Integrating TAM, VAM, PAM And Security Perception In The Intention Of Fintech Service Usage

Aglis Andhita Hatmawan


This study is aims to develop a TAM (Technology Acceptance Model) by integrating a VAM (Value-based Adoption Model), PAM (Pos Acceptance Model) and security perceptions. The frameworks of the theories are expected to deeper explain the perspective perceived by fintech services users in terms of security, perceived value, benefits and satisfaction in using fintech. Survey through a quantitative approach combined with explanatory research in Madiun Residency are used in this research. The results showed that all hypotheses proposed in this study were accepted. Security is the main requirement for people to continue or discontinue using fintech services. Perceived value is a comparative result influenced by the amount of sacrifice made and the perceived benefits. People who are satisfied and tends to assume that the fintech service is valuable, will have a tendency to continue using the fintech service. Therefore, it can be concluded that TAM, VAM, PAM and security perceptions are a unified whole in understanding one's behaviour in adopting technology such as fintech servicces.



TAM, VAM, PAM, Security Persptions, Fintech.

Full Text:



Aboelmaged, M., & Gebba, T. R. (2013). Mobile Banking Adoption: An Examination of Technology Acceptance Model and Theory of Planned Behavior. International Journal of Business Research and Development, 2(1), 35–50.

Ahn, T., Ryu, S., & Han, I. (2004). The Impact Of The Online And Offline Features On The User Acceptance Of Internet Shopping Malls. Electronic Commerce Research and Applications, 3, 405–420.

Alsajjan, B., & Dennis, C. (2010). Internet Banking Acceptance Model: Cross-Market Examination. Journal of Business Research, 63(9–10), 957–963.

Arner, D. W., Barberis, J., Buckley, R. P., Arnott, R. D., & Aronson, T. R. (2017). Foundation Briefs Fintech And Regtech In A Nutshell , And The Future In A Sandbox. Research Foundation Briefs, Volume 3(Issue 4).

Bansal, G. (2017). Distinguishing Between Privacy And Security Concerns: An Empirical Examination And Scale Validation. Journal of Computer Information Systems, 57(4), 330–343.

Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351–370.

Bhattacherjee, A. (2014). Qarterjy Continuance : Management Information Systems Research Center, 25(3), 351–370.

Bollen, K. A. (1990). Overall Fit in Covariance Structure Models: Two Types of Sample Size Effects. Psychological Bulletin, 107(2), 256–259.

Byrne, arbara M. (1998). Structural Equation Modeling With Lisrel, Prelis, and Simplis (1st Editio). New York: Psychology Press.

Chiu, I. H.-Y. (2016). Fintech And Disruptive Business Models. Journal Of Technology Law & PolicY, 21, 55–112.

Davis, Jr., F. D. (1986). A Technology Acceptance Model For Empirically Testing New End-User Information Systems: Theory And Results [Dissertation].

Davis, F. D. (1989). Perceived Usefulnees, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340.

Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. USA: Addison-Wesley.

Ghozali, I. (2013). Aplikasi Analisis Multivariate dengan program IBM SPSS 21. Semarang: Badan Penerbit Universitas Diponegoro.

Hai, L. C., & Kazmi, S. H. A. (2015). Dynamic Support of Government in Online Shopping. Asian Social Science, 11(August).

Hun, S., Kim, D. J., Hur, Y., & Park, K. (2018). An Empirical Study of the Impacts of Perceived Security and Knowledge on Continuous Intention to Use Mobile Fintech Payment Services. International Journal of Human - Computer Interaction.

Johnson, V. L., Kiser, A., Washington, R., & Torres, R. (2018). Limitations To The Rapid Adoption Of M-Payment Services: Understanding The Impact Of Privacy Risk On M-Payment Services. Computers in Human Behavior, 79, 111–122.

Kagermann, H. (2013). Securing the future of German manufacturing industry initiative INDUSTRIE 4.0 implementing the strategic Recommendations for Final report of the Industrie 4.0 Working Group.

Kim. (2011). Understanding Antecedents of Continuance Intention in Social-Networking Services. Cyber Psychology,Behavior, And Social Networking, 14(4).

Kim, H., Chan, H. C., & Gupta, S. (2007). Value-Based Adoption Of Mobile Internet : An Empirical Investigation. Decision Support Systems, 43, 111–126.

Kim, S. H., Bae, J. H., & Jeon, H. M. (2019). Continuous Intention on Accommodation Apps : Integrated Value-Based Adoption and Expectation – Confirmation Model Analysis. Sustainabi, 1–17.

Kleijnen, M., Wetzels, M., & de Ruyter, K. (2004). Consumer Acceptance Of Wireless Finance. Journal of Financial Services Marketing, 8(3), 206–217.

