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Corrected Item- Cronbach’s Alpha if
Construct & Item Mean SD
Total Correlation Item Deleted
AIBDA2 3.480 1.287 0.793 0.915

AIBDA3 3.370 1.158 0.874 0.849
Source. Compiled from SPSS 26 output
3.2. Exploratory factor analysis
The authors included all observed variables in the exploratory factor analysis (EFA),
which was conducted using the Principal Components extraction method with Varimax
rotation and a minimum loading threshold of 0.5. In the first iteration (KMO = 0.861,
Bartlett’s test p = 0.000), six factors were extracted (Eigenvalue = 1.038, total variance
explained = 61.983%). However, items MOC1, CRMC2, and NPDC4 exhibited cross-
loadings and were therefore removed. In the second iteration (KMO = 0.844, Bartlett’s
test p = 0.000), six factors were again extracted (Eigenvalue = 1.030, total variance
explained = 64.670%), with all loadings exceeding 0.45 and each observed variable clearly
loading on its corresponding latent factor (see Table 3). Thus, after removing problematic
items, the factors demonstrated discriminant validity and were deemed suitable for
subsequent analysis.
Table 3. Rotated component matrix
Component
Item
1 2 3 4 5 6
AIBDA1 0.916

AIBDA2 0.915

AIBDA3 0.867
MOC4 0.808

MOC3 0.790

MOC5 0.596
MOC2 0.540

NPDC3 0.781
NPDC1 0.774

NPDC2 0.731
EP1 0.762

EP3 0.746

EP2 0.678
EP4 0.571

BMC1 0.786
BMC2 0.705

BMC3 0.642

CRMC3 0.849

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