Examining the adoption of Apple Pay among generation Z in Vietnam
-
DOIhttp://dx.doi.org/10.21511/bbs.19(1).2024.04
-
Article InfoVolume 19 2024, Issue #1, pp. 34-47
- 533 Views
-
276 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
This study examines the level of knowledge, use, and determinants determining the adoption of Apple Pay among Generation Z customers in Vietnam. An online survey with 339 participants aged 18-26 was done using quantitative methods. The participants were recruited using social media platforms. The study model included elements from technological acceptance theories, such as effort expectation, perceived risk, perceived value, and convenience. The measurements were ensured to be reliable and genuine. The hypotheses were tested by analyzing the data using partial least squares structural equation modeling. The study’s results suggest that the data collected through PLS-SEM analysis provide evidence in support of the hypotheses proposing that factors such as Mobile User Skillfulness, Personal Innovation, Perceived Usefulness, Effort Expectation, Convenience, and Perceived Value have a positive influence on individuals’ Intentions to Use Apple Pay in Vietnam. Furthermore, the study revealed that the variables of Perceived Risk and Social Image did not have a statistically significant influence. The findings suggest that the pragmatic orientation of Generation Z towards the functionality and ease of use of Apple Pay has a significant impact on their adoption of this payment system in Vietnam. The study offers banks in Vietnam significant insights regarding the promotion of mobile wallet adoption among the younger demographic. Adoption may be increased by presenting Apple Pay as a practical and convenient application.
Acknowledgment
The author would like to thank everyone who filled out the survey. Without the help of everyone involved and the Ho Chi Minh University of Banking (Vietnam), this study would not have been possible.
- Keywords
-
JEL Classification (Paper profile tab)D12, E44, G21, G41
-
References46
-
Tables4
-
Figures2
-
- Figure 1. Research model for examining the adoption of Apple Pay among generation Z in Vietnam
- Figure 2. Results of PLS-SEM model analysis to examine Apple Pay adoption among Vietnam’s generation Z
-
- Table 1. Descriptive metrics for demographic variables
- Table 2. Testing the reliability level of variables in detail
- Table 3. Testing the stability and discriminant validity of variables
- Table 4. Results of hypotheses evaluation
-
- Agarwal, R., & Prasad, J. (1998). A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology. Information Systems Research, 9(2), 204-215.
- Ai, Y. J., Sze, C. C., Fern, Y. S., Toong, T. H., & Chian, C. B. (2021). The use of e-wallet among Gen-Y in Malaysia during the global pandemic: An analysis using PLS-SEM. Applied Quantitative Analysis, 1(1), 1-8.
- Alfany, Z., Saufi, A., & Mulyono, L. E. H. (2019). The Impact of Social Influence, Self-Efficacy, Perceived Enjoyment, and Individual Mobility on Attitude toward use and Intention to use Mobile Payment of Ovo. Global Journal of Management and Business Research, 19(E7), 1-8.
- Al-Qudah, A. A., Al-Okaily, M., Alqudah, G., & Ghazlat, A. (2022). Mobile payment adoption in the time of the COVID-19 pandemic. Electronic Commerce Research.
- 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), 258-274.
- Apple. (2023). Apple reports first quarter results. Apple.
- Appleinsider. (2023). Apple Pay finally launches in Vietnam. Appleinsider.
- Cho, G., Hwang, H., Sarstedt, M., & Ringle, C. M. (2020). Cutoff criteria for overall model fit indexes in generalized structured component analysis. Journal of Marketing Analytics, 8(4), 189-202.
- Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.
- de Luna, I. R., Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2019). Mobile payment is not all the same: The adoption of mobile payment systems depending on the technology applied. Technological Forecasting and Social Change, 146, 931-944.
- Effendy, F., Hurriyati, R., & Hendrayati, H. (2021). Perceived usefulness, perceived ease of use, and social influence: Intention to use e-wallet. 5th Global Conference on Business, Management and Entrepreneurship (GCBME 2020) (pp. 311-315).
- Finken, S., & Heiduk, L. (2021). Factors influencing the acceptance of proximity mobile payment in Germany: The example of Apple Pay. Journal of Payments Strategy & Systems, 15(1), 92-108.
- Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50.
- Gerrard, P., & Barton Cunningham, J. (2003). The diffusion of Internet banking among Singapore consumers. International Journal of Bank Marketing, 21(1), 16-28.
- Hair, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106-121.
- Hair, J., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling. SAGE.
- Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (Eds.), New Challenges to International Marketing, Vol. 20 (pp. 277-319). Emerald Group Publishing Limited.
- Homburg, C., Klarmann, M., & Vomberg, A. (2022). Handbook of market research. Springer.
- Ilieva, G., Yankova, T., Dzhabarova, Y., Ruseva, M., Angelov, D., & Klisarova-Belcheva, S. (2023). Customer Attitude toward Digital Wallet Services. Systems, 11(4), 185.
- Irimia-Diéguez, A., Velicia-Martín, F., & Aguayo-Camacho, M. (2023). Predicting Fintech Innovation Adoption: The Mediator Role of Social Norms and Attitudes. Financial Innovation, 9(1), 36.
