*3.2. Model Measurement*

The fit of the measurements was examined using validity and reliability standards. The ability of a measuring device (or objects) to consistently produce the same result is known as reliability. Validity is a measure of how accurately a notion is measured by a measuring tool (items). Since there are multicollinearity conditions, the actions that can be taken include lowering or eliminating indications with a high degree of association. The outcomes of VIF measurements at the manifest variable level for all latent variables in Table 1 are listed below, while a summary of the model's measurements after the multicollinearity test is shown in Table 2.


**Table 1.** Variable Manifest (VM) VIF Measurement Results.

Source: Compiled by the author.

**Table 2.** Summary of model measurements after multicollinearity test.


Source: Compiled by the author.

The statements in the questionnaire were valid at a significance level of 5%, where r counts surpassed r tables based on the validity and reliability results of the 30 samples (0.361). In this study, each variable's Cronbach's alpha value was greater than 0.06, which indicates the dependability of the variables.

The Fornell–Larcker criterion, a gauge of the anticipated degree of "difference" between items for various factors, was used to test discriminant validity. The correlation square was compared to the AVE of each factor to assess the discriminant validity of the model. The other numbers are the correlation coefficients between the factors, which are thought to have excellent discriminant validity when the AVE is greater than the correlation coefficient between the factor and the other factors. The value on the diagonal represents the square root of the AVE (Hair et al. 2017). Values off the diagonal are correlations, whereas values (on the diagonal) represent the square root of the AVE. The discriminant validity results are shown in Table 3.


**Table 3.** Discriminant validity matrix.

Source: Compiled by the author.
