Determinants of intention to use e-wallet in Generation Z


  • Safitri Dwi Rahmadhani Universitas Negeri Jakarta
  • Agung Dharmawan Buchdadi Universitas Negeri Jakarta
  • Muhammad Fawaiq Universitas Negeri Jakarta
  • Budi Agung Prasetya Universitas Negeri Jakarta



E-wallet, Generation Z, Technology Acceptance Model, Perceived Security, Social Influence


Generation Z, as true digital natives or generations born and raised with the internet, is familiar with digital products. One digital product is an e-wallet used as an online payment method. This study aims to discover the factors that influence behavioural intention to use e-wallets in generation Z that use an e-wallet and live in Greater Jakarta. The theoretical basis of this study is the Technology Acceptance Model (TAM), where the variables involved are behavioural intention to use, perceived ease of use, and perceived usefulness, and two other factors, i.e., perceived security and social influence. A total of 245 respondents were collected by online survey and analysed with Structural Equation Modelling (SEM). This study finds that social influence affects perceived ease of use positively and significantly. Perceived ease of use influences perceived usefulness positively and significantly. Perceived ease of use and perceived usefulness positively influence behavioural intention to use. However, social influence does not influence perceived usefulness, and perceived security does not influence behavioural intention to use. This study's originality resides in its examination of reported ease of use and perceived usefulness as mediating variables of social influence on Generation Z's propensity to use e-wallets.


Almaiah, M. A. (2018). Acceptance and usage of a mobile information system services in University of Jordan. Education and Information Technologies, 23(5), 1873–1895.

Alsaleh, M., Alomar, N., & Alarifi, A. (2017). Smartphone users: understanding how security mechanisms are perceived and new persuasive methods. PLOS ONE, 12(3), 1–35.

Amsal, A. A., Putri, S. L., Rahadi, F., & Fitri, M. E. Y. (2021). Perceived satisfaction and perceived usefulness of e- learning: The role of interactive learning and social influence. Proceedings of the 3rd International Conference on Educational Development and Quality Assurance (ICED-QA 2020), 535–541.

Andrew, J. V., Ambad, S. N. A., & Tan, K. E. (2020). A model of factors influencing consumers intention to use e-wallet system in Malaysia: A systematic review. Malaysian Journal of Business and Economics (MJBE), 53–53.

Arpaci, I., Yardimci Cetin, Y. Y., & Turetken, O. (2015). Impact of perceived security on organizational adoption of smartphones. Cyberpsychology, Behavior, and Social Networking, 18(10), 602–608.

Aslam, W., Ham, M., & Arif, I. (2017). Consumer behavioral intentions towards mobile payment services: An empirical analysis in Pakistan. Market-Tržište, 29(2), 161– 176.

APJII. (2018). Potret Zaman Now Pengguna dan Perilaku Internet Indonesia!. Retrived June 14, 2022, from

APJII. (2020). Laporan survei internet APJII 2019-2020 (Q2). Retrieved June 18, 2022, from

National Cyber and Crypto Agency. (2021). Laporan Tahunan Monitoring Keamanan Siber Tahun 2021. Retrived May 25, 2022, from

Balapour, A., Nikkhah, H. R., & Sabherwal, R. (2020). Mobile application security: Role of perceived privacy as the predictor of security perceptions. International Journal of Information Management, 52.

Bank Indonesia. (2016). Peraturan Bank Indonesia Nomor 18/40/PBI/2016 tentang Penyelenggaraan Pemrosesan Transaksi Pembayaran. Retrieved May 15, 2022, from

Bank Indonesia. (2019). Publikasi Laporan Perekonomian Indonesia. Retrieved May 15, 2022, from

Bank Indonesia. (2021). The Red Book Statistics. Retrieved May 15, 2022, from

Barkhordari, M., Nourollah, Z., Mashayekhi, H., Mashayekhi, Y., & Ahangar, M. S. (2017). Factors influencing adoption of e-payment systems: An empirical study on Iranian customers. Information Systems and E-Business Management, 15(1), 89–116.

