational statistics & data analysis, 48(1): 159-205.
[57] Thắng, T. T., Lượng, L.D., Hoa, N. T. N., Huyen, P.T. (2022). Research on factors affecting the intention to use digital banking services at commercial bank branches in Bac Ninh province. Journal of Banking Science and Training, 240: 66-80. (in Vietnamese)
[58] Thanh, T. L., Huan, N. Q., Hong, T. T. T., & Foroudi, P. (2021). Effects of corporate social responsibility on SMEs’ performance in emerging market. Cogent Business & Management, vol. 8, no. 1.
[59] Thu, N.Q. & Tuyen, L.T.K. (2018). The role of social influence factors in the relationship with trust, attitude, risk perception on online purchase intention of consumers in Ho Chi Minh City," Asian Journal of Economic and Business Research, vol. 1. (in Vietnamese)
[60] Van der Heijden, H., Verhagen, T. and Creemers, M. (2003). Understanding online purchase intentions: contributions from technology and trust perspectives. European Journal of Information Systems, 12(1): 41-48.
[61] Varadarajan, P.R. & Yadav, M.S. (2002). Marketing strategy and the internet: an organizing framework. Journal of the Academy of Marketing Science, 30(4): 296-312.
[62] Venkatesh, V., Thong, J. Y., & 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.
[63] Zhao, H., & Khaliq, N. (2024). In quest of perceived risk determinants affecting intention to use fintech: Moderating effects of situational factors. Technological Forecasting and Social Change, vol. 207.
[64] Policy Tracker, "Policy Response to COVID 19," International Monetary Fund (2021).Available at: https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19
[2] Amin, M., Rezaei, S., & Abolghasemi, M. (2014). User satisfaction with mobile websites: the impact of perceived usefulness (PU), perceived ease of use (PEOU) and trust. Nankai Business Review International, 5(3): 285-274.
[3] Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, pp. 351-370.
[4] Chellappa, R. K., & Pavlou, P. A. (2002). Perceived information security, financial liability and consumer trust in electronic commerce transactions. Logistics Information Management, 15(5/6): 358-368.
[5] Chinomona, R. (2013). The influence of perceived ease of use and perceived usefulness on trust and intention to use mobile social software: technology and innovation. African Journal for Physical Health Education, Recreation and Dance, 19(2): 285-273.
[6] Cohen, J. (1988). Statistical power analysis for the behavioral sciences, 2nd ed, Routledge.
[7] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, pp. 319-340.
[8] Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). Technology acceptance model. J Manag Sci, 35(8): 982-1003.
[9] Doll, W. J., Hendrickson, A., & Deng, X. (1998). Using Davis's perceived usefulness and ease‐of‐use instruments for decision making: a confirmatory and multigroup invariance analysis. Decision sciences, 29(4): 839-869.
[10] Du, L., Razzaq, A., & Waqas, M. (2023). The impact of COVID-19 on small-and medium-sized enterprises (SMEs): empirical evidence for green economic implications. Environmental Science and Pollution Research International, 30(1): 1540-1561.
[11] Eastlick, M. A., Lotz, S. L., & Warrington, P. (2006). Understanding online B-to-C relationships: An integrated model of privacy concerns, trust, and commitment. Journal of business research, 59(8): 877-886.
[12] Flavián, C., & Guinalíu, M. (2006). Consumer trust, perceived security and privacy policy: three basic elements of loyalty to a web site. Industrial management & data Systems, 106(5): 601-620.
[13] Foon, Y. S., & Fah, B. C. Y. (2011). Internet banking adoption in Kuala Lumpur: an application of UTAUT model. International Journal of Business and Management, 6(4): 161-167.
[14] Giao, H. N. K., & Vuong, B. N. (2019). Master's Course in Scientific Research Methods in Business - Updated by SmartPLS. Financial Publishing House. (in Vietnamese)
[15] Green, S. B., & Neil, J. (2010). Salkind. Using SPSS for Windows and Macintosh: Analyzing and understanding data.
[16] Hair Jr, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European business review, 26(2): 106-121.
[17] Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long range planning, 6(1-2): 1-12.
[18] 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).
[19] Hair, J., Anderson, R., Tatham, R., & Black, W. (2005). Multivariate data analysis. NJ: Prentice Hall.
[20] Hayuningtyas, W. H., & Widiyanto, I. (2015). Antecedent kepercayaan dan keputusan pembelian. Diponegoro Journal of Management, pp. 639-749.
[21] Hoang, H., & Le Tan, T. (2023). Unveiling digital transformation: Investigating technology adoption in Vietnam's food delivery industry for enhanced customer experience. Heliyon, vol. 9, no. 9.