- Kim, J., & Kim, M. (2022). Intention to Use Mobile Easy Payment Services: Focusing on the Risk Perception of COVID-19. Frontiers in Psychology, 13, 878514.
- Liébana-Cabanillas, F., GarcíaMaroto, I., Muñoz-Leiva, F., & Ramos-de-Luna, I. (2020). Mobile Payment Adoption in the Age of Digital Transformation: The Case of Apple Pay. Sustainability, 12(13), 5443.
- Liébana-Cabanillas, F., Marinković, V., & Kalinić, Z. (2017). A SEM-neural network approach for predicting antecedents of m-commerce acceptance. International Journal of Information Management, 37(2), 14-24.
- Liu, L., & Zhou, M. (2017). Empirical study of influencing factors of the users’ intention based on the survey of apple pay users. Journal of Interdisciplinary Mathematics, 20(6-7), 1391-1395.
- Lu, H., & Yu-Jen Su, P. (2009). Factors affecting purchase intention on mobile shopping web sites. Internet Research, 19(4), 442-458.
- Mew, J., & Millan, E. (2021). Mobile wallets: Key drivers and deterrents of consumers’ intention to adopt. The International Review of Retail, Distribution and Consumer Research, 31(2), 182-210.
- Mohd Amir, R. I., Mohd, I. H., Saad, S., Abu Seman, S. A., & Tuan Besar, T. B. H. (2020). Perceived Ease of Use, Perceived Usefulness, and Behavioral Intention: The Acceptance of Crowdsourcing Platform by Using Technology Acceptance Model (TAM). In N. Kaur & M. Ahmad (Eds.), Charting a Sustainable Future of ASEAN in Business and Social Sciences (pp. 403-410). Springer.
- Mun, Y. P., Khalid, H., & Nadarajah, D. (2017). Millennials’ Perception on Mobile Payment Services in Malaysia. Procedia Computer Science, 124, 397-404.
- Nunnally, J. C. (1975). Psychometric Theory – 25 Years Ago and Now. Educational Researcher, 4(10), 7-21.
- Pal, D., Vanijja, V., & Papasratorn, B. (2015). An Empirical Analysis towards the Adoption of NFC Mobile Payment System by the End User. Procedia Computer Science, 69, 13-25.
- Pavlov, G., Maydeu-Olivares, A., & Shi, D. (2021). Using the Standardized Root Mean Squared Residual (SRMR) to Assess Exact Fit in Structural Equation Models. Educational and Psychological Measurement, 81(1), 110-130.
- Pu, X., Chan, F. T. S., Chong, A. Y.-L., & Niu, B. (2020). The adoption of NFC-based mobile payment services: An empirical analysis of Apple Pay in China. International Journal of Mobile Communications, 18(3), 343-371.
- Purohit, S., Kaur, J., & Chaturvedi, S. (2022). Mobile payment adoption among youth: generation z and developing country perspective. Journal of Content, Community and Communication, 15(8), 194-209.
- Raj, L. V., Amilan, S., & Aparna, K. (2023). Factors influencing the adoption of cashless transactions: Toward a unified view. South Asian Journal of Marketing, ahead-of-print(ahead-of-print).
- Ramos-de-Luna, I., Montoro-Ríos, F., & Liébana-Cabanillas, F. (2016). Determinants of the intention to use NFC technology as a payment system: An acceptance model approach. Information Systems and E-Business Management, 14(2), 293-314.
- Saputri, M. E. (2022). The effect of performance expectation, effort expectancy, social influence, perceived risk, and perceived cost on the intention of using mobile payment in Indonesia. Jurnal Sosioteknologi, 21(1), 9-21.
- Slade, E., Dwivedi, Y. K., Piercy, N. C., & Williams, M. D. (2015a). Modeling Consumers’ Adoption Intentions of Remote Mobile Payments in the United Kingdom: Extending UTAUT with Innovativeness, Risk, and Trust. Psychology & Marketing, 32(8), 860-873.
- Slade, E., Williams, M., Dwivedi, Y., & Piercy, N. (2015b). Exploring consumer adoption of proximity mobile payments. Journal of Strategic Marketing, 23(3), 209-223.
- Sleiman, K. A. A., Juanli, L., Lei, H., Liu, R., Ouyang, Y., & Rong, W. (2021). User Trust levels and Adoption of Mobile Payment Systems in China: An Empirical Analysis. SAGE Open, 11(4), 21582440211056599.
- Song, X., & Wang, R. (2022). Research on Influencing Factors of Intention to Use E-CNY. Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022) (pp. 241-254).
- Statista. (2023). Mobile POS Payments – Worldwide. Statista.
- Tam, C., Santos, D., & Oliveira, T. (2020). Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model. Information Systems Frontiers, 22(1), 243-257.
- Ugoni, A., & Walker, B. F. (1995). The Chi square test: An introduction. COMSIG Review, 4(3), 61-64.
- 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.
- Wei, M.-F., Luh, Y.-H., Huang, Y.-H., & Chang, Y.-C. (2021). Young Generation’s Mobile Payment Adoption Behavior: Analysis Based on an Extended UTAUT Model. Journal of Theoretical and Applied Electronic Commerce Research, 16(4), 618-637.
- Zeithaml, V. A. (1988). Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence. Journal of Marketing, 52(3), 2-22.