Bencsik, A., Juhász, T., & Horváth-Csikós, G. (2016). Y and Z generations at workplaces. Journal of Competitiveness, 6(3), 90–106.

Chandra, Y. U., & Hartono, S. (2018). Analysis factors of technology acceptance of cloud storage: A case of higher education students use Google Drive. International Conference on Information Technology Systems and Innovation), Bandung: 22–26 October, 2018. Page 188–192.

Chen, L., & Aklikokou, A. K. (2020). Determinants of e-government adoption: Testing the mediating effects of perceived usefulness and perceived ease of use. International Journal of Public Administration, 43(10), 850–865.

Choi, M. J., Lee, S.-J., Lee, S. J., Rho, M. J., Kim, D.-J., & Choi, I. Y. (2021). Behavioral intention to use a smartphone usage management application between a non- problematic smartphone use group and a problematic use group. Frontiers in Psychiatry, 12, 571795.

Daragmeh, A., Lentner, C., & Sági, J. (2021). Fintech payments in the era of COVID-19: Factors influencing behavioral intentions of “Generation X” in Hungary to use mobile payment. Journal of Behavioral and Experimental Finance, 32.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–325.

F5 Labs. (2017). Lessons Learned from a Decade of Data Breaches. Retrieved June, 5, 2022, from

GlobalWebIndex. (2019). The Youth of the Nations: Global Trends Among Gen Z. Retrieved June 2, 2022, from

Hair, J.F., Celsi, M. W., Money, A. H., Samouel, P., & Page, M. J. (2015). Essentials of business research methods. Taylor & Francis Group.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2018). Multivariate data analysis (Eight). Cengage.

Halim, F., Efendi, E., Butarbutar, M., Malau, A. R., & Sudirman, A. (2020). Constituents driving interest in using e-wallets in generation Z. Proceeding on International Conference of Science Management Art Research Technology, Bandung. 101–116.

Hankun, H., Yafang, L., Xuemei, H., & Jing, F. (2016). A comparative study of China and US users’ acceptance of online payment. 2016 13th International Conference on Service Systems and Service Management, China. 101–116.

IDADX. (2022). Indonesia anti-phishing data exchange Periode Q1 2022. Retrieved April 3, 2022, from

Internet Crime Complain Center. (2021). Internet Crime Report. Federal Bureau of Investigation. Retrieved June 7, 2022, from

IPSOS. (2019). The Evolution of the Digital Wallet: Driving the Next Wave of Growth. Retrieved April 17, 2022, from

Kar, A. K. (2020). What affects usage satisfaction in mobile payments? Modelling user generated content to develop the “Digital service usage satisfaction model.” Information Systems Frontiers, 23, 1341–1261.

Lai, P.C. (2017). The Literature Review of Technology Adoption Models and Theories for The Novelty Technology. Journal of Information Systems and Technology Management, 14(1), 21–38.

Lee, E.-Y., Lee, S.-B., & Jeon, Y. J. J. (2017). Factors influencing the behavioral intention to use food delivery apps. Social Behavior and Personality: An International Journal, 45(9), 1461–1473.

Liébana-Cabanillas, F., Molinillo, S., & Japutra, A. (2021). Exploring the determinants of intention to use p2p mobile payment in Spain. Information Systems Management, 38(2), 165–180.

Lwoga, E. T., & Lwoga, N. B. (2017). User acceptance of mobile payment: The effects of user-centric security, system characteristics and gender. The Electronic Journal of Information Systems in Developing Countries, 81(1), 1–24.

Madan, K., & Yadav, R. (2016). Behavioural intention to adopt mobile wallet: A developing country perspective. Journal of Indian Business Research, 8(3), 227– 244.,

Maduku, D. K. (2017). Understanding e-book continuance intention: Empirical evidence from e-book users in a developing country. Cyberpsychology, Behavior, and Social Networking, 20(1), 30–36.

