## 5 Data Analysis Results and Research Findings
### Exploratory Factor Analysis (EFA)  
Researchers commonly use EFA to discover a small number of latent constructs for a more significant number of observed variables to identify influencing factors and analyse their relationship [113]. However, [PERSON] [110] and [PERSON] [109] highlighted various purposes for which factor analysis can be conducted: to evaluate the score validity of measurement variables (questions), develop a theory regarding the constructs, discover the relationship between factor scores and variables, and further analyse the relationship of the variables. Hence, this study applied EFA to explore the measurement variables (questions) score validity and their relationship to the identified constructs to measure government intention to adopt cloud services. Several researchers acknowledged conducting EFA using various factorisation techniques such as principal component analysis, maximum likelihood analysis, and principal axis factoring [114; 109; 115]. Principal component analysis describes the relationship between the variables by developing components that summarise the relationship between the correlations [110; 114]. Principal axis factoring is the least-squares inference of common factors, while maximum likelihood analysis estimates the values for model parameters and exhibits hypothetical benefits [116]. According to [PERSON] and [PERSON] [117] and [PERSON] and [PERSON] [116], many researchers have argued that there is no evidence regarding preferred techniques to adopt for different factor patterns or sample sizes. However, [PERSON] and [PERSON] [117] acknowledged the importance of principal components analysis for all uniform variable distributions and samples. Nevertheless, principal component analysis has been considered more efficient [118; 119], and thus, the researcher used principal component analysis for factorisation to ascertain the validity of the measurement scale. Researchers suggested that a sample size is required for reliable factors and that the sample size is determined as a function of the number of variables being analysed [118; 120]. However, [PERSON] and [PERSON]'s [120] findings show that the assessed component pattern is based on the number of variables that define the component's factor loadings. [PERSON] [118] further suggested that factor loadings should be greater than 0.40; however, \begin{table}
\begin{tabular}{c c c c} \hline
**S/N** & **Variables** & **Number of Measurement Items** & **Cronbach’s Alpha Reliability** \\ \hline
**1** & Privacy (Priva) & 4 & 0.847 \\ \hline
**2** & Governance framework (GovtF) & 8 & 0.807 \\ \hline
**3** & Performance expectancy (PerfEx) & 4 & 0.932 \\ \hline
**4** & Information security (InfoSec) & 4 & 0.950 \\ \hline
**5** & Perceived risk (PerRisk) & 4 & 0.844 \\ \hline
**6** & Government intention (GovtInt) & 6 & 0.815 \\ \hline \end{tabular}
\end{table} 
Table 1: Coefficient of reliability. if a component has four or more variables with a factor loading above 0.60, then the component pattern may be interpreted irrespective of the sample size; in contrast, a component of about ten or more variables with a low loading of 0.40 is reliable with a low sample size. Therefore, to retain the individual items in the measurement scales in line with [PERSON] [107], such component items must have a minimum factor loading of above 0.40 on the relevant factors [121; 122; 123]. * Kaiser-Meyer-Olkin (KMO) and Bartlett's Test The two statistical measures, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and [PERSON]'s Test of Sphericity, give the statistical significance tests of the correlations to indicate the reliability between the pairs of the variables and sampling adequacy [107; 114]. [PERSON] and [PERSON] [114] identify [PERSON]'s measure of sampling adequacy as the ratio of the sum of the squared correlations to the sum of the squared correlations plus the sum of squared partial correlations.