3.2.1. Exploratory Factor Analysis

Exploratory factor analysis was conducted based on 98 observations for two dimensions: accessibility and effectiveness. Variables D4 and D5 did not meet the normality assumption. They were therefore removed from the EFA model. Variable D1 did not load correctly on the expected factors—accessibility. Eventually, eight variables: D2, D3, D6, D7, E1, E2, E3, and E4 were left. The principal component analysis (PCA) and promax rotation with Kaiser normalisation were used to extract two components (Table 5). The Kaiser-Meyer-Olkin measure of sampling adequacy (KMO) equalled 0.73 > 0.6. The KMO value considered as correct is 0.6. Bartlett's test of sphericity provided a significant result (χ<sup>2</sup> = 201.125; df = 28, *p* < 0.0001). The probability *p* should be smaller than 0.05, thereby indicating that the values are correct and the sample size is sufficient for the factor analysis.


**Table 5.** Pattern matrix for the EFA model.

Extraction Method: Principal Component Analysis. Rotation Method: Promax with Kaiser Normalization, a. Rotation converged in 3 iterations.

PCA retained two factors with eigenvalues greater than 1. The total variance explained by the EFA model was equal to 56% (Table 6), which should be greater than 50% [78]. For eight variables, the factor loadings ranged from 0.638 to 0.837 and are greater than the recommended 0.35 cut-off point [79]. A reliability analysis showed that the extracted model was acceptable since the Cronbach's alpha coefficients for accessibility (0.666) and effectiveness (0.663) were greater than 0.6 [80]. Those values (Table 6) allowed for further factor analysis [65,81].


**Table 6.** Eigenvalues and total variance explained by the EFA model.

Extraction Method: Principal Component Analysis.