Meyliana, M., Fernando, E., & Surjandy, S. (2019). The influence of perceived risk and trust in adoption of fintech services in Indonesia. Communication and Information Technology Journal, 13(1), 31–37.

Min, S., So, K. K. F., & Jeong, M. (2019). Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model. Journal of Travel & Tourism Marketing, 36(7), 770–783.

Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56–73.

Patel, K. J., & Patel, H. J. (2018). Adoption of internet banking services in Gujarat: An extension of TAM with perceived security and social influence. International Journal of Bank Marketing, 36(1), 147–169.

Phan, T. N., Ho, T. V., & Le-Hoang, P. V. (2020). Factors affecting the behavioral intention and behavior of using e-wallets of youth in Vietnam. The Journal of Asian Finance, Economics and Business, 7(10), 295–302.

Raza, S. A., Umer, A., & Shah, N. (2017). New determinants of ease of use and perceived usefulness for mobile banking adoption. International Journal of Electronic Customer Relationship Management, 11(1), 44–65.

Sathye, S., Prasad, B., Sharma, D., Sharma, P., & Sathye, M. (2018). Factors influencing the intention to use of mobile value-added services by women-owned microenterprises in Fiji. The Electronic Journal of Information Systems in Developing Countries, 84(2), 1–10.

Schmitz, A., Díaz-Martín, A. M., & Yagüe Guillén, M. J. (2022). Modifying UTAUT2 for a cross-country comparison of telemedicine adoption. Computers in Human Behavior, 130, 1–11.

Simorangkir, Z. Z., & Afgani, K. F. (2021). The analysis on factors influencing the use of mobile payment system among generation Z in Bekasi city. Advanced International Journal of Business, Entrepreneurship and SMEs, 3(9), 334–348.

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.

Teo, S. C., Law, P. L., & Koo, A. C. (2020). Factors affecting adoption of e-wallets among youths in Malaysia. Journal of Information System and Technology Management, 5(19), 39–50.

Thakkar, J. J. (2020). Structural equation modelling: application for research and practice (with AMOS and R). Singapore: Springer Singapore.

To, A. T., & Trinh, T. H. M. (2021). Understanding behavioral intention to use mobile wallets in Vietnam: Extending the tam model with trust and enjoyment. Cogent Business & Management, 8(1), 1–14.

Tsai, W.-H., Wu, Y.-S., Cheng, C.-S., Kuo, M.-H., Chang, Y.-T., Hu, F.-K., Chu, C. M. (2021). A technology acceptance model for deploying masks to combat the COVID-19 Pandemic in Taiwan (My Health Bank): Web-based cross-sectional survey study. Journal of Medical Internet Research, 23(4), 1–19.

Tu, J.-C., & Hu, C.-L. (2018). A Study on the Factors Affecting Consumers’ Willingness to Accept Clothing Rentals. Sustainability, 10(11), 1–30.

Vărzaru, A. A., Bocean, C. G., Rotea, C. C., & Budică-Iacob, A.-F. (2021). Assessing antecedents of behavioral intention to use mobile technologies in e-commerce. Electronics, 10(18), 2231.

Verma, P., & Sinha, N. (2018). Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service. Technological Forecasting and Social Change, 126, 207–216.

Western Governors University. (2019). Who is gen Z and how will they impact the workplace?. Retrieved April 28, 2022, from

Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232.

Zhao, Y., & Bacao, F. (2021). How does the pandemic facilitate mobile payment? An investigation on users’ perspective under the Covid-19 pandemic. International Journal of Environmental Research and Public Health, 18(3), 1–22.




How to Cite

Rahmadhani, S. D., Buchdadi, A. D., Fawaiq, M., & Prasetya, B. A. . (2023). Determinants of intention to use e-wallet in Generation Z. BISMA (Bisnis Dan Manajemen), 15(1), 60–77.



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