## 5 Data Analysis Results and Research Findings
### Exploratory Factor Analysis (EFA)
[PERSON] [107] suggested checking for data suitability for factor analysis by confirming the value of the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy value of 0.6 and above, as recommended by [PERSON] and [PERSON] [114], and [PERSON]'s Test of Sphericity value should be significant at 0.05 and smaller. This study applies the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and [PERSON]'s Test of Sphericity to assess the factorability of the responses and ensure that the factor analysis is appropriate [107; 114; 124]. [PERSON] [118] suggested applying KMO and [PERSON]'s test when a researcher uses component analysis with a small sample size. The analysis in Table 2 shows the Kaiser-Meyer-Olkin (KMO) measure of the sampling adequacy value of 0.731, and [PERSON]'s test of statistical significance indicates \(<\)0.001 (\(p\leq 0.001\)); therefore, the factor analysis is appropriate for the study, as specified by [PERSON] [107] and [PERSON] and [PERSON] [114]. * Communality Coefficients Values Communality of a variable, as stated by [PERSON] [118] and [PERSON] and [PERSON] [114], connotes the number of variances on a variable that account for the set of factors, which is the sum of the square loadings for the variable through the factors. The communality values for the factors measured were also considered in addition to the factor loading of the items. [PERSON] and [PERSON] [114] described very low communality values as an indication that the variables are unrelated to other variables; therefore, the variable is usually an outlier among different variables and should be ignored or deleted in factor analysis. Thus, this study removed common variance values of less than 0.40 from the measurement scales, as [PERSON] and [PERSON] [114] recommended. Therefore, component item 1 for government framework (GovtF1) was removed, as shown in communality values for the components in Table 3.
* Total Variance Explained The principal components analysis determines and extracts the number of factors (components) with eigenvalues of 1 or more, which is the common criterion to ascertain useful factors. According to [PERSON] [118], the eigenvalue indicates the number of variances reported by each factor. This is relevant to determining the number of components that meet [PERSON]'s criterion of eigenvalues greater than 1. However, six components are retained in Table 4, as [PERSON] and [PERSON] [114] specify that a reasonable number of four to six factors is optimal for the total variance explained. The result analysis explained that 70.079% of the total variance consisted of the six factors (privacy, governance framework, performance expectancy, information security, perceived risk, and government intentions), as supported by [PERSON] and [PERSON] [114]. * Rotated Component Matrix To further interpret the retention of the six components, the study analysed the components' rotation matrix with varimax and Kaiser normalisation rotation. The rotation component matrix analysis results in Table 5 show that the first factor was the performance expectancy scale with four (4) items, and the factor loadings ranged between 0.780 and 0.886, explaining 24.798% of the variance. The second factor was the governance framework scale, with seven items, and the factor loading ranged between 0.454 and 0.762, explaining 12.707% of the variance. The third measurement scale is the information security scale with four items, and the factor loading ranged between 0.908 and 0.952, explaining 10.950% of the variance. The fourth factor is privacy, with four items, and the factor loadings ranged between 0.704 and 0.907, explaining 8.789% of the variance. The fifth factor is perceived risk, and the scale has four items with factor loadings ranging between 0.705 and 0.849, explaining 7.771% of the variance. Lastly, the sixth factor is the government intention variable with six items, and the factor loading ranged between 0.442 and 0.848, explaining 5.064% of the variance.