Examining the adoption of Apple Pay among generation Z in Vietnam

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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. 

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    • 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
    • Conceptualization
      Nguyen Minh Sang
    • Data curation
      Nguyen Minh Sang
    • Formal Analysis
      Nguyen Minh Sang
    • Methodology
      Nguyen Minh Sang
    • Software
      Nguyen Minh Sang
    • Visualization
      Nguyen Minh Sang
    • Writing – original draft
      Nguyen Minh Sang
    • Writing – review & editing
      Nguyen Minh Sang