Table 3

Summary of the measurement model statistics

ConstructItemsFactor loadingsAVE, CR, and α
Performance expectancy and facilitating conditionsMobile payment is useful to save time0.833AVE = 0.76
CR = 0.77
α = 0.72
Mobile payment would enable me to conduct tasks (financial transfer, shopping) more easily0.854
Mobile payment would increase my productivity0.792
Mobile payment would improve my work performance0.800
I have the resources necessary to use mobile payment0.867
I have the knowledge necessary to use mobile payment0.816
Mobile payment is compatible with other systems I use0.704
Social influenceCelebrities can influence my behaviour in using mobile payment0.748AVE = 0.74 CR = 0.81
α = 0.70
Family members can influence my behaviour in using mobile payment0.832
Friends/colleagues can influence my behaviour in using mobile payment0.800
Perceived technology securityI feel completely secure operating with mobile payment0.769AVE = 0.62
CR = 0.78
α = 0.69
Mobile payment is a secure means for sharing sensitive information0.795
My safety concerns are only with mobile payments0.835
Hedonic motivationUsing mobile payment is fun0.762AVE = 0.58 CR = 0.81
α = 0.71
Depending on cash for payment is stressful0.728
Adoption of mobile paymentI have been using mobile payment methods for some time now0.899AVE = 0.72 CR = 0.88
α = 0.72
I am likely to increase the use of mobile payment in my life0.924
I always recommend to others to use mobile payments0.922

Note(s): Fit indices χ2 (485) = 465.37, (p < 0.01), CFI = 0.97, GFI = 0.91, NFI = 0.92, TLI = 0.98, RMSEA = 0.032, SRMR = 0.03; FL, factor loading; AVE, average variance extracted; CR, composite reliability; α, Cronbach's alpha; χ2, Chi-square; CFI, comparative fit index; GFI, goodness-of-fit index; NFI, normed fit index; TLI, Tucker–Lewis index; RMSEA, root mean square error of approximation; SRMR, standardized root mean residual

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