Adoption of Mobile Payment Approach Extended the UTAUT 2

anna widayani

Abstract


One of the most widely used mobile payment applications in Indonesia is LinkAja. This product has received official permission from Bank Indonesia as digital technology innovation in transactions. The purpose of this study was to determine the use of the LinkAja mobile payment with the UTAUT 2 approach. This type of research is explanatory research with a quantitative approach. This research analysis technique is descriptive and inferential using the Structural Equation Model-Partial Least Square (SEM-PLS). The population in this study was LinkAja users in Indonesia with a sample size of 249 respondents. The results showed that performance expectancy, facilitating conditions, hedonic motivation, price value, and habit have a positive and significant effect on behavior intention. Effort expectancy and social influence have a negative and significant effect on behavior intention. Behavioral Intention has a positive and significant effect on Actual Usage on the use of the LinkAja mobile payment.


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Andrianto, A. (2020). Faktor Yang Mempengaruhi Behavior Intention Untuk Penggunaan Aplikasi Dompet Digital Menggunakan Model Utaut2. Jurnal Ilmiah Ekonomi Bisnis, 25(2), 111–122. https://doi.org/10.35760/eb.2020.v25i2.2412

Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50, 418–430. https://doi.org/10.1016/j.chb.2015.04.024

Brown, T. A. (2006). Confirmatory Factor Analysis for Applied Research Second Edition.

Brown, V. V. (2005). Model of Adoption of Technology A Baseline Model Test Households : and Extension Incorporating Life Cycle. 29(3), 399–426.

Cholifaturrosida, A., Mawardi, K., & Bafadhal, A. (2018). Pengaruh Hedonic Dan Utilitarian Motivation Terhadap Behavioral Intention Pada Pemilihan Tas Mewah (Survei Online Terhadap Konsumen Wanita yang membeli Tas Mewah Pada Store Urban Icon Di Surabaya). Jurnal Administrasi Bisnis, 55(2), 84–92.

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.5962/bhl.title.33621

Dodds, W. B., Monroe, K. B., Grewal, D., Dodds, B., & Monroe, B. (2013). Effects of Price, Brand, and store Information on Buyers’ Product Evaluations. 28(3), 307–319.

Dzulhaida, R., Rifaldi, R., & Giri, W. (2017). Di Indonesia Dengan Menggunakan Model Modifikasi Unified Theory of Acceptance and Use Technology 2 ( Utaut 2 ). Majalah Ilmiah UNIKOM, 15(2), 155–166.

Ghalandari, K. (2012). The Effect of Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions on Acceptance of E-Banking Services in Iran: the Moderating Role of Age and Gender. Middle-East Journal of Scientific Research, 12(6), 801–807. https://doi.org/10.5829/idosi.mejsr.2012.12.6.2536

Gholami, R., Ogun, A., Koh, E., & Lim, J. (2010). Factors Affecting e-Payment Adoption in Nigeria. Journal of Electronic Commerce in Organizations, 8(4), 51–67. https://doi.org/10.4018/jeco.2010100104

Gunawan, & Flawrencia, C. (2019). Pengaruh Performance Expectancy Dan Social Influence Terhadap Behavioral Intention Di Aplikasi Hijabenka. Agora, 7(2).

Hair, J. F. J., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate Data Analysis (7th Edition) by Joseph F. Hair, William C. Black, Barry J. Babin, Rolph E. Anderson (z-lib.org).pdf (p. 761). p. 761.

Handayani, R. (2005). Analisis Faktor-Faktor Yang Mempengaruhi Minat Pemanfaatan Sistem Informasi Dan Penggunaan Sistem Informasi. 46(2),(Neurofibroma, schwannoma or a hybrid tumor of the peripheral nerve sheath), 113-116.

Hill, R. J., Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Contemporary Sociology, 6(2), 244. https://doi.org/10.2307/2065853

Ivan, D., & Karina M.R. Brahmana, R. (2018). Analisis Pengaruh Performance Expectancy Dan Effort Expectancy Terhadap Behavioral Intention Pada Online Marketplace. Agora, 6(2), 1–6.

Jati, N. J., & Laksito, H. (2012). Analisis Faktor-Faktor yang Mempengaruhi Minal Pemanfaatan dan Penggunaan Sistem E-Ticket. 1(2003), 1–15.

Kim, Sung S, N. K. M. (2005). A Longitudinal Model of Continued IS Use : An Integrative View of Four Mechanisms Underlying Postadoption Phenomena. 51(5), 741–755.

Lancelot Miltgen, C., Popovič, A., & Oliveira, T. (2013). Determinants of end-user acceptance of biometrics: Integrating the “big 3” of technology acceptance with privacy context. In Decision Support Systems (Vol. 56). https://doi.org/10.1016/j.dss.2013.05.010.