Kwon, H., & Seo, K. (2013). Application of Value-based Adoption Model to Analyze SaaS Adoption Behavior in Korean B2B Cloud Market. 5(12), 368–373.

Lee, M. (2010). Computers & Education Explaining And Predicting Users ’ Continuance Intention Toward E-Learning : An Extension Of The Expectation – Confirmation Model. Computers & Education, 54(2), 506–516.

Lim, S. H. (2016). 전자메일 서비스 이용자의 패스워드 교체 심리에 대한 연구 An Investigation of the Psychology of Password Replacement by Email Users. Journal of The Korea Institute of Information Security & Cryptology, 26(5).

Lin, T. C., Wu, S., Hsu, J. S. C., & Chou, Y. C. (2012). The Integration Of Value-Based Adoption And Expectation-Confirmation Models: An Example Of IPTV Continuance Intention. Decision Support Systems, 54(1), 63–75.

Maccallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power Analysis And Determination Of Sample Size For Covariance Structure Modeling Of Fit Involving A Particular Measure Of Model. Psychologycal Methods, 13(2), 130–149.

Mallat, N., Rossi, M., Tuunainen, V. K., & Anssi, O. (2009). Information & Management The Impact Of Use Context On Mobile Services Acceptance : The Case Of Mobile Ticketing §. Information & Management, 46, 190–195.

Nunnally, J. C. (1978). Psychometric theory (2nd Editio). New York: McGraw Hill.

Oliver, R. L. (1980). A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research, 17, 460–469.

Oliver, R. L., & Desarbo, W. S. (1988). Response Determinants in Satisfaction Judgments. The Journal Of Consumer Research, 14(March), 495–507.

Ooi, K. B., & Tan, G. W. H. (2016). Mobile Technology Acceptance Model: An Investigation Using Mobile Users To Explore Smartphone Credit Card. Expert Systems with Applications, 59, 33–46.

Ray, S. (2011). Security Assurance : How Online Service Providers Can Influence Security Control Perceptions and Gain Trust. Decision Science, 42(2), 391–412.

Roostika, R. (2012). Mobile Internet Acceptance among University Students : A Value-based Adoption Model. International Journal of Research in Management & Technology, 2(1), 21–28.

Salo, M., & Haapio, H. (2017). Robo-Advisors And Investors : Enhancing Human-Robot Interaction Through Information Design : International Legal Informatics Symposium IRIS 2017, 441–448.

Tabachnick, B. G., & Fidell, L. S. (2006). Using Multivariate Statistics (5th Edition). Boston: MA : Allyn and Bacon.

Tan, G. W. H., Ooi, K. B., Chong, S. C., & Hew, T. S. (2014). NFC Mobile Credit Card: The Next Frontier Of Mobile Payment? Telematics and Informatics, 31(2), 292–307.

Teo, A. C., Tan, G. W. H., Ooi, K. B., & Lin, B. (2015). Why Consumers Adopt Mobile Payment? A Partial Least Squares Structural Equation Modelling (PLS-SEM) approach. International Journal of Mobile Communications, 13(5), 478–497.

Torres, R., & Gerhart, N. (2017). Mobile Proximity Usage Behaviors Based on User Characteristics Mobile Proximity Usage Behaviors Based on User Characteristics. Journal of Computer Information Systems, 00(00), 1–10.

Turel, O., Serenko, A., & Bontis, N. (2010). User Acceptance Of Hedonic Digital Artifacts: A Theory Of Consumption Values Perspective. Information and Management, 47(1), 53–59.

Vatanasombut, B., Igbaria, M., Stylianou, A. C., & Rodgers, W. (2008). Information Systems Continuance Intention Of Web-Based Applications Customers: The Case Of Online Banking. Information and Management, 45(7), 419–428.

Woodruff, R, B. (1997). Customer Value The Next Source For Competitive Advantage. Journal of the Academy of Marketing Science, 25(2), 139–153.

Yang, D., & Li, M. (2018). Evolutionary Approaches and the Construction of Technology-Driven Regulations. Emerging Markets Finance & Trade, 1–16.

Zeithaml, V. A. (2012). Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence. Journal of Marketing, 52(3), 2–22.



  • There are currently no refbacks.

Copyright (c) 2021 Jurnal Ilmiah Manajemen dan Bisnis

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

Jurnal Ilmiah Manajemen dan Bisnis
Fakultas Ekonomi dan Bisnis Universitas Mercu Buana
Jl. Raya Meruya Selatan, Kembangan, Jakarta 11650
Tlp./Fax: +62215871335

ISSN: 2460-8424
E-ISSN: 2655-7274

This journal is indexed by:


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

Analytics Made Easy - StatCounter
View My Stats