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224 Geby Firstania Rolanda Rambe (2022)

4.2 Composite Realibility

At this stage, a definition of composite reliability can be defined as a type of part that is used to test the reliability of indicators constructed
from a variable. According to (Anuraga et al., 2017), a variable falls into the composite reliability category if its value is more than 0.7.

4.3 Cronbach Alpha

The researcher additionally makes reference to the Cronbach alpha value in order to bolster the reliability test based on the data supplied
above. According to (Anuraga et al., 2017), a variable is regarded to be dependable if the Cronbach Alpha value is more than 0.7.

4.4 Inner Analysis Model

The structural model or inner model test is used to ascertain the relationship between constructs, their significance, and the R-square (R2),
f-square effect size (f2). The structural model was analysed in this study utilising bootstrapping and blindfolding approaches with a
significance level of 0.05 in SmartPLS version 3.0.

4.4.1 Score Analysis R-squared (R2)

The R-squared (R2) test is used to determine a structural model's Goodness of Fit (GOF). The R-squared (R2) value is used to determine the
degree to which specific independent latent variables have an effect on the dependent latent variable. According to Chin (1998), an R2 value
of 0.67 implies that the model is considered to be of high quality.

4.4.2 F-Square (R2) Analysis

The f-square statistic is used to determine the predictor variable's effect on the dependent variable. f Test of the square (effect size): In this
study, the f square value represents the magnitude of the effect of endogenous variables on exogenous variables. According to Henseler
(2009), the following criteria should be used to evaluate the f square: 0.02 f 0.15 indicates a modest influence, 0.15 f 0.35 indicates a medium
effect, and f 0.35 indicates a significant effect.

4.4.3 Determine the importance and amount of latent variables' influence.

To examine the effect of the independent latent variable on the dependent latent variable, the t test was performed. The path coefficient
hypothesis is investigated at this stage. The recommended value is greater than 1.96. (Hair et al, 2011). By examining the route analysis
coefficient, one may determine the size of each independent latent variable's influence (Widarjono, 2015: 277).

4.4.4 Validation of hypotheses

Validation of hypotheses
The Structural Equation Model (SEM) technique was used to test hypotheses, with Partial Least Squares (PLS) software. SEM is a subtype
of simple linear regression analysis, which is the use of statistical techniques to the investigation of the relationship between two research
variables.

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