Leong, L. Y., Hew, T. S., Tan, G. W. H., & Ooi, K. B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 40(4), 5604–5620.

Li, Z., Zhang, H. mei, Zhao, H. feng, Zeng, H. ping, Liang, W., Wang, L. kui, & Wang, Y. feng. (2012). [Effect of Roux-en-Y gastric bypass on quality of life in non-obese patients with type 2 diabetes mellitus]. Zhonghua Wei Chang Wai Ke Za Zhi = Chinese Journal of Gastrointestinal Surgery, 15(11), 1136–1138.

Limayem, M., Hirt, S. G., Cheung, C. M. K., & Hirt, S. G. (2007). How Habit Limits the Predictive Power Intention : The Case of Information Systems Continuance. 31(4), 705–737.

Mowen, John C & Minor, M. (2002). Perilaku Konsumen. Edisi 5, Jilid 1 & Jilid 2. Jakarta. Erlangga: Alih Bahasa Dwi Kartini Yahya.

Muntianah, S. T., Astuti, E. S., & Azizah, D. F. (2012). Pengaruh Minat Perilaku Terhadap Actual Use Teknologi Informasi Dengan Pendekatan Technology Acceptance Model (TAM) (Studi kasus pada kegiatan belajar Mahasiswa Fakultas Ilmu Administrasi Universitas Brawijaya Malang). Profit, 6(1), 88–113.

Murray, K. B. (2007). Explaining Cognitive Lock-In : The Role of Skill- Based Habits of Use in Consumer Choice. 34(June), 77–88.

Ng, B., & Kwok, K. . (2017). Emergence of Fintech and Cybersecurity in a Global Financial Centre: Strategic Approach by a Regulator. Journal of Financial Regulation and Compliance, 25(4), 422–434. https://doi.org/https://doi.org/10.1108/JFRC-01-2017-0013

Pham, T. T. T., & Ho, J. C. (2015). The effects of product-related, personal-related factors and attractiveness of alternatives on consumer adoption of NFC-based mobile payments. Technology in Society, 43, 159–172. https://doi.org/10.1016/j.techsoc.2015.05.004

Rogers, E. M., Singhal, A., & Quinlan, M. M. (1995). Diffusion of innovations. In An Integrated Approach to Communication Theory and Research, Third Edition. https://doi.org/10.4324/9780203710753-35

Sedana, I. G. N., & Wijaya, S. W. (2012). Penerapan Model Utaut Untuk Memahami Penerimaan Dan Penggunaan Learning Management System Studi Kasus: Experential E-Learning of Sanata Dharma University. Jurnal Sistem Informasi, 5(2), 114. https://doi.org/10.21609/jsi.v5i2.271

Septiana, I., Salim, M., & Daulay, M. Y. I. (2020). Analysis the Effect of Habit and Perceived Enjoyment Mediated By Behavioural Intention To Adoption on Students Using Mobile Banking Bni. Managament Insight: Jurnal Ilmiah Manajemen, 15(1), 78–94. https://doi.org/10.33369/insight.15.1.78-94

Sivathanu, B. (2019). Adoption of digital payment systems in the era of demonetization in India: An empirical study. Journal of Science and Technology Policy Management, 10(1), 143–171. https://doi.org/10.1108/JSTPM-07-2017-0033

Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly: Management Information Systems, 19(4), 561–568. https://doi.org/10.2307/249633

Thompson, R. L., Higgins, C. A., & Howell, J. M. (1994). Influence of experience on personal computer utilization: Testing a conceptual model. Journal of Management Information Systems, 11(1), 167–187. https://doi.org/10.1080/07421222.1994.11518035

Venkatesh, V., & Davis, F. D. (2000). Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

Venkatesh, V., & Hall, R. H. S. S. of B. U. of M. V. M. (2003). User Acceptance Of Information Technology: Toward A Unified View. Microvascular Research. https://doi.org/10.1006/mvre.1994.1019

Venkatesh, V., Thong, J. Y. L., & 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. Retrieved from https://pdfs.semanticscholar.org/6256/0e2001480fd1f22558ce4d34ac93776af3e6.pdf%0Ahttps://pdfs.semanticscholar.org/6256/0e2001480fd1f22558ce4d34ac93776af3e6.pdf?_ga=2.124539978.1994179764.1540339706-2125081534.1540339706

Yang, K., & Forney, J. C. (2013). The moderating role of consumer technology anxiety in mobile shopping adoption: Differential effects of facilitating conditions and social influences. Journal of Electronic Commerce Research, 14(4), 334–347.

Zeithaml, V. A. (1988). Consumer Perceptions A Means-End Value : Quality , and Model Synthesis of Evidence. 52(3), 2–22.




DOI: https://doi.org/10.22515/relevance.v4i2.4019

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