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remain unchanged and have loading coefficients greater than 0.5. This means that
variables within a factor have high correlations with each other, indicating convergent

validity (see Table 8).
Table 8. Rotated Component Matrix

Source: Data analyzed in SPSS 27.0

Factor analysis of dependent variables: The result of the Kaiser-Meyer-Olkin
(KMO) test for dependent variables, with a KMO coefficient of 0.736 > 0.5 and a
significance level (Sig) < 0.05, indicates high significance, demonstrating correlations
among the observed variables in the overall dataset (see Table 9).

Table 9. KMO và Bartlett's Test for Dependent Variables
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .736

Bartlett's Test of Sphericity Approx. Chi-Square 241.701
df 3
Sig. <.001

Source: Data analyzed in SPSS 27.
The Eigenvalue is 2.367, which is greater than 1, and only one factor is extracted,
indicating the best summary information. The total variance extracted is 78.892%,
which is greater than 50%, indicating that the EFA model is appropriate. Therefore, the

extracted factor explains 78.892% of the variance in the observed variables. The EFA
analysis is completed as it has achieved statistical reliability. Thus, the scale can be
used for further analyses (see Table 10).

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