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Table 6. Results of Exploratory Factor Analysis (EFA)
Component
1 2 3 4
DI2 .853
DI3 .842
DI4 .812
DI1 .781
EC4 .814
EC2 .771
EC3 .796
EC1 .752
FI2 .830
FI1 .794
FI4 .735
FI3 .662
IN2 .753
IN3 .781
IN4 .675
IN1 .699
Source: SPSS analysis results
The rotated component matrix shows that the factor loadings of all variables are greater
than 0.55; therefore, the model demonstrates good explanatory and analytical capability.
Table 7. Results of KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy. .863
Bartlett's Test of Approx. Chi-Square 687.005
Sphericity df 15
Sig. .000
Source: SPSS analysis results
The results show that the KMO value is 0.863, satisfying the condition 0.5 ≤ KMO ≤ 1.
Bartlett’s test yields a value of 687.005 with a significance level of Sig = 0.000 <
0.005. Therefore, the null hypothesis (H₀: the observed variables are not correlated in the
population) is rejected. Thus, the hypothesis that the correlation matrix is an identity
matrix is rejected, meaning that the variables are correlated with each other and meet
the conditions for factor analysis.
4.2.3. Regression analysis results
Table 8. Regression Coefficients
Unstandardized Standardized Collinearity
Coefficients Coefficients Statistics
Model B Std. Error Beta t Sig. Tolerance VIF
1 (Constant) .250 .153 1.651 .100
DI .463 .059 .375 6.516 .000 .395 2.538
EC .252 .054 .272 4.530 .001 .352 2.876
FI .071 .041 .074 1.426 .041 .487 2.154
IN .096 .048 .078 1.384 .000 .464 2.452
Source: SPSS analysis results

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