[22] Horst, M., Kuttschreuter, M., & Gutteling, J. M. (2007). Perceived usefulness, personal experiences, risk perception and trust as determinants of adoption of e-government services in The Netherlands. Computers in human behavior, 23(4): 1838-1852.
[23] Hussein, L. A., Baharudin, A. S., Kiumarsi, S., & Hilmi, M. F. (2020). Factors Influencing the Intention to Continue using B2B e-Commerce in Manufacturing SMEs. Engineering, Technology & Applied Science Research, vol. 10, no. 2.
[24] Jahangir, N., & Begum, N. (2008). The role of perceived usefulness, perceived ease of use, security and privacy, and customer attitude to engender customer adaptation in the context of electronic banking. African journal of business management, 2(1): 32-40.
[25] Jarvenpaa, S. L., Tractinsky, N., & Saarinen, L. (1999). Consumer trust in an Internet store: A cross-cultural validation. Journal of Computer-Mediated Communication, vol. 5, no. 2.
[26] Juliandi, A. (2018). Structural equatuion model based partial least square (SEM-PLS): Menggunakan SmartPLS. Universitas Batam.
[27] Junadi and Sfenrianto. (2015). A model of factors influencing consumer’s intention to use e-payment system in Indonesia. Procedia Computer Science, 59: 214 - 220 .
[28] Karim, M. W., Haque, A., Ulfy, M. A., Hossain, M. A., & Anis, M. Z. (2020). Factors influencing the use of E-wallet as a payment method among Malaysian young adults. Journal of International Business and Management, 3(2): 1-12.
[29] Kim, D. J., Ferrin, D. L., & Rao, H. R. (2003). Antecedents of consumer trust in B-to-C electronic commerce. Ninth Americas Conference on Information Systems, pp. 157-167.
[30] Kimery, K. M., & McCord, M. (2002). Third party assurances: mapping the road to trust in eretailing. Journal of Information Technology Theory and Application (JITTA), vol. 4, no. 2.
[31] Kripanont, N. (2007). Examining a technology acceptance model of internet usage by academics within Thai business schools. Doctoral dissertation, Victoria University.
[32] Kristina, N., & Harris, I. (2020). The technology acceptance model of mobile payment usage on generation Z. Binus Business Review, 11(3): 149-156.
[33] Kumar, A., Haldar, P., & Chaturvedi, S. (2024). Factors influencing intention to continue use of e-wallet: mediating role of perceived usefulness. Vilakshan-XIMB Journal of Management.
[34] Kwon, K. N., & Lee, J. (2003). Concerns about payment security of Internet purchases: a perspective on current on-line shoppers. Clothing and Textiles Research Journal, 21(4): 174-184.
[35] Lee, T., & Jun, J. (2007). Contextual perceived value? Investigating the role of contextual marketing for customer relationship management in a mobile commerce context. Business Process Management Journal, 13(6): 798-814.
[36] Liao, C., Palvia, P., & Chen, J. L. (2009). Information technology adoption behavior life cycle: Toward a Technology Continuance Theory (TCT). International Journal of Information Management, 29(4): 309-320.
[37] Liu, G. S., & Tai, P. T. (2016). A study of factors affecting the intention to use mobile payment services in Vietnam. Economics World, 4(6): 249 – 273.
[38] Mike Wade, Neil Tomlinson, Val Srinivas. (2023). 2024 banking and capital markets outlook. Financial Services, 2023. [Online]. Available at: https://www2.deloitte.com/us/en/insights/industry/financial-services/financial-services-industry-outlooks/banking-industry-outlook.html
[39] Nguyen, D.P. (2021). Research on the impact of factors on online payment decisions on e-commerce platforms of young people in Hanoi, Economy and Forecast, pp. 11-15. (in Vietnamese)
[40] Nguyen, G. D., & Dao, T. H. T. (2024). Factors influencing continuance intention to use mobile banking: an extended expectation-confirmation model with moderating role of trust. Humanities and Social Sciences Communications, 11(1): 1-14.
[41] Nguyen, G. D., & Thi Dao, T. H. (2024). The Moderating Role of Flow Experience on Mobile Commerce Continuance Intention: The Integrative View of User Adaptation, Expectation-Confirmation, and Task-Technology Models. SAGE Open, vol. 14, no. 2.
[42] Nguyen, P. T., Phung, M. T., Pham, T. M. L., & Vo, T. L. T. (2024). Factors affecting Vietnamese consumers’ intention to continue using e-wallet: A case study of MoMo. The VMOST Journal of Social Sciences and Humanities, 66(1): 29-44.
[43] Nur, T., & Panggabean, R. R. (2021). Factors influencing the adoption of mobile payment method among generation Z: the extended UTAUT approach. Journal of Accounting Research, Organization and Economics, 4(1): 14-28.
[44] Puriwat, W., & Tripopsakul, S. (2021). Explaining an adoption and continuance intention to use contactless payment technologies: during the COVID-19 pandemic. Emerging Science Journal, 5(1): 85-95.
[45] Quan, N.H. (2021). Factors In Online Payment that Affect Purchasing Intention and Payment Decision in B2C E-Commerce: A Case Study in Hanoi. International Journal Of Management And Economics, vol. 138.
[46] Quynh, N.T.N. & Anh, P.T.N. (2021). Determinants of behavior of using mobile payment services- The case of Banking University Ho Chi Minh City. Journal of Banking Science & Training, 235: 37-51.
[47] Rahadi, R. A. R. A., et al. (2022). Towards a cashless society: Use of electronic payment devices among generation z. International Journal of Data and Network Science, 6(1): 137-146.
[48] Rahadia, R. A., et al. (2022). Determining the factors influencing residential property price: A comparative study between Indonesia and Malaysia. Decision Science Letters, 11: 485–496.
[49] Raman, A., & Viswanathan, A. (2011). Web services and e-shopping decisions: A study on malaysian e-consumer. Wireless Information Networks & Business Information System, 2(5): 54-60.
[50] Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long range planning, 45(5-6): 341-358.
[51] Sang, N.M. (2023). Factors affecting Gen Z’s intention to use QR Pay in Vietnam after Covid-19. Innovative Marketing, vol. 19, no. 3.
[52] Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS quarterly, pp. 553-572.
[53] Susanto, E., Solikin, I., & Purnomo, B. S. (2022). A review of digital payment adoption in Asia. Advanced International Journal of Business, Entrepreneurship and SMEs, 4(11): 1-15.
[54] Swaminathan, V., Lepkowska-White, E., & Rao, B. P. (1999). Browsers or buyers in cyberspace? An investigation of factors influencing electronic exchange. Journal of computer-mediated communication, vol. 5, no. 2.
[55] Tan, S. H., Chong, L. L., & Ong, H. B. (2024). Continuance usage intention of e-wallets: Insights from merchants. International Journal of Information Management Data Insights, vol. 4, no. 2.
[56] Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational statistics & data analysis, 48(1): 159-205.
[57] Thắng, T. T., Lượng, L.D., Hoa, N. T. N., Huyen, P.T. (2022). Research on factors affecting the intention to use digital banking services at commercial bank branches in Bac Ninh province. Journal of Banking Science and Training, 240: 66-80. (in Vietnamese)
[58] Thanh, T. L., Huan, N. Q., Hong, T. T. T., & Foroudi, P. (2021). Effects of corporate social responsibility on SMEs’ performance in emerging market. Cogent Business & Management, vol. 8, no. 1.
[59] Thu, N.Q. & Tuyen, L.T.K. (2018). The role of social influence factors in the relationship with trust, attitude, risk perception on online purchase intention of consumers in Ho Chi Minh City," Asian Journal of Economic and Business Research, vol. 1. (in Vietnamese)
[60] Van der Heijden, H., Verhagen, T. and Creemers, M. (2003). Understanding online purchase intentions: contributions from technology and trust perspectives. European Journal of Information Systems, 12(1): 41-48.
[61] Varadarajan, P.R. & Yadav, M.S. (2002). Marketing strategy and the internet: an organizing framework. Journal of the Academy of Marketing Science, 30(4): 296-312.
[62] Venkatesh, V., Thong, J. Y., & 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.
[63] Zhao, H., & Khaliq, N. (2024). In quest of perceived risk determinants affecting intention to use fintech: Moderating effects of situational factors. Technological Forecasting and Social Change, vol. 207.
[64] Policy Tracker, "Policy Response to COVID 19," International Monetary Fund (2021).Available at: https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19
Published
2024-12-30
How to Cite
NGUYEN THI PHUONG, Giang et al.
Factors Affecting the Intention to Continue Using Online Payment Applications of SMEs at Viet Nam.
Theoretical and Practical Research in Economic Fields, [S.l.], v. 15, n. 4, p. 1023 - 1038, dec. 2024.
ISSN 2068-7710.
Available at: <https://journals.aserspublishing.eu/tpref/article/view/8691>. Date accessed: 06 jan. 2025.
doi: https://doi.org/10.14505/tpref.v15.4(32).18.
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